SGF 2017 Papers - Index

460 papers. Source: SAS Global Forum conference proceedings.

#	Title ×	Authors ×	Abstract ×	Keywords ×	Extras	Pages
1	%SURVEYGENMOD Macro: An Alternative to Deal with Complex Survey Design for the GENMOD Procedure	Alan Ricardo da Silva	The purpose of this paper is to show a SAS macro named %surveygenmod developed in the `SAS/IML` procedure as an upgrade of macro %surveyglm developed by Silva and Silva (2014) to deal with complex survey design in generalized linear models (GLM). The new capabilities are the inclusion of negative binomial distribution, zero-inflated Poisson (ZIP) model, zero-inflated negative binomial (ZINB) model, and the possibility to get estimates for domains. The R function svyglm (Lumley, 2004) and Stata software were used as background, and the results showed that estimates generated by the %surveygenmod macro are close to the R function and Stata software	proc calis, binomial distribution, estimate, macro loop, proc genmod, poisson distribution, proc step, macro call, likelihood function, sample, response variable, macro conditional, proc print, negative binomial distribution, zero-inflated negative binomial, maximum likelihood estimation, zero-inflated poisson, regression analysis, proc surveyreg, significance level, odds ratio, sample weight, poisson regression	source	28
2	A Big-Data Challenge: Visualizing Social Media Trends about Cancer Using SAS Text Miner	Scott Koval, Yijie Li, Mia Lyst	Analyzing big data and visualizing trends in social media is a challenge that many companies face as large sources of publically available data become accessible. While the sheer size of usable data can be staggering, knowing how to find trends in unstructured textual data is just as important of an issue. At a Big Data conference, data scientists from several companies were invited to participate in tackling this challenge by identifying trends in cancer using unstructured data from Twitter users and presenting their results. This paper explains how our approach using SAS analytical methods was superior to other Big Data approaches in investigating these trends	big data, data mining, raw data, proc freq, sas enterprise guide	source	8
3	A Browser-Based Tool for Automating SAS Batch Program Generation: How to Get Individual Logs and Outputs	Mohammad Reza Rezai, Mahmoud Azimaee, Jiming Fang, Jason Chai-Onn	Migration to a SAS Grid computing environment provides many advantages. However, such migration may not be free from challenges especially considering users' pre-migration routines and programming practices. While SAS provides good graphical user interface solutions (e.g. SAS Enterprise Guide) to develop and submit the SAS code to SAS Grid, some situations may need command-line batch submission of a group of related SAS programs in a particular sequence. Saving individual log and output files for each program may also be a favorite routine in many organizations. SAS has provided SAS Grid Manager Client Utility, and SASGSUB commands to enable command-line submission of SAS programs to the grid. However, submitting a sequence of SAS programs in a conventional batch program style and getting individual logs and outputs for them needs a customized approach. This paper presents such an approach. In addition, an HTML/JavaScript tool developed in- house is introduced	proc printto, proc step, sas enterprise guide, sas grid	source	8
4	A Critique of Implementing the Study Data Tabulation Model (SDTM) for Drugs and Medical Devices	Carey G. Smoak	The Clinical Data Interchange Standards Consortium (CDISC) encompasses a variety of standards for medical research. Amongst the several standards developed by the CDISC organization are standards for data collection (Clinical Data Acquisition Standard Harmonization - CDASH), data submission (Study Data Tabulation Model - SDTM) and data analysis (Analysis Data Model - ADaM). Drug development was the original impetus for developing these standards. Therapeutic Area User Guides (TAUGs) have been a recent focus to provide advice, examples and explanations for collecting and submitting data for a specific disease. Non-subjects even have a way to collect data using the Associated Persons Implementation Guide (SDTMIG-AP). SDTM domains for medical device were published 2012. Interestingly, the use of Device domains in the TAUGs occurs in seventeen out of twenty-four of TAUGs providing examples of the use of the various Device domains	clinical trial, clinical research, vital signs, adverse events, cdisc	source	16
5	A Custom Method to Auto-load SAS LASR Tables and Longitudinally Report on ETL, DQ, and LASR Timings	Jessica Fraley	Automatic loading, tracking, and visualization of data readiness in SAS Visual Analytics is easy when you combine SAS Data Integration Studio with the DATASET and LASR procedures. This paper illustrates the simple method that the University of North Carolina at Chapel Hill (Enterprise Reporting and Departmental Systems) uses to automatically load tables into the SAS LASR Analytic Servers, and then store reportable data about the HDFS tables created, the LASR tables loaded, and the ETL job execution times. This methodology gives the department the ability to longitudinally visualize system loading performance and identify changes in system behavior, as well as providing a means of measuring how well we are serving our customers over time	standard deviation, batch job, record count	source	10
6	A Data Mining Approach to Predict Students at Risk	Youyou Zheng, Thanuja Sakruti	Student success is one of the most important topics for institutions. In this paper, the institutional researchers discussed the data mining process that could predict student at risk for a major STEM course at a top public university. SAS Visual Analytics and SAS Enterprise Miner were used for data visualization and predictive modeling. Several different modeling methods were compared to identify the optimal model	neural network, data mining, proc hpsplit, regression analysis, data cleaning	source	13
7	A General SAS Macro to Implement Optimal N:1 Propensity Score Matching Within a Maximum Radius	Kathy Hardis Fraeman	A propensity score is the probability that an individual will be assigned to a condition or group, given a set of baseline covariates when the assignment is made. For example, the type of drug treatment given to a patient in a real-world setting might be non-randomly based on the patient's age, gender, geographic location, and socioeconomic status when the drug is prescribed. Propensity scores are used in many different types of observational studies to reduce selection bias. Subjects assigned to different groups are matched based on these propensity score probabilities, rather than matched based on the values of individual covariates. Although the underlying statistical theory behind the use of propensity scores is complex, implementing propensity score matching with SAS is relatively straightforward. An output data set of each subject's propensity score can be generated with SAS using `PROC LOGISTIC`	propensity score, propensity score matching, proc step, proc logistic, logistic distribution, dataset, macro loop, macro definition	source	13
8	A Guru's Guide: Producing Section 508-Compliant Custom Reports with the Output Delivery System	Daniel O'Connor	Creating sophisticated, visually stunning reports is imperative in today's business environment, but is your fancy report really accessible to all? Let's explore some simple enhancements that were made in the fourth maintenance release of SAS 9.4 to Output Delivery System (`ODS`) layout and the Report Writing Interface that will truly empower you to accommodate people who use assistive technology. `ODS` now provides the tools for you to meet Section 508 compliance and to create an engaging experience for all who consume your reports	dataset, output delivery system	source	10
9	A Hadoop Journey along the SAS Road	Sumit Sukhwani, Krutharth Peravalli, Amit Gautam	As the open-source community has been taking the technology world by storm, especially in the big data space, large corporations such as SAS, IBM, and Oracle have been working to embrace this new, quickly evolving ecosystem to continue to foster innovation and to remain competitive. For example, SAS, IBM, and others have aligned with the Open Data Platform initiative and are continuing to build out Apache Hadoop and Apache Spark solutions. And, Oracle has partnered with Cloudera to create the Big Data Appliance. This movement challenges companies that are consuming these products to select the right products and support partners. The hybrid approach-using a combination of tools available-seems to be the methodology chosen by most successful companies. West Corporation, an Omaha-based provider of technology-enabled communication solutions, is no exception	data mining, customer experience, proc sql, spark, logistic regression, data validation rules, predictor variable, sas enterprise guide, data science, feature selection, logistic distribution, proc step, regression analysis	source	8
10	A Long-Time SAS Programmer Learns New Tricks	Lisa Ann Horwitz	When a large and important project with a strict deadline hits your desk, it's easy to revert to those tried- and-true SAS programming techniques that have been successful for you in the past. In fact, trying to learn new techniques at such a time can prove to be distracting and a waste of time. However, the lull after a project's completion is the perfect time to reassess your approach. Once you've met your deadline, find out whether any new features-or features that are new to you-have been added to the SAS arsenal that could be of great use the next time around. Such a post-project reassessment has provided me with the opportunity to learn about several features that will prove to be extremely valuable as I rework a key project for Round 2. These features include	sas enterprise guide, macro variable, stored macro, libname statement	source	12
11	A Macro that Can Search and Replace Strings in Your SAS Programs	Ting Sa	In this paper, a SAS macro is introduced that can search and replace any string in a SAS program. To use the macro, the user needs only to pass the search string to a folder. If the user wants to use the replacement function, the user also needs to pass the replacement string. The macro checks all of the SAS programs in the folder and subfolders to find out which files contain the search string. The macro generates new SAS files for replacements so that the old files are not affected. An HTML report is generated by the macro to include the original file locations, the line numbers of the SAS code that contain the search string, and the SAS code with search strings highlighted in yellow. If you use the replacement function, the HTML report also includes the location information for the new SAS files. The location information in the HTML report is created with hyperlinks so that the user can directly open the files from the report	macro definition, sample, create table, macro variable, proc sql	source pdf	7
12	A Macro that Creates U.S State and County Keyhole Markup Language Files for Google Map Use	Ting Sa	This paper introduces a macro that can generate the keyhole markup language (KML) files for U.S. states and counties. The generated KML files can be used directly by Google Maps to add customized state and county layers with user-defined colors and transparencies. When someone clicks on the state and county layers in Google Maps, customized information is shown. To use the macro, the user needs to prepare only a simple SAS input data set. The paper includes all the SAS codes for the macro and provides examples that show you how to use the macro as well as how to display the KML files in Google Maps	create table, order by, dataset, sample, proc sql	source	10
13	A Man with One Watch Always Knows What Time It Is	Tim Fairchild	"A man with one watch always knows what time it is. A man with two watches is never sure." Contrary to this adage, load forecasters at electric utilities would gladly wear an armful of watches. With only one model to choose from, it is certain that the forecasts will be wrong. But with multiple models, forecasters can have confidence about periods when the forecasts agree and can focus their attention on periods where the predictions diverge. Having a second opinion is preferred, and that's one of the six classic rules for forecasters as per Dr. Tao Hong of the University of North Carolina at Charlotte. Dr. Hong is the premiere thought leader and practitioner in the field of energy forecasting. This presentation discusses Dr. Hong's six rules, how they relate to the increasingly complex problem of forecasting electricity consumption, and the role that predictive analytics plays	mape, mae, neural network	source	9
14	A Moment-Matching Approach for Generating Synthetic Data in SAS	Brittany Bogle, Jared Erickson	Disseminating data to potential collaborators can be essential in the development of models, algorithms, and innovative research opportunities. However, it is often time consuming to get approval to access sensitive data such as health data. An alternative to sharing the real data is to use synthetic data, which has similar properties to the original data but does not disclose sensitive information. The collaborators can use the synthetic data to make preliminary models or work out bugs in their code while waiting to get approval to access the original data. A data owner can also use the synthetic data to crowdsource solutions from the public through competitions like Kaggle and then test those solutions on the original data. This paper implements a method that generates fully synthetic data in a way that matches the statistical moments of the true data up to a specified moment order as a SAS macro	proc step, odds ratio, proc corr, standard deviation, proc calis, covariance, nominal variable, linear programming, roc curve, training set, logistic distribution, macro definition, confidence interval, missing value	source	17
15	A New SAS Mobile BI and Microsoft Windows 10 Application	Murali Nori	Microsoft Windows 10 is a new operating system that is increasingly being adopted by enterprises around the world. SAS has planned to expand SAS Mobile BI, which is currently available on Apple iOS and Google Android, to the Microsoft Windows 10 platform. With this new application, customers can download business reports from SAS Visual Analytics to their desktop, laptop, or Microsoft Surface device, and use these reports both online and offline in their day-to-day business life. With Windows 10, users have the option of pinning a report to the desktop for quick access. This paper demonstrates this new SAS mobile application. We also demonstrate the cool new functionality on iOS and Android platforms, and compare them with the Windows 10 application		source	8
16	A Novel Approach to Calculating Medicare Hospital 30-Day Readmissions for the SAS Novice	Karen E. Wallace	Hospital Medicare readmission rate has become a key indicator for measuring the quality of healthcare in the US, currently adopted by major healthcare stakeholders including the Centers for Medicare and Medicaid Services (CMS), the Agency for Healthcare Research and Quality (AHRQ), and the National Committee for Quality Assurance (NCQA) (Fan and Sarfarazi, 2014). Although many papers have been written about how to calculate readmissions, this paper includes updated coding to include ICD-10 (International Classification of Diseases) as well as offering a novel and comprehensive approach using the options of the SAS `DATA STEP` as well as `PROC SQL` for: 1) de-identifying patient data, 2) calculating sequential admissions, and 3) subsetting criteria required to report for CMS 30-day readmissions	proc sql, proc step, macro variable, excel, proc means, sample, data cleaning	source pdf	11
17	A Practical Guide to Getting Started with Propensity Scores	Thomas Gant	This paper gives tools to begin using propensity scoring in SAS to answer research questions involving observational data. It is for both those attendees who have never used propensity scores and those who have a basic understanding of propensity scores, but are unsure how to begin using them in SAS. It provides a brief introduction to the concept of propensity scores, and then turns its attention to giving you tips and resources that will help you get started. The paper walks you through how the code in the book "Analysis of Observational Health Care Data Using SAS", which is published by the SAS Institute, is used to analyze how a health care treatment impacted an associated health care outcome. It details how propensity scores are created and how propensity score matching is used to balance covariates between treated and untreated observations	propensity score, propensity score matching, logistic distribution, logistic regression, negative binomial regression, binomial distribution, regression analysis, response variable, predictor variable	source	10
18	A Practical Guide to Healthcare Data: Tips, Traps, and Techniques	Gregory S. Barnes Nelson	Healthcare is weird. Healthcare data is even more so. The digitization of healthcare data that describes the patient experience is a modern phenomenon with most healthcare organizations still in their infancy. While the business of healthcare is already a century old, most organizations have focused their efforts on the financial aspects of healthcare and not on stakeholder experience or clinical outcomes. Think of the workflow that you may have experienced such as scheduling an appointment through doctor visits, obtaining lab tests, or prescriptions for interventions such as surgery or physical therapy. As you traverse the modern healthcare system, we are left with a digital footprint of administrative, process, quality, epidemiological, financial, clinical, and outcome measures that range in size, cleanliness and usefulness	clinical endpoint, unstructured data	source	20
19	A SAS Macro for Covariate Specification in Linear, Logistic, and Survival Regression	Sai Liu	Specifying the functional form of a covariate is a fundamental part of developing a regression model. The choice to include a variable as continuous, categorical, or as a spline can be determined by model fit. This paper offers an efficient and user-friendly SAS macro (%SPECI) to help analysts determine how best to specify the appropriate functional form of a covariate in a linear, logistic, and survival analysis models. For each model, our macro provides a graphical and statistical single page comparison report of the covariate as a continuous, categorical, and restricted cubic spline variable so that users can easily compare and contrast results. The report includes the residual plot and distribution of the covariate. You can also include other covariates in the model for multivariable adjustment. The output displays the likelihood ratio statistic, the Akaike Information Criterion (AIC), as well as other model-specific statistics	regression analysis, macro variable, likelihood function, logistic regression, logistic distribution, proc step, nominal variable, interval variable, proc reg, degrees of freedom, estimate, sample, adjusted r-squared, proc sgplot, survival analysis, response variable, work library, normalization, proc logistic, proc phreg, proc report, outlier, binary variable	source zip	19
20	A SAS Macro to Generate Kaplan-Meier Plot and Optional Estimates	Chia-Ling (Ally) Wu	This paper aims to provide a SAS macro which hopes to help users to have a Kaplan-Meier plot with optional estimates, which includes the number of subjects at risk, the number of events and total subjects, median survival rate and Cox proportional hazard ratio. This macro supports customized header, labels for time interval (X-axis) and survival rate (Y-axis), and tick marks for time interval (X-axis) and subject at risk table, as well as adding filter to the data. The output destination can be PDF, RTF or html files	hazard ratio, kaplan-meier plot, proc phreg, proc step, median survival, macro variable	source pdf	11
21	A SAS Program to Identify Duplicates in Clinical Data	Xiaoli Lu	Presentation		source	4
22	A Step-by-Step Solution to Create a Customized Graph for Grouped Data Using Graph Template Language	Elva Chen	Meaningful and interpretable visualization is critical to turn data into knowledge. Almost every software systems provide settings to create default graphs. Although creating default graphs provided by these software is straightforward, creating customized graphs for specialized analysis and specific interpretation are challenging. Furthermore, in most analysis, data are collected and summarized by groups. To create customized graph for group data for specific layouts and desired appearances are even more complicated. This paper provides a step by step, start-from-scratch approach to create a customized graph for group data using SAS Graph Template Language (GTL)	proc step, proc template, series plot, confidence interval, scatter plot	source	11
23	A Step-by-Step Solution to Create a Customized Graph for Grouped Data Using Graph Template Language	Elva Chen	Meaningful and interpretable visualization is critical to turn data into knowledge. Almost every software systems provide settings to create default graphs. Although creating default graphs provided by these software is straightforward, creating customized graphs for specialized analysis and specific interpretation are challenging. Furthermore, in most analysis, data are collected and summarized by groups. To create customized graph for group data for specific layouts and desired appearances are even more complicated. This paper provides a step by step, start-from-scratch approach to create a customized graph for group data using SAS Graph Template Language (GTL)	confidence interval, proc step, proc template, series plot, scatter plot	source	16
24	A Survey of Some of the Most Useful SAS Functions	Ronald P. Cody	SAS Functions provide amazing power to your `DATA STEP` programming. Some of these functions are essential- some of them save you writing volumes of unnecessary code. This paper covers some of the most useful SAS functions. Some of these functions may be new to you and they will change the way you program and approach common programming tasks	character variable, missing value, length statement, substr function, input statement, lag function, compress function, sas function, put statement, sysdate9, yrdif, character functions, numeric functions, call missing, call sortn, missing function, if-then-else	source	16
25	Accelerate Your Data Prep with SAS Code Accelerator	Paul Segal		proc step, proc sql, proc transpose, by group processing	source	5
26	Accelerate your SAS Programs with GPUs	Henry Bequet, Huina Chen	Graphical Processing Units (GPUs) are quickly becoming a commodity on many desktops and servers. For less than $1 per core, a server can go from a few cores to thousands of cores. The major goal of the research project that we describe in this paper is to investigate how we can easily leverage the computing power of GPUs in SAS programs. To complete this project, we relied heavily on the Infrastructure for Risk Management solution (IRM), but the techniques that we describe in this article are applicable to plain old SAS MVA code. Some programming, either in Base SAS or C, will be required to replicate the results that we describe in this paper. Readers with an advanced knowledge of Base SAS (9.4), the SAS Macro Language, and `PROC FCMP` will understand how to leverage GPU computing to achieve unsurpassed performance improvements in their solutions. Readers with an advanced knowledge of C will understand how to integrate their GPU-aware code to SAS programs	dataset, proc fcmp, sas programmer, proc step, binomial distribution	source	9
27	Accessibility and SAS University Edition: Tips for Students and Professors	Ed Summers, Amy Peters	Accessibility has become a hot topic on campus due to a flurry of recent investigations of discrimination against students with disabilities by the U.S. Department of Justice and the U.S. Department of Education. This paper provides an update on the latest improvements in SAS University Edition that are specifically targeted to enable students with disabilities to `EXCEL` in the classroom and beyond. This paper covers the entire SAS University Edition user experience including installation, documentation, training, support, using SAS Studio, and the new accessibility features in the fourth maintenance release of	sas studio, sas output, data visualization, regression analysis, sample	source	7
28	Accessibility and SAS Visual Analytics Viewers: Which Report Viewer Is Best for Your Users' Needs?	Jesse Sookne, Kristin Barker, Joe Sumpter, Lavanya Mandavilli	Many organizations that use SAS Visual Analytics must conform with accessibility requirements such as Section 508, the Americans with Disabilities Act, and the Accessibility for Ontarians with Disabilities Act. SAS Visual Analytics provides a number of different ways to view reports, including the SAS Report Viewer and SAS Mobile BI native apps for Apple iOS and Google Android. Each of these options has its own strengths and weaknesses when it comes to accessibility-a one-size-fits-all approach is unlikely to work well for the people in your audience who have disabilities. This paper provides a comprehensive assessment of the latest versions of all SAS Visual Analytics report viewers, using the Web Content Accessibility Guidelines (WCAG) version 2.0 as a benchmark to evaluate accessibi lity. You can use this paper to direct the end users of your reports to the viewer that best meets their individual needs	bar chart, line chart	source zip	18
29	Accessing DBMS with the GROOVY Procedure and a JDBC Connection	Lilyanne Zhang	`SAS/ACCESS` software grants access to data in third-party database management systems (DBMS), but how do you access data in DBMS not supported by `SAS/ACCESS` products? The introduction of the `GROOVY` procedure in SAS 9.3 lets you retrieve this formerly inaccessible data through a JDBC connection. Groovy is an object-oriented, dynamic programming language executed on the Java Virtual Machine (JVM). Using Microsoft Azure HDInsight as an example, this paper demonstrates how to access and read data into a SAS data set using `PROC GROOVY` and a JDBC connection	proc groovy, proc step, proc sql, macro variable, macro definition	source	5
30	Adding a Workflow to Your Analytics with SAS Visual Investigator	Gordon Robinson, Ryan Schmiedl	Monitoring server events to proactively identify future outages. Looking at financial transactions to check for money laundering. Analyzing insurance claims to detect fraud. These are all examples of the many applications that can use the power of SAS Analytics to identify threats to a business. Using SAS Visual Investigator, users can now add a workflow to control how these threats are managed. Using the administrative tools provided, users can visually design the workflow that the threat would be routed through. In this way, the administrator can control the tasks within the workflow, as well as which users or groups those tasks are assigned to. This paper walks through an example of using the administrative tools of SAS Visual Investigator to create a ticketing system in response to threats to a business. It shows how SAS Visual Investigator can easily be adapted to meet the changing nature of the threats the business faces	internal audit, outlier	source	10
31	Advanced Hierarchical Modeling with the MCMC Procedure	Fang K. Chen, Maura E. Stokes	Hierarchical models, also known as random-effects models, are widely used for data that consist of collections of units and are hierarchically structured. Bayesian methods offer flexibility in modeling assumptions that enable you to develop models that capture the complex nature of real-world data. These flexible modeling techniques include choice of likelihood functions or prior distributions, regression structure, multiple levels of observational units, and so on. This paper shows how you can fit these complex, multilevel hierarchical models by using the `MCMC` procedure in `SAS/STAT` software. `PROC MCMC` easily handles models that go beyond the single-level random-effects model, which typically assumes the normal distribution for the random effects and estimates regression coefficients	proc mixed, proc step, likelihood function, response variable, prior probability, regression analysis, sample, proc phreg, proc glimmix	source	21
32	Advanced Location Analytics Using Demographic Data from Esri and SAS Visual Analytics	Murali Nori, Himesh Patel	Location information plays an important role in business data. Everything that happens in a business happens somewhere, whether it's the sales of products in different regions of a country or crimes that happened in a city. Business analysts typically use the historic data that they have gathered for years for analysis. One of the most important pieces of data that can help answer more questions qualitatively, is the demographic data combined with the business data. An analyst can use demographic details like gender, age groups, family income, race, and other pieces of information, for better insight. This paper focus on importing custom shape files and how a business analyst can bring the demographic and lifestyle data from Esri into SAS Visual Analytics and join the data with business data. The integration o f SAS Visual Analytics with Esri facilitate this to happen. We also cover the new geo based capabilities in the 8.1 release of SAS Visual Analytics	business intelligence, big data	source	11
33	Advanced Programming Techniques with PROC SQL	Kirk Paul Lafler	The `SQL` Procedure contains a number of powerful and elegant language features for SQL users. This hands-on workshop (HOW) emphasizes highly valuable and widely usable advanced programming techniques that will help users of Base-SAS harness the power of the `SQL` procedure. Topics include using `PROC SQL` to identify FIRST.row, LAST.row and Between.rows in BY-group processing; constructing and searching the contents of a value-list macro variable for a specific value; data validation operations using various integrity constraints; data summary operations to process down rows and across columns; and using the MSGLEVEL= system option and _METHOD SQL option to capture vital processing and the algorithm selected and used by the optimizer when processing a query	proc sql, proc step, dictionary tables, where statement, macro variable, select, create table, inner join, sas log, order by, sas library, group by, macro facility, view, macro call, right join, options statement	source zip	20
34	Advanced, Dynamic, and Effective Dashboarding with SAS Visual Analytics	Abhilasha Tiwari	SAS Visual Analytics provides a robust platform to perform business intelligence through a high-end and advanced dashboarding style. In today's technology era, dashboards not only help in gaining insight into an organization's operations, but they also are a key performance indicator. In this paper, I discuss five important and frequently used objects in SAS Visual Analytics. These objects are used to get the most out of dashboards in an effective and efficient way. This paper covers the use of dates (as a format) in the date slider and gauges, cascading filters, custom graphs, linking reports within sections of the same report or with other reports, and associating buttons with graphs for dynamic functionality	business intelligence, key performance indicator	source	11
35	Am I Getting the Most Value out of My SAS Installation Dollars?	Al Cordoba	Would you agree that the value of SAS for your organization co mes from transforming data into actionable information, using w ell-prepared human resources? This paper presents seven areas w here this potential SAS value can be lost by inefficient data access, limited reporting and visualization, poor data cleansing, obsolete predictive analytics, incomplete SAS solutions, li mited hardw are use, and lack of governance. This paper also suggests w hat to do to overcome these issues	sas grid, predictive analytics, data mining, sas model manager, sas programming, data cleaning, sas enterprise guide	source	7
36	An Analysis of the Coding Movement: How Code.org and Educators Are Bringing Coding to Every Student	Lauren Cook, Talazia Moore	Presentation		source	4
37	An Analysis of the Repetitiveness of Lyrics in Predicting a Song's Popularity	Drew Doyle	Presentation		source	6
38	An Easy-to-Use SAS Macro for a Descriptive Statistics Table	Yuanchao Zheng, Jin Long, Maria E. Montez-Rath	Are you tired of copying output from the `PROC FREQ` or `PROC MEANS` procedures and pasting it into your tables? Do you need to produce summary tables repeatedly? Are you spending a lot of your time generating the same summary tables for different subpopulations? This paper introduces an easy-to-use macro to generate a descriptive statistics table. The table reports counts and percentages for categorical variables as well as means, standard deviations, medians, and quantiles for continuous variables. For variables with missing values, the table also includes the count and percentage missing. Customization options allow for the analysis of stratified data, the specification of variables' output order, and user- defined formats. Additionally, this macro incorporates the SAS Output Delivery System (`ODS`) to automatically output a Rich Text Format (RTF) file, which can be further edited by a word processor for the purpose of publication	proc means, proc step, macro variable, interval variable, proc freq, macro loop, macro definition, %if-%then-%else	source pdf	16
39	An Efficient Way to Deploy and Run Text Analytics Models in Hadoop	Seung Lee, Xu Yang, Saratendu Sethi	Significant growth of the Internet has created an enormous volume of unstructured text data. In recent years, the amount of this type of data that is available for analysis has exploded. While the amount of textual data is increasing rapidly, an ability to obtain key pieces of information from such data in a fast, flexible, and efficient way is still posing challenges. This paper introduces SAS Contextual Analysis In- Database Scoring for Hadoop, which integrates SAS Contextual Analysis with the SAS Embedded Process. SAS Contextual Analysis enables users to customize their text analytics models in order to realize the value of their text-based data. The SAS Embedded Process enables users to take advantage of SAS In-Database Code Accelerator for Hadoop to run scoring models. By using these key SAS technologies, the overall experience of analyzing unstructured text data can be greatly improved	libname statement, score code, macro variable, proc step	source	11
40	An Information Technology Perspective of SAS at The University of Memphis	Robert Jackson	THE UNIVERSITY OF MEMPHIS HAS BEEN A SAS CUSTOMER SINCE THE MAINFRAME COMPUTING ERA. OUR DEPLOYMENTS HAVE INCLUDED VARIOUS SAS PRODUCTS INVOLVING WEB-BASED APPLICATIONS, CLIENT/SERVER IMPLEMENTATIONS, DESKTOP INSTALLATIONS, AND VIRTUALIZED SERVICES. THIS PAPER USES AN INFORMATION TECHNOLOGY (IT) PERSPECTIVE TO DISCUSS HOW THE UNIVERSITY HAS LEVERAGED SAS, AS WELL AS THE LATEST BENEFITS AND CHALLENGES FOR OUR MOST RECENT DEPLOYMENT INVOLVING SAS	sample, business intelligence, data mining	source	8
41	An Insider's Guide to Fine-Tuning Your CREATE TABLE Statements Using SAS Options	Jeffrey Bailey	The SAS code looks perfect. You submit it and to your amazement, there is a problem with the CREAT E TABLE statement. You need to change the table definition, ever so slightly, but how? Explicit pass- through? That's not an option. Fortunately, there are a handful of SAS options that can save the day. This paper will cover everything you need to know in order to adjust your SAS-generated create table statements using SAS options. This paper covers the following SAS options	create table, proc sql, proc step, dataset, options statement, implicit sql pass-through, select, sas log, hive	source	16
42	An Introduction to SAS Visual Analytics 8.1	Jeff Diamond	The first release of SAS Visual Analytics version 8.1 on SAS Viya has something exciting for everyo ne. The latest version is a clean, modern HTML5 interface. SAS Visual Analytics Designer, SAS Visual Analytics Explorer, and SAS Visual Statistics are merged into a single web appl ication. Whether you are designing reports, exploring data or running interactive, predictive models, it is all integrated into one seamless experience. The application delivers on the same basic promise: get pertinent answers from any-size data. This paper will walk you through key features that you have come count on from auto charting to display rules and more. It will acclimate you to the new interface and highlight a few exciting new features like web content and donut pie charts. Finally, this paper will touch upon the ability to promote your existing reports to the new environment	sas viya, machine learning, sas 9	source	14
43	An Introduction to the Improved SAS Data Integration Studio Batch Deployment Utility on UNIX	Jeff Dyson	Interactively redeploying SAS Data Integration Studio jobs can be a slow and tedious process. The updated batch deployment utility gives the ETL Tech Lead a more efficient and repeatable method for administering batch jobs. This improved tool became available in SAS Data Integration Studio 4.901	sas management console, batch processing	source	11
44	An Investigation into Big Data Analytics Applied to Insurance	Rebecca Peters, Penny Holborn	Data is generated every second. The term Big Data refers to the volume, variety, and velocity of data that is being produced. Now woven into every sector, its size and complexity has left organizations faced with difficulties in being able to create, manipulate, and manage Big Data. This research identifies and reviews a range of Big Data techniques available through SAS, highlighting the fundamental opportunities that SAS provides for overcoming a variety of business challenges. Insurance is a data-dependent industry hence this research focuses on understanding what SAS can offer to insurance companies and how it could interact with existing customer databases and online, user-generated content. A key data source has been identified for this purpose. The research demonstrates how models can be built based on existing relationships found in past data and then used to identify prospective customers	logistic regression, logistic distribution, insurer, regression analysis, proc logistic, correlation, odds ratio, likelihood function, sas enterprise guide, proc princomp, factor loading, scree plot, chi-square test, null hypothesis, maximum likelihood estimation, roc curve, response variable	source	18
45	An Oasis of Serenity in a Sea of Chaos: Automating the Management of Your UNIX/Linux Multi-tiered SAS Services	Clifford Meyers	UNIX and Linux SAS administrators, have you ever been greeted by one of these statements as you walk into the office before you have gotten your first cup of coffee? "Power outage!" "SAS servers are down." * "I cannot access my reports." Have you frantically tried to restart the SAS servers to avoid loss of productivity and missed one of the steps in the process, causing further delays while other work continues to pile up? If you have had this experience, you understand the benefit to be gained from a utility that automates the management of these multi-tiered deployments. Until recently, there was no method for automatically starting and stopping multi-tiered services in an orchestrated fashion. Instead, you had to use time-consuming manual procedures to manage SAS services. These procedures were also prone to human error, which could result in corrupted services and additional time loss debugging and resolving issues injected by this process	sas administrator, note	source	23
46	An Overview of SAS Visual Data Mining and Machine Learning on SAS Viya	Jonathan Wexler, Susan Haller	Machine learning is in high demand. Whether you are a citizen data scientist who wants to work interactively or you are a hands-on data scientist who wants to code, you have access to the latest analytic techniques with SAS Visual Data Mining and Machine Learning on SAS Viya. This offering surfaces in-memory machine-learning techniques such as gradient boosting, factorization machines, neural networks, and much more through its interactive visual interface, SAS Studio tasks, procedures, and a Python client. Learn about this multi-faceted new product and see it in action	sas studio, sas viya, machine learning, gradient boosting, proc step, logistic regression, proc hpsplit, data cleaning, regression analysis, score code, logistic distribution, support vector machine, libname statement, neural network, response variable, feature importance	source	23
47	Analysis of the Disparity of the "Haves" and "Have-Nots" in the United States	Gaurang Margaj, Tejaswi Jha, Tejashree Pande	Presentation		source	4
48	Analytics Approach to Predict Total Recall in the Automobile Industry	Prathap Maniyur, Mansi Bhat, Prashanth Nayak	Presentation		source	4
49	Analytics of Healthcare Things (AoHT) IS THE Next Generation of Real World Data	Joy King	As you know Real World Data (RWD) provides highly valuable and practical insights. But as valuable as RWD is, it still has limitations. It is encounter-based and we are largely blind to what happens between encounters in the Healthcare System. The encounters generally occur in a clinical setting which may not reflect actual patient experience. Many of the encounters are subjective interviews, observations, or self-reports rather than objective data. Information flow can be slow (even real-time is not fast enough in healthcare anymore). And some data that could be transformative cannot be captured currently	life sciences, iot	source	2
50	Analytics Using SAS Customer Intelligence: Multivariate Testing Processes for Digital Marketing Campaigns	Thomas Lehman	Multi variate website testing helps marketers understand which combinations of factors, working together, are most important for driving conversion. A real-world objection to running multi variate tests is the volume of traffic needed to draw reliable conclusions. This paper introduces a novel and practical method for doing multi variate web site testing. It is based on reducing a full factorial test to a smaller number of design points and then fitting a predictive model to estimate outcomes of untested combinations from measured results. The marketer can then choose to follow up the multi variate test with a subsequent A/B test once a small number of highly performing combinations have been identified	sample size, sample, proc step, a/b testing, model training	source	9
51	Analyzing Correlated Data in SAS	Niloofar Ramezani	Correlated data are extensively used across disciplines when modeling data with any type of correlation that may exist among observations due to clustering or repeated measurements. When modeling clustered data, Hierarchical linear modeling is a popular multilevel modeling technique which is widely used in different fields such as education and health studies (Gibson & Olejnik, 2003). A typical example of multilevel data involves students nested within classrooms that behave similarly due to shared situational factors. Ignoring their correlation may result in underestimated standard errors and inflated type-I error (Raudenbush & Bryk, 2002). When modeling longitudinal data, many studies have been conducted on continuous outcomes; however, fewer studies on discrete responses over time have been completed. These studies require models within conditional, transitional and marginal models (Fitzmaurice, Davidian, Verbeke, & Molenberghs, 2009)	proc step, correlation, proc mixed, fixed effects, longitudinal data, response variable, predictor variable, regression analysis, normal distribution, proc genmod, conditional logistic regression, proc glimmix, proc model, proc sgplot, covariance, repeated measures, spaghetti plot, logistic distribution, logistic regression, likelihood function, random slope, sample, proc univariate, maximum likelihood estimation, random intercept, proc phreg	source	13
52	Analyzing Residuals in a PROC SURVEYLOGISTIC Model	Bogdan Gadidov	Data from an extensive survey conducted by the National Center for Education Statistics (NCES) is used for predicting qualified secondary school teachers across public schools in the U.S. The sample data includes socioeconomic data at the county level, which are used as predictors for hiring a qualified teacher. The resultant model is used to score other regions and is presented on a heat map of the U.S. SAS' survey family of procedures such as SURVEYFREQ and SURVEYLOGISTIC are used in the analyses since the data involves replicate weights to account for the complex survey design. In looking at residuals from a logistic regression, since all the outcomes are binary, the residuals do not necessarily follow the normal distribution that is so often assumed in residual analysis. Furthermore, in dealing with survey data, the weights of the observations must be accounted for, as these affect the variance of the parameter estimates	logistic distribution, logistic regression, standard deviation, proc step, proc calis, regression analysis, proc logistic, normal distribution, proc genmod, proc surveylogistic, estimate, sample, outlier, residual analysis, proc sql	source	8
53	Analyzing the Effect of Weather on Uber Ridership	Anusha Mamillapalli, Singdha Gutha	Uber has changed the face of taxi ridership, making it more convenient and comfortable for riders. But, there are times when customers are left unsatisfied because of shortage of vehicles which ultimately led to Uber adopting surge pricing. It's a very di fficult task to forecast number of riders at different locations in a city at different points in time. This gets more complicated with changes in weather. In this paper we attempt to estimate the number of trips per borough on a daily basis in New York City. We add an exogenous factor, weather to this analysis to see how it impacts the changes in number of trips. We fetched six month worth data (approximately 1 million records) of Uber rides in New York City ranging from January 2015 to June 2015 from GitHub. We also gathered weather data (from Weather Underground) of New York City Borough wise for the same period of six months from Jan 2015 to June 2015	time series analysis, response variable, sample	source pdf	10
54	Analyzing the Effectiveness of COPD Drugs Through Statistical Tests and Sentiment Analysis	Indra Kiran Chowdavarapu, Dursun Delen, Vivek Manikandan Damodaran	Chronic Obstructive Pulmonary disease(COPD) is the third leading causing of death in the United States. An estimated 24 million people suffer from COPD, and the medical costs associated with it stands at a whopping $36 billion. Besides the emotional and physical impact, a patient with COPD has to undergo severe economic burden to pay for the medication. At this juncture, identifying the best medicine to treat COPD enhances the living conditions of patients. This paper deals with analyzing the effectiveness of three popular drugs prescribed for the COPD patients through statistical tests and Sentiment analysis. T he statistical analysis determines the effectiveness of these drugs on the patients in terms of mortality rates and readmission within 30 days of discharge. The impact of comorbidities, such as cardio vascular diseases, accident history, and smoking, on COPD patients is also examined	chi-square test, odds ratio, post hoc test, anova, nominal variable, data science, response variable	source	23
55	Analyzing the Predictive Power of Political and Social Factors in Determining Country Risk	Vishwanath Kolar Bhaskara, Bhuvaneswari Yallabandi	Presentation		source	4
56	Application of Survival Analysis for Predicting Customer Churn with Recency, Frequency, and Monetary	Bo Zhang, Liwei Wang	Customer churn is an important area of concern that affects not just the growth of your company, but also the profit. Conventional survival analysis can provide a customer's likelihood to churn in the near term, but it does not take into account the lifetime value of the higher-risk churn customers you are trying to retain. Not all customers are equally important to your company. Recency, frequency, and monetary (RFM) analysis can help companies identify customers that are most important and most likely to respond to a retention offer. In this paper, we use the `IML` and PHREG procedures to combine the RFM analysis and survival analysis in order to determine the optimal number of higher-risk and higher-value customers to retain	survival analysis, proc freq, churn, sample, macro function, proc phreg	source	6
57	Applying Text Analytics and Machine Learning to Assess Consumer Financial Complaints	Tom Sabo	The Consumer Financial Protection Bureau (CFPB) collects tens of thousands of complaints against companies each year, many of which result in the companies in question taking action, including making payouts to the individuals who filed the complaints. Given the volume of the complaints, how can an overseeing organization quantitatively assess the data for various trends, including the areas of greatest concern for consumers? In this paper, we apply a repeatable model of text analytics techniques to the publicly available CFPB data. Specifically, we use SAS Contextual Analysis to explore sentiment and machine learning techniques to model the natural language available in each free-form complaint against a disposition code for the complaint, primarily focusing on whether a company paid out money. This process generates a taxonomy in an automated manner	proc hpsplit, dataset, debit card, sas enterprise guide	source	15
58	Association between Sunlight and Specific-Cause Mortality	Wei Xiong	Presentation		source	4
59	Auditing in SAS Visual Analytics	Elena Muriel	Many organizations are using SAS Visual Analytics for their daily reporting. But as more users gain access to the visual tool, it is easy to lose track of what data is being used, what reports are being accessed, and what elements of the system are classified as critical. With SAS Visual Analytics comes a governance exercise that all organizations should provision for, as otherwise it jeopardizes its maintenance and performance. This paper explores the three different auditing areas that can be configured with SAS Visual Analytics and the different metrics that are associated with them. It presents how to configure the auditing, the data sources that are being populated on the background, and how to exploit them to expand your reports beyond the pre-created audit reports. Consideration is also given to the IT and infrastructure side of enabling auditing mechanisms, with data volumes and archiving practices being at the heart of the discussion	dataset, audit dataset, sas management console, proc freq, sas administrator	source	18
60	Auto Telematics: Deviations Drive Success	Kelsey Osterloo, Deovrat Kakde	The use of telematics data within the insurance industry is becoming prevalent as insurers use this data to give discounts, categorize drivers, and provide feedback to improve customer's driving. The data captured through in-vehicle or mobile devices includes acceleration, braking, mileage, and others. Data elements are analyzed to determine 'unsafe' events such as rapid acceleration, hard braking, quick turning, etc. The time between these successive unsafe events is often a function of the mileage driven and time in the telematics program. Our discussion highlights analysis of unsafe events using recurrent event data analysis techniques available in the `RELIABILITY` procedure within the `SAS/QC` software. The `RELIABILITY` procedure is used to determine a mean cumulative function (MCF) of unsafe events as a function of mileage, which describes the average driving behavior. The MCF is compared with each driver's cumulative history function of unsafe events to rank their driving behavior	data science, proc means	source	12
61	Automate Validation of CDISC SDTM with SAS	Sy J. Truong	There are many good validation tools for CDISC SDTM such as Pinnacle 21 Enterprise. However, the power and customizability of SAS provides an effective tool for validating SDTM datasets used in clinical trials FDA submissions. This paper presents three distinct methods of using SAS to validate the transformation from EDC (Electronic Data Capture) data into CDISC SDTM format. This includes: 1. Duplicate Programming - An independent SAS program used to transform EDC data with `PROC`	data cleaning, proc step, excel, dataset, sdtm domain, raw data, proc freq, macro variable, numeric variable, proc compare, proc sql, outlier, cdisc	source	11
62	Automated Hyperparameter Tuning for Effective Machine Learning	Patrick Koch, Brett Wujek, Oleg Golovidov, Steven Gardner	Machine learning predictive modeling algorithms are governed by "hyperparameters" that have no clear defaults agreeable to a wide range of applications. The depth of a decision tree, number of trees in a forest, number of hidden layers and neurons in each layer in a neural network, and degree of regularization to prevent overfitting are a few examples of quantities that must be prescribed for these algorithms. Not only do ideal settings for the hyperparameters dictate the performance of the training process, but more importantly they govern the quality of the resulting predictive models. Recent efforts to move from a manual or random adjustment of these parameters include rough grid search and intelligent numerical optimization strategies. This paper presents an automatic tuning implementation that uses local search optimization for tuning hyperparameters of modeling algorithms in SAS Visual Data Mining and Machine Learning	data cleaning, machine learning	source	23
63	Automatic Singular Spectrum Analysis and Forecasting	Michael J. Leonard, Bruce Elsheimer	The singular spectrum analysis (SSA) method of time series analysis applies nonparametric techniques to decompose time series into principal components. SSA is particularly valuable for long time series, in which patterns (such as trends and cycles) are difficult to visualize and analyze. An important step in SSA is determining the spectral groupings; this step can be automated by analyzing the w-correlations (weighted correlations) of the spectral components. This paper provides an introduction to singular spectrum analysis and demonstrates how to use `SAS/ETS` software to perform it. To illustrate, monthly data on temperatures in the United States for about the last 100 years are analyzed to discover significant patterns	time series analysis, correlation, proc princomp	source	18
64	Automatically Create Diagrams Showing the Structure and Performance of Your SAS Code	Philip A. Mason	The enhanced analysis script allows users to analyse their SAS programs, and display that program visually. It then overlays an extra level of insight on performance measures within that script, allowing the user to identify potential bottlenecks and areas for additional coding efficiency. The following document explains h ow this code works in more detail, and explains how to use it within your own programs	proc step, macro variable, txt, create table, keyword parameter, macro parameter, %macro parameters, sample, proc sql	source	17
65	Automatically Map CDASH Data to the Study Data Tabulation Model (SDTM) Structure	Hao Xu, Hong Chen	In the pharmaceutical industry, CDISC SDTM is required by various regulatory agencies as the standard data structure for regulatory submission of clinical data. Manually mapping raw data to SDTM domains can be time-consuming and error-prone considering the complexity of clinical data and the strict requirements of the SDTM. However, this process can be much more efficient if the raw data is collected using CDASH standard, allowing for the automatic conversion to the SDTM data structure. This paper introduces a macro called %AUTOSDTM that can automatically create a SAS program for each SDTM domain (e.g. dm.sas for DM), that maps CDASH data to SDTM data	sdtm domain, proc sort, proc step, macro variable, raw data, clinical trial, macro definition, %if-%then-%else, cdisc	source	18
66	Automating Gorgeous Executive-Level Presentations Using SAS Office Analytics	Dwight J. Fowler	A lot of time and effort goes into creating presentations or dashboards for the purposes of management business reviews. Data for the presentation is produced from a variety of tools, and the output is cut and pasted into Microsoft PowerPoint or Microsoft `EXCEL`. Time is spent not only on the data preparation and reporting, but also on the finishing and touching up of these presentations. In previous years, SAS Global Forum authors have described the fundamental automation capabilities of SAS and Microsoft Office. The default look and feel of SAS output in Microsoft PowerPoint and `EXCEL` is not always adequate for the more polished requirement of an executive presentation. This paper focuses on how to combine the capabilities in SAS Enterprise Guide, SAS Visual Analytics, and Microsoft PowerPoint into a finished, professional presentation. We will build and automate a beautiful finished end product that can be refreshed by anyone with the click of a mouse	sas enterprise guide, bar chart, excel	source	18
67	Basic Ingredients for Blending the SAS Client with SAS Viya	Denise M. Poll	Let's walk through an example of communicating from the SAS client to SAS Viya. The demonstration focuses on how to use SAS language to establish a session, transport and persist data, and receive results. Learn how to establish communication with SAS Viya. Explore topics such as: What is a session? How do I make requests? What does my SAS log tell me? Get a deeper understanding of data location on the client and the server side. Learn about applying existing user formats, how to get listings or reports, and how to query sessions, data and properties	cas, proc step, proc format, sas log, sas viya, user-defined format, sas studio, libname statement, access control, proc print, proc options	source	20
68	Befriend SAS In-Database Technologies to Accelerate SAS Integration with Your Data Platform	Tatyana Petrova	You have SAS software. You have databases or data platforms like Hadoop, possibly with some large distributed data. If you already know how to make SAS talk to your data platforms, you have already taken a solid step toward a successful integration. But you might also want to know how to take this communication to a different level. If your data platform is the one that is built for massively parallel processing, chances are that SAS has already created the SAS Embedded Process framework that allows SAS tasks to be embedded next to your data sources for execution. SAS In-Database Technologies is a family of products that use this framework and provide an accelerated level of integration. This paper explains the core principles behind these technologies and presents application scenarios for each of these products	proc step, proc transpose, data cleaning, proc sql, stored procedure, data wrangling, sas model manager, parallel processing	source	16
69	Behavioral Spend Modeling of Cheque Card Data Using SAS Text Miner	Amelia Van Schalkwyk	Understanding customer behavior profiles is of great value to companies. Customer behavior is influenced by a multitude of elements-some are capricious, presumably resulting from environmental, economic, and other factors, while others are more fundamentally aligned with value and belief systems. In this paper, we use unstructured textual cheque card data to model and estimate latent spending behavioral profiles of banking customers. These models give insight into unobserved spending habits and patterns. SAS Text Miner is used in an atypical manner to determine the buying segments of customers and the latent buying profile using a clustering approach. Businesses benefit in the way the behavioral spend model is used. The model can be used for market segmentation, where each cluster is seen as a target marketing segment, leads optimization, or product offering where products are specifically compiled to align to each customer's requirements	customer segmentation, parsing	source pdf	14
70	Best Practices in Connecting External Databases to SAS	Mathieu Gaouette	Connecting database schemas to libraries in the SAS metadata is a very important part of setting up a functional and useful environment for business users. This task can be quite difficult for the untrained administrator. This paper addresses the key configuration items that often go unnoticed but can make a big difference. Using the wrong options can lead to poor database performance or even to a total lockdown, depending on the number of connections to the database	sas management console, sas enterprise guide	source	7
71	Best-Practice Programming Techniques Using SAS Software	Kirk Paul Lafler	It's essential that SAS users possess the necessary skills to implement "best practice" programming techniques when using the Base-SAS software. This presentation illustrates core concepts with examples to ensure that code is readable, clearly written, understandable, structured, portable, and maintainable. Attendees learn how to apply good programming techniques including implementing naming conventions for datasets, variables, programs and libraries; code appearance and structure using modular design, logic scenarios, controlled loops, subroutines and embedded control flow; code compatibility and portability across applications and operating platforms; developing readable code and program documentation; applying statements, options and definitions to achieve the greatest advantage in the program environment; and implementing program generality into code to enable its continued operation with little or no modifications	proc step, if-then-else, options statement, proc print, subsetting if, dataset, proc sql	source	10
72	Beyond IF THEN ELSE: Techniques for Conditional Execution of SAS Code	Joshua M. Horstman	Nearly every SAS program includes logic that causes certain code to be executed only when specific conditions are met. This is commonly done using the IF…THEN…ELSE syntax. In this paper, we will explore various ways to construct conditional SAS logic, including some that may provide advantages over the IF statement. Topics will include the SELECT statement, the `IFC` and `IFN` functions, the CHOOSE and WHICH families of functions, as well as some more esoteric methods. We'll also make sure we understand the difference between a regular IF and the %IF macro statement	if-then-else, subsetting if, where statement, ifn, select, ifc, macro definition, sas programmer, %if-%then-%else, missing value, proc format, whichn, execution phase, macro parameter, when, proc step, macro facility	source	10
73	Beyond Table Lookup: The Versatile SAS Hash Object	Paul M. Dorfman, Donald J. Henderson	Soon after the advent of the SAS hash object in SAS9, its early adopters realized that its potential functionality is much broader than merely using its fast table lookup capability for file matching. This is because in reality, the hash object is a versatile data storage structure with a roster of standard table operations such as create, drop, insert, delete, clear, search, retrieve, update, order, and enumerate. Since it is memory-resident and its key-access operations execute in O(1) time, it runs them as fast as or faster than other canned SAS techniques, with the added bonus of not having to code around their inherent limitations. Another advantage of the hash object as compared to the methods that had existed before its implementation is its dynamic, run-time nature and the ability to handle I/O all by itself, independently of the intrinsic statements of a `DATA STEP` or DS2 program calling its methods	hash table, sample, data wrangling, hash iterator, order by, composite key, do loop, key, proc sql, sorted data, lookup, dataset	source	27
74	Big Value from Big Data: SAS/ETS Methods for Spatial Econometric Modeling in the Era of Big Data	Guohui Wu, Jan Chvosta	Data that are gathered in modern data collection processes are often large and contain geographic information that enables you to examine how spatial proximity affects the outcome of interest. For example, in real estate economics, the price of a housing unit is likely to depend on the prices of housing units in the same neighborhood or nearby neighborhoods, either because of their locations or because of some unobserved characteristics that these neighborhoods share. Understanding spatial relationships and being able to represent them in a compact form are vital to extracting value from big data. This paper describes how to glean analytical insights from big data and discover their big value by using spatial econometric methods in `SAS/ETS` software	big data, response variable, euclidean distance, regression analysis	source	15
75	Breaking through the Barriers: Innovative Sampling Techniques for Unstructured Data Analysis	Murali Krishna Pagolu	As in any analytical process, data sampling is a definitive step for unstructured data analysis. Sampling is of paramount importance if your data is fed from social media reservoirs such as Twitter, Facebook, Amazon, and Reddit, where information proliferation happens minute by minute. Unless you have a sophisticated analytical engine and robust physical servers to handle billions of pieces of data, you can't use all your data for analysis without sampling. So, how do you sample textual data? The standard method is to generate either a simple random sample, or a stratified random sample if a stratification variable exists in the data. Neither of these two methods can reliably produce a representative sample of documents from the population data simply because the process does not encompass a step to ensure that the distribution of terms between the population and sample sets remains similar. This shortcoming can cause the supervised or unsupervised learning to yield inaccurate results	sample, response variable, proc freq	source	8
76	Bringing Real-Time Scoring to Your SAS Visual Analytics Dashboards with SAS Visual Statistics Score Code	Eli Kovick	Whether you are calculating a credit risk, a health risk, or something entirely different, there is a need for instant, on-the-fly risk score calculation across multiple industries. This paper demonstrates how you can produce individualized risk scores through interactive dashboards. Your risk scores are backed by powerful SAS analytics because they leverage score code that you produce in SAS Visual Statistics. Advanced topics, including the use of calculated items and parameters in your dashboards, as well as how to develop SAS Stored Processes capable of accepting parameters that are passed through your SAS Visual Analytics Dashboard are covered in detail	score code, insurance risk score, macro variable, sas enterprise guide, dataset	source	12
77	Build Apps for Your Enterprise with SAS and HTML5	Nikola Markovic	SAS is perfect for the job of building Enterprise Apps. Think about it: it speaks to almost any database you can think of, and is probably already hooked in to most of the data sources in your organisation. A fully fledged metadata security layer happens to already be integrated with your single sign-on authentication provider, and every time a user interacts with the system, their permissions are checked and the data their app asks for is automatically encrypted. SAS ticks all the boxes required by IT, and it means that the skills required to start developing Apps already sit within your department. Your team most likely already know what your App needs to do, so instead of writing lists of requirements, give them a HTML5 resource and together they can write and deploy the back end code themselves. The Apps run in the browser, the server-side logic is deployed using SAS .spk packages, and permissions are managed via Management Console	sas programmer, macro variable	source	13
78	Build Lightning-Fast Web Apps with HTML5 and SAS	Allan Bowe	What do we want? Web applications! When do we want them? Well.. Why not today? This author argues that the key to delivering web apps 'lightning fast' can be boiled down to a few simple factors, such as: Standing on the shoulders (not the toes) of giants. Specifically, learning and leveraging the power of free / open source toolsets such as Google's Angular, Facebook's React.js and Twitter Bootstrap Creating 'copy paste' templates for web apps that can be quickly re-used and tweaked for new purposes Using the right tools for the job (and being familiar with them) By choosing SAS as the back end, your apps will benefit from: Full blown analytics platform Access to all kinds of company data Full SAS metadata security (every server request is metadata validated) By following the approach taken in this paper, you may well find yourself in possession of an electrifying capability to deliver great content and professional-looking web apps faster than one can say "Usain Bolt"	sas management console, bootstrap, sas log, dataset	source	16
79	Building a Bridge between Risk and Finance to Address IFRS 9 and CECL	Ling Xiang, Anthony Mancuso, Martim Rocha	Historically, the risk and finance functions within a bank have operated within different rule sets and structures. Within its function, risk enjoys the freedom needed to properly estimate various types of risk. Finance, on the other hand, operates within the well-defined and structured rules of accounting, which are required for standardized reporting. However, the International Financial Reporting Standards (IFRS) newest standard, IFRS 9, brings these two worlds together: risk, to estimate credit losses, and finance, to determine the right level of provisions on the balance sheet to face credit losses, this implies total alignment between the two functions as the level of provisions should be based on the estimation. To help achieve this integration, SAS has introduced SAS Expected Credit Loss. SAS Expected Credit Loss enables customers to perform risk calculations in a controlled environment and to use those results for financial reporting within the same managed environment	ifrs 9, sas risk, expected loss	source	6
80	Building a Member Centric World from a Transactional Data Galaxy	Brian Mitchell	Health insurers have terabytes of transactional data. However, transactional data does not tell a member- level story. Humana Inc. is often faced with requirements for tagging (identifying) members with various clinical conditions such as diabetes, depression, hypertension, hyperlipidemia, and various member-level utilization metrics. For example, Consumer Health Tags are built to identify the condition (that is, diabetes, hypertension, and so on) and to estimate the intensity of the disease using medical and pharmacy administrative claims data. This case study takes you on an analytics journey from the initial problem diagnosis and analytics solution using SAS	proc sql, proc step, create table, order by	source	3
81	Building Bayesian Network Classifiers Using the HPBNET Procedure	Ye Liu, Weihua Shi, Wendy Czika	A Bayesian network is a directed acyclic graphical model that represents probability relationships and conditional independence structure between random variables. SAS Enterprise Miner implements a Bayesian network primarily as a classification tool; it supports naï ve Bayes, tree-augmented naï ve Bayes, Bayesian-network-augmented naï ve Bayes, parent-child Bayesian network, and Markov blanket Bayesian network classifiers. The HPBNET procedure uses a score-based approach and a constraint-based approach to model network structures. This paper compares the performance of Bayesian network classifiers to other popular classification methods such as classification tree, neural network, logistic regression, and support vector machines. The paper also shows some real-world applications of the implemented Bayesian network classifiers and a useful visualization of the results	proc step, probability distribution, data cleaning, network optimization, training set, response variable, machine learning, feature selection, create table, data mining	source	19
82	Building Intelligent Macros: Using Metadata Functions with the SAS Macro Language	Arthur L. Carpenter	The SAS macro language gives us the power to create tools that to a large extent can think for themselves. How often have you used a macro that required your input and you thought to yourself "Why do I need to provide this information when SAS already knows it?" SAS may well already know what you are being asked to provide, but how do we direct our macro programs to self-discern the information that they need? Fortunately there are a number of functions and other tools within SAS that can intelligently provide our programs with the ability to find and utilize the information that they require. If you provide a variable name, SAS should know its type and length; given a data set name, the list of variables should be known; given a library or libref, the full list of data sets that it contains should be known. In each of these situations there are functions that can be utilized by the macro language to determine and return these types of information. Given a libref these functions can determine the library's physical location and the list of all the data sets it contains. Given a data set they can return the names and attributes of any of the variables that it contains. These functions can read and write data, create directories, build lists of files in a folder, and even build lists of folders. Maximize your macro's intelligence; learn and use these functions	macro variable, macro facility, macro function, macro loop, do loop, dataset, sas log, proc step, proc contents, where statement, macro put, %if-%then-%else	source	17
83	Change in Themes of Billboard Top 100 Songs Over Time	Jayant Sharma, John Harden	Presentation		source	5
84	Change Management: Best Practices for Implementing SAS Prescriptive Analytics	Scott Shuler	When new technologies, workflows, or processes are implemented, an organization and its employees must embrace changes in order to ensure long-term success. This paper provides guidelines and best practices in change management that the SAS Advanced Analytics Division uses with customers when it implements prescriptive analytics solutions (provided by `SAS/OR` software). Highlights include engaging technical leaders in defining project scope and providing functional design documents. The paper also highlights SAS' approach in engaging business leaders on business scope, garnering executive-level project involvement, establishing steering committees, defining use cases, developing an effective communication strategy, training, and implementing of `SAS/OR` solutions	data cleaning, prescriptive analytics	source	4
85	Charting Your Path to Using the "New" SAS ODS and SG Graphics Successfully	Roger D. Muller	SAS Output Delivery System (`ODS`) Graphics started appearing in SAS 9.2. Collectively these new tools were referred to as "`ODS` Graphics," "SG Graphics" and "Statistical Graphics". When first starting to use these tools, the traditional `SAS/GRAPH` software user might come upon some very significant challenges in learning the new way to do things. This is further complicated by the lack of simple demonstrations of capabilities. Most graphs in training materials and publications are rather complicated graphs that, while useful, are not good teaching examples for starting purposes. This paper contains many examples of very simple ways to get very simple things accomplished. Many different graphs are developed using only a few lines of code each, using data from the SASHELP data sets. The use of the `SGPLOT`, SGPANEL, and SGSCATTER procedures are shown	proc step, proc template, sas studio, proc sgplot, sas enterprise guide, output delivery system, proc sgpanel, sample	source pdf	16
86	Check Please: An Automated Approach to Log Checking	Richann J. Watson	In the pharmaceutical industry, we find ourselves having to re-run our programs repeatedly for each deliverable. These programs can be run individually in an interactive SAS session, which enables us to review the logs as we execute the programs. We could run the individual programs in batch and open each individual log to review for unwanted log messages, such as ERROR, WARNING, uninitialized, have been converted to, and so on. Both of these approaches are fine if there are only a handful of programs to execute. But what do you do if you have hundreds of programs that need to be re-run? Do you want to open every single one of the programs and search for unwanted messages? This manual approach could take hours and is prone to accidental oversight. This paper discusses a macro that searches a specified directory and checks either all the logs in the directory, only logs with a specific naming convention, or only the files listed	where statement, sample, macro variable	source	12
87	Choose Carefully! An Assessment of Different Sample Designs on Estimates of Official Statistics	Leesha Delatie-Budair, Jessica Campbell	Designing a survey is a meticulous process involving a number of steps and many complex choices. For most survey researchers, the choice of a probability or non-probability sample is somewhat simple. However, throughout the sample design process, even with probability samples, there are more complex choices- each of which may affect the survey estimates. For example, the sampling statistician must decide whether to stratify the frame. If so, s/he has to decide how many strata, whether to explicitly stratify, and how should a stratum be defined. S/He also has to decide whether to use clusters, and, if so, how to define a cluster and what should be the ideal cluster size. The factors affecting these choices, along with the impact of different sample designs on survey estimates, are explored in this paper	sample, parameter, proc calis, random sampling, cv, design effect, sample size, proc surveyfreq, proc step, proc surveyselect, proc freq	source	14
88	Choosing the Best Fit for Your Client/Server Architecture: SAS Studio versus SAS Enterprise Guide	Shayne Muelling, John Brower	SAS is often deployed in a client/server architecture in which SAS Foundation is installed on a server and is accessed from each user's workstation. Many system administrators prefer that users not log on directly to the server to run SAS, nor do they want to set up a complex Citrix environment. SAS client applications are an attractive alternative for this type of architecture. But with the advent of multiple SAS Studio editions and ongoing enhancements to SAS Enterprise Guide, choosing the most suitable client application presents a challenge for many system administrators. To help guide you in this choice, this paper compares the administration of three SAS Foundation client applications that can be used in a client/server architecture: SAS Enterprise Guide, SAS Studio Basic, and SAS Studio Mid-Tier. The usage differences between SAS Studio and SAS Enterprise Guide have been addressed elsewhere	sas studio, sas enterprise guide	source	14
89	Circular Metadata Group Membership Can Make Us Dizzy!	Karen Hinkson, Greg Lehner	Today it is vital for an organization to manage, distribute, and secure content for its employees. In most cases, different groups of employees are interested in different content, and some content should not be available to everyone. It is the SAS administrator's job to design a metadata-group structure that makes managing content easier. SAS enables you to create any metadata-group organizational structure imaginable, and it is common to define a metadata-group structure that mimics the organization's hierarchy. Circular group memberships are frequently the cause of unexpected issues with SAS web applications. A circular group relationship can be as simple as having two groups that are members of one another. You might not be aware that you have defined this type of recursive association between groups	sas base, missing value, proc step, proc print, raw data, sas enterprise guide	source	22
90	Classification Decision Accuracy and Consistency Using SAS/IML Software	Sung-Hyuck Lee, Kyung Yong Kim	Presentation	accuracy	source	4
91	Classroom Success with SAS Grid Manager and SAS Visual Analytics: Coping With Big Data	John Jernigan, Ken Gahagan, Cheryl Doninger	The Institute for Advanced Analytics struggled to provide student computing environments capable of analyzing increasingly larger data sets for its Master of Science in Analytics program. For the fast-paced practicum, the centerpiece of the curriculum, waiting 24 hours for a `FREQ` procedure to complete was unacceptable. Practicum proposals from industry were pared down (or turned down) because the data sets were too large, depriving students of exciting and relevant learning experiences. By augmenting the practicum architecture with an 18-node computing cluster running SAS Grid Manager, SAS Visual Analytics, and the latest High-Performance Analytics procedures, we were able to dramatically increase performance and begin accepting terabyte-scale practicum proposals from industry	sas grid, data mining, proc freq, sas base, big data	source	11
92	Cold-Start Solution to A/B Testing Using Adaptive Sample Size Modification	Bo Zhang, Liwei Wang	A/B testing is a form of statistical hypothesis testing on two business options (A and B) to determine which is more effective in the modern Internet age. The challenge for startups or new product businesses leveraging A/B testing are two-fold: a small number of customers and poor understanding of their responses. This paper shows you how to use the `IML` and POWER procedures to deal with the reassessment of sample size for adaptive multiple business stage designs based on conditional power arguments, using the data observed at the previous business stage	a/b testing, sample size, sample, conditional power, type i error, proc step, effect size, normal distribution, proc iml	source	9
93	Comparing and Contrasting the LUA Procedure and the Macro Facility	Anand Vijayaraghavan	The `LUA` procedure is a relatively new SAS procedure, having been available since SAS 9.4. It enables you to use the Lua language as an interface to SAS and as an alternative scripting language to the SAS macro facility. This paper compares and contrasts `PROC LUA` with the SAS macro facility, showing examples of approaches and highlighting the advantages and disadvantages of each	macro definition, macro facility, proc lua, proc step, sas function, macro variable, macro function	source	9
94	Complex Merging of Emergency Department and Hospitalization Data to Create a Longitudinal Data Set	Charlotte Baker	Epidemiologists and other health scientists are often tasked with solving health problems but find collecting original data prohibitive for a multitude of reasons. For this reason, it is common to instead utilize secondary data such as that from emergency departments (ED) or inpatient hospital stays. In order to use some of these secondary data sets to study problems over time, it is necessary to link them together using common identifiers and still keep all the unique information about each ED visit or hospitalization. This paper will discuss a method that was used to combine five years worth of individual ED visits and five years worth of individual hospitalizations to create a single and (much) larger data set for longitudinal analysis	proc transpose, proc step, do loop, by group processing, sample, proc freq, proc sql	source	9
95	Construction of a Disease Network and a Prediction Model for Dementia	Jinwoo Cho	Regarding a human disease network, most of studies have estimated the associations of disorders primarily with gene or protein information. Those studies, however, have some difficulties in the data, because of massive volume of the data and huge computational cost. Instead, we constructed a human disease network that can describe the associations between diseases, using the claim data of Korean Health insurance. Through several statistical analyses, we show the applicability and suitability of the disease network. Furthermore, we have developed a statistical model that can predict a prevalence rate for dementia by utilizing significant associations of the network in a statistical perspective. The source of the data is Korean HIRA (Health Insurance Review and Assessment), Korean organization that manages the overall claims of health insurance	odds ratio, proc freq, logistic regression, frequency analysis, health insurance, response variable, regression analysis, logistic distribution, binary variable, data mining, sample, feature selection, insurance claims	source	14
96	Continuous Predictors in Regression Analyses	Ruth Croxford	This presentation discusses the options for including continuous covariates in regression models. In his book, Clinical Prediction Models, Ewout Steyerberg presents a hierarchy of procedures for continuous predictors, starting with dichotomizing the variable and moving to modeling the variable using restricted cubic splines or using a fractional polynomial model. This presentation discusses all of the choices, with a focus on the last two. Restricted cubic splines express the relationship between the continuous covariate and the outcome using a set of cubic polynomials, which are constrained to meet at pre-specified points, called knots. Between the knots, each curve can take on the shape that best describes the data. A fractional polynomial model is another flexible method for modeling a relationship that is possibly nonlinear	restricted cubic spline, regression analysis, degrees of freedom, interval variable, polynomial regression, response variable, sample, predictor variable, sample size, chi-square test, likelihood function, significance level, type i error	source	12
97	Convergence of Big Data, the Cloud, and Analytics: A Docker Toolbox for the Data Scientist	Donna De Capite	Learn what a container is and how it can be used to run SAS Analytics for Containers. This paper will describe the architecture of containers running in the public or private cloud. The topic of leveraging the distributed compute power of Hadoop will also be discussed. This paper discusses how to work with a SAS container running in a variety of Cloud platforms. Additional topics include provisioning web-browser based clients via Jupyter Notebooks and SAS Studio to empower data scientists with the tool of their choice. A customer use case will be discussed on how SAS Analytics for Containers enables their IT department to meet the ad hoc, compute intensive, and scaling demands of the organization. An exciting differentiator for the data scientist, is the ability to send a portion of the analytic workload to run inside their Hadoop cluster via the SAS Hadoop Accelerators	data science, sas studio, sample	source	11
98	Correlating a Customer's Interactive Voice Response (IVR) Journey to Their CSAT Scores	Vinoth Kumar Raja, Sumit Sukhwani, Dmitriy Khots	Interactive Voice Response (IVR) system is a powerful tool that automates routine inbound call tasks. Companies leverage this system and realize substantial savings by cutting down call center costs while customers from do-it-yourselfers to non-tech-savvies take advantage of this technology rather than wait in line to speak to a Customer Care Representative (CSR). The flip side of the coin is that customers often see IVR as a barrier to overcome in order to talk to a real person. So it is important that the IVR is managed in such a way that it is mutually beneficial for both businesses and their customers. If managing IVR is critical then measuring Customer satisfaction scores is paramount as it helps in understanding customers better. This paper discusses analysis of different use cases of how customer ratings correlate with their journey inside IVR	chi-square test, financial services, proc sql	source	5
99	Counter Radicalization through Investigative Insights and Data Exploitation Using SAS Viya	Lawrie Elder	This end-to-end capability demonstration illustrates how SAS Viya can aid intelligence, homeland security, and law-enforcement agencies in counterterrorism activities. Many of us are familiar with recent examples of agency failure to apportion significance to isolated pieces of information that, in context, are indicative of an escalating threat, and that require intervention. Recent terrorist acts have been carried out by radicalized individuals who should have been firmly on the organizational radar. Although SAS products perform analysis and interpretation of data that enables the law enforcement and homeland security communities to recognize and triage threats, intelligence information must be viewed in its full context. SAS Viya can rationalize previously disconnected capabilities in a single platform, empowering intelligence, security, and law enforcement agencies	sas viya, stream processing, real-time processing	source	10
100	Create a Unique Datetime Stamp for Filenames or Many Other Purposes	Joe DeShon	This paper shows how to use Base SAS to create unique date/time stamps which can be used for naming external files. These file names provide automatic versioning for systems and are intuitive and completely sortable, providing enhanced flexibility when compared to generation data sets, which could be created by SAS or by the operating system	macro variable, sas base, data step, excel, macro definition, dataset	source	11
101	Creating a Departmental Standard SAS Enterprise Guide Template	Amanda Pasch, Chris Koppenhafer	This paper describes an ongoing effort to standardize and simplify SAS coding across a rapidly growing analytics team in the health care industry. The number of SAS analysts in Kaiser Permanente's Data and Information Management Enhancement (DIME) department has nearly doubled in the past two years, going from approximately 25 to 50. The level of experience and technical skill varies greatly across the team - some analysts need to use SAS daily as part of their toolset, and some may not touch it for weeks. Analysts are required to provide quick turn-around on a large volume of analytical requests in this dynamic environment. An effort was initiated in 2016 to create a SAS Enterprise Guide template to standardize and simplify coding across the department. This paper focuses on the process in which the template was initiated, drafted, and socialized across a diverse team of SAS analysts	proc step, sas enterprise guide, excel, proc sql	source	5
102	Creating Daily Rolling 6-Month Average and Cumulative Enrollment Graphs Using PROC EXPAND and INTNX	Susan Schleede	Clinical research study enrollment data consists of subject identifiers and enrollment dates that investigators use to monitor enrollment progress. Meeting study enrollment targets is critical to ensuring there will be enough data and endpoints to ensure the predefined statistical power of the study. For clinical trials which do not experience heavy, nearly daily enrollment, there will be a number of dates on which no subjects were enrolled. Therefore, graphs of cumulative enrollment represented by a smoothed line can give a false impression, or imprecise reading, of study enrollment. A more accurate display is a step function graph that includes dates where no subjects were enrolled. Rolling average graphs often start with summing the data by month and creating a rolling average from the monthly sums	moving average, proc step, time series data, proc sql, datepart, clinical research, time series analysis, proc gplot	source	10
103	Creating Personal Game Statistics for Video Game Events: Examples from EVE Online and Ingress	Peter Timusk	Presentation		source	5
104	Creating the Perfect BI Report: Where to Begin	Lisa Eckler	We've learned a great deal about how to develop great reports and about business intelligence (BI) tools and how to use them to create reports, but have we figured out how to create true BI reports? Not every report that comes out of a BI tool provides business intelligence! In pursuit of the perfect BI report, this paper explores how we can combine the best of lessons learned about developing and running traditional reports and about applying business analytics in order to create true BI reports that deliver integrated analytics and intelligence	reporting, business intelligence, data profiling, data governance, data science	source	17
105	Data Analytics and Visualization Tell Your Story with a Web Reporting Framework Based on SAS	Qiang Li	For all business analytics projects, big or small, the results are used to support business or managerial decision-making processes, and many of them eventually lead to business actions. However, executives or decision makers are often confused and feel uninformed about contents when presented with complicated analytics steps, especially when multi-processes or environments are involved. After many years of research and experiment, a web reporting framework based on SAS Stored Processes was developed to smooth the communication between data analysts, researches, and business decision makers. This web reporting framework uses a storytelling style to present essential analytical steps to audiences, with dynamic HTML5 content and drill-down and drill-through functions in text, graph, table, and dashboard formats. No special skills other than SAS programming are needed for implementing a new report. The model-view-controller (MVC) structure in this framework significantly reduced the time needed for developing high-end web reports for audiences not familiar with SAS. Additionally, the report contents can be viewed by tablet or smartphone users. A business analytical example is demonstrated during this session. By using this web reporting framework based on SAS Stored Processes, many existing SAS results can be delivered more effectively and persuasively on a SAS Enterprise BI platform	business intelligence, data profiling	source	8
106	Data Can Be Beautiful: Crafting a Compelling Story with SAS Visual Analytics	Cheryl L. Coyle, Mark Malek, Chelsea Mayse, Vaidehi Patil, Sierra Shell	Do your reports effectively communicate your message? Are your reports aesthetically pleasing? This paper provides guidance for SAS Visual Analytics Designer users to create compelling data stories. The primary goal of a report is to enable readers to quickly and easily get answers to their questions. The visualizations you use for your data, the quantity and arrangement of information included, and the use of color all contribute to the reader's ability to understand your report. This paper describes how to guide your reader's movement through a report to support comprehension of the data story. It includes tips on how to express quantitative data using the most appropriate graphs, suggests ways to organize content using visual design techniques, and explains how color can be used to enhance the efficacy of a report. A thoughtfully designed report can educate the viewer without compromising visual appeal	bar chart, data visualization, missing value, line chart, scatter plot, series plot	source	20
107	Data Grids in Business Rules, Decisions, Batch Scoring, and Real-Time Scoring	Carl Sommer, Ernest Jessee, Chris Upton	Users want more power. SAS delivers. Data grids are a new data type available to users of SAS Business Rules Manager and SAS Decision Manager. These data grids can be deployed to both batch and web service scoring for data mining models and business decisions. Use rs will learn how to construct data with grid data types, create business rules using high-level expressions, and deploy decisions to both batch and web services for scoring	web service, json	source	10
108	Data Management and Access Considerations for SAS Visual Analytics	Chun-Yian Liew	SAS Visual Analytics is a very powerful tool for users to visually explore data, but in some organizations not all data should be available for everybody. And although it is relatively easy to scale up a SAS Visual Analytics environment when the need for data increases, it still would be beneficial to set up a structure where the organization can keep control over who actually has the right to load data versus providing everybody the right to load data into a SAS Visual Analytics environment. Within this paper a potential solution is shown by providing a high-level overview of the SAS Visual Analytics data access management solution at ING Bank in the Netherlands for the Risk Organization	business intelligence, access control	source	7
109	Data Management for Cybersecurity	Alex Anglin	As an information security or data professional, you have seen and heard about how advanced analytics has impacted nearly every business domain. You recognize the potential of insights derived from advanced analytics to improve the information security of your organization. You want to realize these benefits, but also to understand their pitfalls. In order to successfully apply advanced analytics to the information security business problem, proper application of data management processes and techniques is of paramount importance. Based on professional services experience in implementing SAS Cybersecurity, this session teaches you about the data sources used, the activities involved in properly managing this data, and the means to which these processes address information security business problems. You will come to appreciate how using advanced analytics in the information security domain requires more than just the application of tools or modeling techniques	data governance, data wrangling	source	6
110	Data Science Rex: How Data Science Is Evolving (or Facing Extinction) across the Academic Landscape	Jennifer Priestley	The discipline of data science has seen an unprecedented evolution from primordial darkness to becoming the academic equivalent of an apex predator on university campuses across the country. But survival of the discipline is not guaranteed. This session will explore the genetic makeup of programs that are likely to survive, the genetics of those that are likely to become extinct and explore the role the business community plays in that evolutionary process	data science, sas engineer	source	6
111	DATA Step in SAS Viya: Essential New Features	Jason Secosky	The `DATA STEP` is the familiar and powerful data processing language in SAS and now SAS Viya. The `DATA STEP`'s simple syntax provides row-at-a-time operations to edit, restructure, and combine data. New to the `DATA STEP` in SAS Viya are a varying-length character data type and parallel execution. Varying- length character data enables intuitive string operations that go beyond the 32KB limit of current `DATA STEP` operations. Parallel execution speeds the processing of big data by starting the `DATA STEP` on multiple machines and dividing data processing among threads on these machines. To avoid multi- threaded programming errors, the run-time environment for the `DATA STEP` is presented along with potential programming pitfalls. Come see how the `DATA STEP` in SAS Viya makes your data processing simpler and faster	by group processing, cpu time, sas viya, character variable, proc step, big data, data wrangling, parallel processing, character functions	source	14
112	Data Validation Using the SAS SORT Procedure and MERGE Statement	Barry Frye	Data validation plays a key role as an organization engages in a data governance initiative. Better data lead to better decisions. This applies to public schools as well as business entities. Each Local Educational Agency (LEA) in Pennsylvania reports children with disabilities to the Pennsylvania Department of Education (PDE) in compliance with IDEA (Individuals with Disabilities Education Act). PDE provides a Comparison Spreadsheet to each LEA to assist in their data validation process. This Comparison Spreadsheet provides counts of various categories for the current and previous year. LEAs use the Comparison Spreadsheet to validate data submitted to PDE. This paper discusses how the Base SAS `SORT` procedure and `MERGE` statement extract hidden information behind the counts to assist LEAs in their data validation process	merge statement, proc sort, data cleaning, sas base	source	11
113	Data Visualization from SAS to Microsoft SharePoint	Xiaogang Tang	SharePoint is a popular web application framework and platform developed by Microsoft, widely used for content and document management by large companies and organizations. Connecting SAS with SharePoint combines the power of these two into one. As a continuation of my SAS Paper 11520-2016 "Releasing the Power of SAS into Microsoft SharePoint", this paper expands on how to implement data visualization from SAS to Microsoft SharePoint. The paper shows users how to use SAS Procedures and SAS Output Delivery System (`ODS`) to create and send visualization output files from SAS to SharePoint Document Library. Several SAS code examples and output screenshots are included to show how to create tables, charts, plots and maps from SAS to SharePoint	dataset, bar chart, data visualization, proc sgplot, proc step, output delivery system, sample, proc freq	source	14
114	Dataninjas: Modeling Life Insurance Risk	David Allen, Seung Lee		odds ratio, logistic regression, life insurance, sample, logistic distribution, regression analysis	source	7
115	Decorative Infographics Using SAS	Sanjay N. Matange	The SAS 9.4 `SGPLOT` procedure is a great tool for creating all types of graphs, from business graphs to complex clinical graphs. The goal for such graphs is to convey the data in a simple and direct manner with minimal distractions. But often you need to grab the attention of a reader in the midst of a sea of data and graphs. For such cases, you need a visual that can stand out above the rest of the noise. Such visuals insert a decorative flavor into the graph to attract the eye of the reader and to encourage them to spend more time studying the visual. This presentation discusses how you can create such attention- grabbing visuals using the `SGPLOT` procedure	bar chart, proc sgplot, scatter plot	source	11
116	Deploying SAS on Software-Defined and Virtual Storage Systems	Tony Brown	This paper presents considerations for deploying SAS Foundation across software-defined storage (SDS) infrastructures, and within virtualized storage environments. There are many new offerings on the market that offer easy, point-and-click creation of storage entities with simplified management. Internal storage area network (SAN) virtualization also removes much of the hands-on management for defining storage device pools. Automated tier software further attempts to optimize data placement across performance tiers without manual intervention. Virtual storage provisioning and automated tier placement have many time-saving and management benefits. In some cases, they have also caused serious unintended performance issues with heavy large-block workloads, such as those found in SAS Foundation. You must follow best practices to get the benefit of these new technologies while still maintaining performance	cards	source	18
117	Deriving Rows in CDISC ADaM BDS Data Sets Using SAS DATA Step Programming	Sandra Minjoe	The ADaM Basic Data Structure (BDS) can be used for many analysis needs. We all know the SAS `DATA STEP` is a very flexible and powerful tool for data processing. In fact, the `DATA STEP` is very useful in the creation of a non-trivial BDS dataset. This paper walks through a series of examples showing use of the SAS `DATA STEP` when deriving rows in BDS. These examples include creating new parameters, new timepoints, and changes from multiple baselines	study day, output, adam dataset, adlb, controlled terminology, cdisc	source	17
118	Designing for Performance: Best Practices for SAS Visual Analytics Reports	Kerri Rivers	As a SAS Visual Analytics report designer, your goal is to create effective data visualizations that quickly communicate key information to report readers. But what makes a dashboard or report effective? How do you ensure that key points are understood quickly? One of the most common questions asked about SAS Visual Analytics is, what are the best practices for designing a report? Experts like Stephen Few and Edward Tufte have written extensively about successful visual design and data visualization. This paper focuses mainly on a different aspect of visual reports - the speed with which online reports render. In today's world, instant results are almost always expected. And the faster your report renders, the sooner decisions can be made and actions taken. Based on proven best practices and existing customer implementations, this paper focuses on server- side performance, client-side performance, and design performance	bar chart, dataset, line chart, star schema, character variable, sas administrator, note	source	19
119	Detecting and Adjusting Structural Breaks in Time Series and Panel Data Using the SSM Procedure	Rajesh S. Selukar	Detection and adjustment of structural breaks are an important step in modeling time series and panel data. In some cases, such as studying the impact of a new policy or an advertising campaign, structural break analysis might even be the main goal of a data analysis project. In other cases, the adjustment of structural breaks is a necessary step to achieve other analysis objectives, such as obtaining accurate forecasts and effective seasonal adjustment. Structural breaks can occur in a variety of ways during the course of a time series. For example, a series can have an abrupt change in its trend, its seasonal pattern, or its response to a regressor. The `SSM` procedure in `SAS/ETS` software provides a comprehensive set of tools for modeling different types of sequential data, including univariate and multivariate time series data and panel data. These tools include options for easy detection and adjustment of a wide variety of structural breaks	proc step, time series analysis, regression analysis, seasonality, proc ucm	source	18
120	Develop a Simple Data Governance Program for a SAS Customer Intelligence Environment in 90 Days	Aaron Nelson	This paper describes speci fic actions to be taken to increase the usability, data consistency, and per formance of an advanced SAS Customer Intelligence solution for marketing and analytic purposes. In addition, the paper focuses on the establishment of a data governance prog ram to support the processes that take place w ithin this environment. This paper presents our experiences developing a data governance "light" program for the enterprise data w arehouse and its sources as w ell as for the data marts created dow nstream to address analytic and ca mpaign management purposes. The challenge w as to design a data governance program for this system in 90 days	data governance, data cleaning, data mart, proc sql, data standard	source	12
121	Developing a Predictive Model of Physician Attribution of Patient Satisfaction Surveys	Ken Ferrell	For all healthcare systems, considerable attention and resources are directed at gauging and improving patient satisfaction. Dignity Health has made considerable efforts in improving most areas of patient satisfaction. However, improving metrics around physician interaction with patients has been challenging. Failure to improve these publicly reported scores can result in reimbursement penalties, damage to Dignity's brand and an increased risk of patient harm. One possible way to improve these scores is to better identify the physicians that present the best opportunity for positive change. Currently, the survey tool mandated by the Centers for Medicare and Medicaid Services (CMS), the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS), has three questions centered on patient experience with providers, specifically concerning listening, respect, and clarity of conversation	data cleaning, sample, regression analysis, lasso regression, proc glmselect, validation set, proc step, bootstrap, predictor variable, sample size	source	5
122	Developing a Product Recommendation Platform for Real-Time Decisions in the Direct Sales Environment	Francisco Pigato	The applying product recommendation solutions for the final customer in e-commerce is already a known practice. By crossing consumer profile information with their behavior, the trend is to generate results that are more than satisfactory to the business. Natura's challenge was to create the same kind of solution for its sales representatives (consultants) i n a web platform developed for ordering. As these consultants are not buying for their own consumption, but to meet a demand from their customers, there is no information about the behavior and preferences of the final customer. Through the creation of a shopping basket concept for its consultants, Natura can develop a new recommendation solution. For this, it created an algorithm using association analysis (Basket Market) and implemented this algorithm directly in the ordering platform through a real-time decision solution (RTD)	sas enterprise guide, data mining	source	7
123	Developing Your Data Strategy	Gregory S. Barnes Nelson	The ever-growing volume of data challenges us to keep pace in ensuring that we use it to its full advantage. Unfortunately, often our response to new data sources, data types and applications is somewhat reactionary. There exists a misperception that organizations have precious little time to consider a purposeful strategy without disrupting business continuity. Strategy is a phrase that is often misused and ill-defined. However, it is nothing more than a set of integrated choices that help position an initiative for future success. With that in mind, this presentation will cover the key elements defining data strategy. Key topics include: What data should we keep or toss? How should we structure data? (warehouse vs. data lake vs. real-time/event stream) * How do we store data? (cloud, virtualization, federation, cloud, Hadoop), the approach we use to integrate and cleanse (ETL vs	data lake, structured data	source	15
124	Developing Your Own SAS Studio Custom Tasks for Advanced Analytics	Elliot Inman, Olivia Wright	Standard SAS Studio tasks already include many advanced analytic procedures for data mining and other high-performance models. These existing tasks enable the point-and-click generation and execution of SAS code. However, you can extend the power of standard tasks by creating tasks of your own. You can create tasks for the very latest SAS statistical procedures, your own default model definitions, or your previously developed `SAS/STAT` software or SAS macro code. Best of all, these point-and-click tasks can be developed directly in SAS Studio without the need to compile binaries using third-party software. This paper demonstrates three approaches to developing custom tasks. First, we build a custom task to provide point-and-click access to the new Item Response Theory procedure commonly used to analyze educational test and opinion survey data	sas studio, proc step, macro definition, data science, macro facility, sas base, ods output, ascii, data mining, output delivery system	source	20
125	Dictionaries: Referencing a New PROC FCMP Data Type	Andrew Henrick, Mike Whitcher, Karen Croft	Hash objects have been supported in the `DATA STEP` and the `FCMP` procedure for a while, but have you ever felt that hash objects could do a little more? For example, what if you needed to store more than doubles and character strings? Introducing `PROC FCMP` dictionaries. Dictionaries allow you to create references not only to numeric and character data, but they also give you fast in-memory hashing to arrays, other dictionaries, and even `PROC FCMP` hash objects. This paper gets you started using `PROC FCMP` dictionaries, describes usage syntax, and explores new programming patterns that are now available to your `PROC FCMP` programs, functions, and subroutines in the new SAS ViyaTM platform environment	hash table, proc fcmp, proc step, sysrc, character constant, hash iterator	source	15
126	Differentiate Effects from the Noise of Promotional Marketing Campaigns	Alex Glushkovsky, Matthew Fabian	In highly competitive markets, the response rates to economically reasonable marketing campaigns are as low as a few percentage points or less. In that case, the direct measure of the delta between the average key performance indicators (KPIs) of the treated and control groups is heavily "contaminated" by non-responders. This paper focuses on measuring promotional marketing campaigns with two properties: (1) price discounts or other benefits, which are changing profitability of the targeted group for at least the promotion periods, and (2) impact of self-responders. The paper addresses the decomposition of the KPI measurement between responders and non-responders for both groups. Assuming that customers who rejected promotional offers will not change their behavior and that non-responders of both treated and control groups are not biased, the delta of the average KPIs for non-responders should be equal to zero. In practice, this component might significantly deviate from zero	treatment effect, sample size, outlier, sample, campaign response, randomization	source	9
127	Distances: Let SAS Do the Heavy Lifting	Jason O'Day	SAS has a very efficient and powerful way to get distances between an event and a customer. Using the tables and code located at http://s upport.sas.com/rnd/datavisualization/mapsonline/html/geocode.html#street (1), you can load latitude and longitude to addresses that you have for your events and customers. Once you have the tables downloaded from SAS, and you have run the code to get them into SAS data sets, this paper helps guide you through the rest using `PROC GEOCODE` and the GEODIST function. This can help you determine to whom to market an event. And, you can see how far a client is from one of your facilities	dataset, proc step	source pdf	6
128	Diving Deep into ODS Graphics Styles	Dan Heath	Creating an effective style for your graphics can make the difference between clearly conveying your message to your audience and hiding your message in a sea of lines, markers, and text. There have been a number of books written on the concepts of effective graphics, but you need an understanding of how styles work in your environment to correctly apply those principles. The goal of this paper is to give you an in-depth discussion of how styles are applied to SAS `ODS` graphics, from the `ODS` style level all the way down to the graph syntax. This discussion will include information about differences in grouped versus non-grouped plots, precedence order of style application, using style references, and much more. Don't forget your scuba gear!	proc step, proc sgplot, box plot, bar chart, class variable, scatter plot, interval variable, waterfall chart, outlier, histogram	source	19
129	Do You Have a Disaster Recovery Plan for Your SAS Infrastructure?	Margaret A. Crevar	Are you prepared if a disaster happens? If your company relies on SAS applications to stay in business, you should have a Disaster Recovery Plan (DRP) in place. By a DRP, we mean documentation of the process to recover and protect your SAS infrastructure (SAS binaries, the operating system that is tuned to run your SAS applications, and all the pertinent data that the SAS applications require) in the event of a disaster. This paper discusses what needs to be in this plan to ensure that your SAS infrastructure not only works after it is recovered, but is able to be maintained on the recovery hardware infrastructure	recovery planning, dataset	source	8
130	Document and Enhance SAS Code, Data Sets, and Catalogs with SAS Functions, Macros, and Metadata	Louise S. Hadden, Roberta J. Glass	Discover how to document your SAS programs, data sets, and catalogs with a few lines of code that include SAS functions, macro code, and SAS metadata. Do you start every project with the best of intentions to document all of your work, and then fall short of that aspiration when deadlines loom? Learn how SAS system macro variables can provide valuable information embedded in your programs, logs, lists, catalogs, data sets and `ODS` output; how your programs can automatically update a processing log; and how to generate two different types of codebooks	proc step, macro variable	source pdf	12
131	Does Factor Indeterminacy Matter in Multidimensional Item Response Theory?	Chong Ho Yu	This paper aims to illustrate proper applications of multi-dimensional item response theory (MIRT), which is available in SAS's `PROC IRT`. MIRT combines item response theory (IRT) modeling and factor analysis when the instrument carries two or more latent traits. While it may seem convenient to accomplish two tasks simultaneously by employing one procedure, users should be cautious of mis-interpretations. This illustration utilizes the 2012 Programme for International Student Assessment (PISA) data set collected by Organization for Economic Cooperation and Development. Because there are two known sub-domains in the PISA test (reading and math), `PROC IRT` was programmed to adopt a two-factor solution. Additionally, the loading plot, dual plot, item difficulty/discrimination plot, and test information function plot in JMP were utilized to examine the psychometric properties of the PISA test. When reading and math items were analyzed in SAS's MIRT, 7-13 latent factors are suggested	factor analysis, proc step, scree plot, correlation	source pdf	7
132	Don't Let Your Annual Report Be Such a Manual Report: Combining Text, Graphs, and Tables in One Doc	Ben Cochran	This paper looks at methods of combining all kinds of output, such as text, graphs and tables, into a single pdf file. The process is accomplished totally within SAS. This paper is the product of having done this for an organization to improve its annual report process. What originally took a few months to produce, now takes only a few minutes	txt, proc step, proc report, conditional do loop	source	15
133	DS2 with Both Hands on the Wheel	Peter W. Eberhardt, Xue Yao	The `DATA STEP` has served SAS programmers well over the years, and although it is handy, the new, exciting, and powerful DS2 is a significant alternative to the `DATA STEP` by introducing an object-oriented programming environment. It enables users to effectively manipulate complex data and efficiently manage the programming through additional data types, programming structure elements, user-defined methods, and shareable packages, as well as threaded execution. This tutorial is developed based on our experiences with getting started with DS2 and learning to use it to access, manage, and share data in a scalable and standards-based way. It facilitates SAS users of all levels to easily get started with DS2 and understand its basic functionality by practicing the features of DS2	missing value, hash table, macro function, proc step, sas base, macro variable, proc fcmp, sas function, sas programmer, proc sql, parallel processing, return, sysdate9, macro definition, temporary array, market basket analysis, dataset	source zip	32
134	Easing into Data Exploration, Reporting, and Analytics Using SAS Enterprise Guide	Marje Fecht	Whether you have been programming in SAS for years, are new to it, or have dabbled with SAS Enterprise Guide before, this hands-on workshop sheds some light on the depth, breadth, and power of the Enterprise Guide environment. With all the demands on your time, you need powerful tools that are easy to learn and deliver end-to-end support for your data exploration, reporting, and analytics needs. Included are the following: data exploration tools formatting code-cleaning up after your coworker s enhanced programming environment (and how to calm it down) easily creating reports and graphics producing the output formats you need (XLS, PDF, RTF, HTML) workspace layout * productivity tips. This workshop uses SAS Enterprise Guide 7.1, but most of the content is applicable to earlier versions	sas enterprise guide, bar chart, scatter plot, vertical bar chart, proc freq, proc step	source zip	17
135	Easy Polar Graphs with SG Procedures	Prashant Hebbar, Sanjay N. Matange	Some data is best visualized in a polar orientation, particularly when the data is directional or cyclical. Although the SG procedures and Graph Template Language (GTL) do not directly support polar coordinates, they are quite capable of drawing such graphs with a little bit of data processing. We demonstrate how to convert your data from polar coordinates to Cartesian coordinates and use the power of SG procedures to create graphs that retain the polar nature of your data. Stop going around in circles: let us show you the way out with SG procedures!	proc sgplot, bar chart, scatter plot, series plot, regression analysis	source	11
136	Enabling Advanced Customer Value Management with SAS	Noorulain Malik	This paper will help the Telecom operator marketers, campaign designers who are responsible for identifying the best customer to target in a campaign, determine which media channel, offer, or campaign program to the customers resulting to increase revenue and profit margins. This paper will provide details how STC achieved advance customer value management using SAS Marketing Automation SAS Decision Manager and SAS Enterprise Miner	data mining, next best offer	source	7
137	Enhancing Customer Experience through Text Analysis of Survey Comments	Vinoth Kumar Raja, Sumit Sukhwani, Dmitriy Khots	Customer feedback is a critical aspect of businesses in today's world as it is invaluable in determining what customers like and dislike about the business' service. This loop of regularly listening to customers' voice through survey comments and improving services based on analytics will lead to better business and more importantly enhancement in customer experience. The challenge here is to classify and analyze these unstructured text comments to gain insights and focus on areas of improvement. The purpose of this paper is to illustrate how Text Mining in SAS Enterprise Miner 14.1 helped one of our clients - a leading financial services company - convert their customer's problems into opportunities. The customers' feedback pertaining to their experience with Interactive Voice Response (IVR) system is collected by an Enterprise Feedback Management (EFM) Company. The comments are then split into two groups as it helps us differentiate customer opinions	data mining, customer experience, financial services	source	8
138	Enterprise Data Management in a Multi-tenant SAS Business Intelligence Environment	Chun-Yian Liew	Sometimes it might be beneficial to share a SAS Business Intelligence environment with multiple tenants within an enterprise, but at the same time this might also introduce additional complexity with regard to the administration of data access. This paper shows one possible setup by sharing a high level overview of such an environment within the ING Bank in the Netherlands for the Risk organization	access control, business intelligence	source	7
139	Escape the Desktop with ODS EPUB	David W. Kelley	The Base SAS 9.4 Output Delivery System (`ODS`) EPUB destination enables users to deliver SAS reports as e-books on Apple mobile devices. `ODS` EPUB e-books are truly mobile - you don't need an Internet connection to read them. Just install Apple's free iBooks app, and you're good to go. This paper shows you how to create an e-book with `ODS` EPUB and sideload it onto your Apple device. You will learn new SAS 9.4 techniques for including text, images, audio, and video in your `ODS` EPUB e-books. You will understand how to customize your e-book's table of contents (TOC) so that readers can easily navigate the e-book. And you will learn how to modify the `ODS` EPUB style to create specialized presentation effects. This paper provides beginning to intermediate instruction for writing e-books with `ODS` EPUB. Please bring your iPad, iPhone, or iPod to the presentation so that you can download and read the examples	proc step, proc freq, ods html, ods pdf, excel	source	14
140	Estimating Causal Effects from Observational Data with the CAUSALTRT Procedure	Michael Lamm, Yiu-Fai Yung	Randomized control trials have long been considered the gold standard for establishing causal treatment effects. Can causal effects be reasonably estimated from observational data too? In observational studies, you observe treatment T and outcome Y without controlling confounding variables that might explain the observed associations between T and Y. Estimating the causal effect of the treatment T therefore requires adjustments that remove the effects of the confounding variables. The new CAUSALTRT (causal-treat) procedure in `SAS/STAT` 14.2 enables you to estimate the causal effect of a treatment decision by modeling either the treatment assignment T or the outcome Y, or both. Specifically, modeling the treatment leads to inverse probability weighting methods, and modeling the outcome leads to regression methods	treatment effect, propensity score, potential outcomes, bootstrap, confidence interval, response variable, proc step, regression analysis	source	18
141	Estimation of Student Growth Percentile Using SAS Procedures	Hongwook Suh, Robert Ankenmann	Presentation		source	4
142	Estimation Strategies Involving Pooled Survey Data	Taylor Lewis	Pooling two or more cross-sectional survey data sets (i.e., stacking the data sets on top of one another) is a strategy often utilized by researchers for one of two purposes: (1) to more efficiently conduct significance tests on point estimate changes observed over time; or (2) to increase the sample size in hopes of improving the precision of a point estimate. The latter purpose is especially common when making inferences on a subgroup, or domain, of the target population insufficiently represented by a single survey data set. The aim of this paper is to walk through a series of practical estimation objectives that can be tackled by analyzing data from two or more pooled survey data sets. Where applicable, the resulting interpretive nuances are discussed	sample, proc step, proc calis, proc surveymeans, covariance, proc surveyreg, binary variable, sample size, regression analysis, proc reg, random sampling, estimate, parameter	source	15
143	Evaluating Predictive Accuracy of Survival Models with PROC PHREG	Changbin Guo, Ying C. So, Woosung Jang	Model validation is an important step in the model building process because it provides opportunities to assess the reliability of models before their deployment. Predictive accuracy measures the ability of the models to predict future risks, and significant developments have been made in recent years in the evaluation of survival models. `SAS/STAT` 14.2 includes updates to the `PHREG` procedure with a variety of techniques to calculate overall concordance statistics and time-dependent receiver operator characteristic (ROC) curves for right-censored data. This paper describes how to use these criteria to validate and compare fitted survival models and presents examples to illustrate these applications	proc step, roc curve, proc phreg, proc logistic, logistic distribution, c-statistic, censoring, logistic regression	source	16
144	Examining Higher Education Performance Metrics with SAS Enterprise Miner and SAS Visual Analytics	Taylor Blaetz, Tuesdi Helbig, Gina Huff, Matt T. Bogard	Given the proposed budget cuts to higher education in the state of Kentucky, public universities will likely be awarded financial appropriations based on several performance metrics. The purpose of this project was to conceptualize, design, and implement predictive models that addressed two of the state's metrics: six-year graduation rate and fall-to-fall retention for freshmen. The Western Kentucky University (WKU) Office of Institutional Research analyzed five years' worth of data on first time, full time bachelor's degree seeking students. Two predictive models evaluated and scored current students on their likelihood to stay enrolled and their chances of graduating on time. Following an ensemble of machine-learning assessments, the scored data were imported into SAS Visual Analytics, where interactive reports allowed users to easily identify which students were at a high risk for attrition or at risk of not graduating on time	sample, response variable, proc hpsplit, roc curve, logistic regression, data mining, gradient boosting, imputation, key performance indicator, logistic distribution, proc step, regression analysis, data cleaning, likelihood function, missing value, proc freq, score code, proc sql	source	15
145	Exploiting Competitor Data Using SAS/ACCESS Interface to Hadoop	Kayne Putman	The British Airways (BA) revenue management team is responsible for surfacing prices made available in the market with the objective of maximizing revenue. BA is currently working to understand how publicly available competitor data can help facilitate better decision making. Due to the low level of aggregation, competitor data is too large (and consequently too expensive) to store on conventional relational databases. Therefore, it has been stored on a small Hadoop installation at BA. Thanks to `SAS/ACCESS` Interface to Hadoop, we have been able to run our complex algorithms on these large data sets without changing the way we work and whilst exploiting the full capabilities of SAS	revenue management, proc sql, analytics platform, sql pass-through	source	6
146	Exploration of Information Technology-Related Barriers Affecting Rural Primary Care Clinics	Ipe Simon Paramel, Ankita Srivastava, Onkar Jadhav, Jennifer Briggs, Georgina Ankrah	With an aim to improve rural healthcare, Oklahoma State University (OSU) Center for Health Systems Innovation (CHSI) conducted a study with primary care clinics (N=35) in rural Oklahoma to identify possible impediments to clinic workflows. T he study entailed semi-structured personal interview s (N=241) and administered an online survey using an iPad (N=190). Respondents encompass ed all consenting clinic constituents (physicians, nurses, practice managers, schedulers). Quantitative data f rom surveys revealed that electronic medical records (EMRs ) are well accepted and contributed to increasing workflow efficiency. However, t he qualitative data from interviews reveals that ther e are IT-related barriers like Internet connectivity, hardware p roblems, and inefficiencies in information system platforms . Interview responses identified six IT -related response categories (computer, connectivity, EMR -related, fax, paperwork, and phone calls) that routinely affect clinic workflow	anova, proc step, proc anova, proc genmod, missing value, proc glm, one-way anova, sas enterprise guide, nominal variable, data science	source	11
147	Exploring the Art and Science of SAS Text Analytics: Best Practices in Developing Rule-Based Models	Murali Krishna Pagolu, Cheyanne Baird, Christina Engelhardt	Traditional analytical modeling, with roots in statistical techniques, works best on structured data. Structured data enables you to impose certain standards and formats in which to store the data values. For example, a variable indicating gas mileage in miles per gallon should always be a number (for example, 25). However, with unstructured data analysis, the free-form text no longer limits you to expressing this information in only one way (25 mpg, twenty-five mpg, and 25M/G). The nuances of language, context, and subjectivity of text make it more complex to fit generalized models. Although statistical methods using supervised learning prove efficient and effective in some cases, sometimes you need a different approach. These situations are when rule-based models with Natural Language Processing capabilities can add significant value	type i error, sample, proc freq, confusion matrix, data cleaning	source	15
148	Extensive Modification of SAS Program Output Routed to ODS Using Proc Document	Roger D. Muller	The `DOCUMENT` procedure is a little known procedure that can save you vast amounts of time and effort when managing the output of your SAS programming efforts. This procedure is deeply associated with the mechanism by which SAS controls output in the Output Delivery System (`ODS`). Have you ever wished you didn't have to modify and rerun the report-generating program every time there was some tweak in the desired report? `PROC DOCUMENT` enables you to store one version of the report as an `ODS` Document Object and then call it out in many different output forms, such as PDF, HTML, listing, RTF, and so on, without rerunning the code. Have you ever wished you could extract those pages of the output that apply to certain BY variables such as State, StudentName, or CarModel? With `PROC DOCUMENT`, you have where capabilities to extract these	proc step, output delivery system	source pdf	11
149	Factorization Machines: A New Tool for Sparse Data	Jorge Silva, Ray Wright	Factorization machines are a new type of model that is well suited to very high-cardinality, sparsely observed transactional data. This paper presents the new `FACTMAC` procedure, which implements factorization machines in SAS Visual Data Mining and Machine Learning. This powerful and flexible model can be thought of as a low-rank approximation of a matrix or a tensor, and it can be efficiently estimated when most of the elements of that matrix or tensor are unknown. Thanks to a highly parallel stochastic gradient descent optimization solver, `PROC FACTMAC` can quickly handle data sets that contain tens of millions of rows. The paper includes examples that show you how to use `PROC FACTMAC` to recommend movies to users based on tens of millions of past ratings, predict whether fine food will be highly rated by connoisseurs, restore heavily damaged high-resolution images, and discover shot styles that best fit individual basketball players	proc step, machine learning, proc princomp	source	14
150	Fast implementation of State Transition Models	Shannon Clark	Implementation of state transition models for loan-level portfolio evaluation was an arduous task until now. Several features have been added to the SAS High-Performance Risk engine that greatly enhance the ability of users to implement and execute these complex, loan-level models. These new features include model methods, model groups, and transition matrix functions. These features eliminate unnecessary and redundant calculations; enable the user to seamlessly interconnect systems of models; and automatically handle the bulk of the process logic in model implementation that users would otherwise need to code themselves. These added features reduce both the time and effort needed to set up model implementation processes, as well as significantly reduce model run time. This paper describes these new features in detail. In addition, we show how these powerful models can be easily implemented by using SAS Model Implementation Platform with SAS 9.4	transition matrix, sample	source	4
151	Fighting Crime in Real Time with SAS Visual Scenario Designer	John Shipway	Credit card fraud. Loan fraud. Online banking fraud. Money laundering. Terrorism financing. Identity theft. The strains that modern criminals are placing on financial and government institutions demand new approaches to detecting and fighting crime. Traditional methods of analyzing large data sets on a periodic, batch basis are no longer sufficient. SAS Event Stream Processing provides a framework and run-time architecture for building and deploying analytical models that run continuously on streams of incoming data, which can come from virtually any source: message queues, databases, files, TCP/IP sockets, and so on. SAS Visual Scenario Designer is a powerful tool for developing, testing, and deploying aggregations, models, and rule sets that run in the SAS Event Stream Processing Engine	stream processing, real-time processing, unique key	source	11
152	Find Daylight Saving Time and Forget It	Chao-Ying Hsieh	Finding daylight saving time is a common task for manipulating time series data. The date of daylight saving time changes every year. If SAS programmers depend on manually entering the value of daylight saving time in their program; the maintenance of the program becomes tedious. Using SAS function can easily find daylight saving time. This paper discusses several ways to capture and utilize daylight saving time	time series data, macro variable, time series analysis	source	4
153	Finding Out about Formats and Their Attributes	Richard D. Langston	This paper discusses format enumeration (via the DICTIONARY.FORMATS view) and the new FMTINFO function that gives information on a format, such as whether it is a date or currency format	proc format, proc step, proc sql	source	4
154	Fitting a Cumulative Logistic Regression	Shana Kelly	Cumulative logistic regression models are used to predict an ordinal response, and have the assumption of proportional odds. Proportional odds means that the coefficients for each predictor category must be consistent, or have parallel slopes, across all levels of the response. The paper uses a sample dataset to demonstrate how to test the proportional odds assumption, and use the UNEQUALSLOPES option when the assumption is violated. A cumulative logistic model is built, and then the performance of the cumulative logistic model on a test set is compared to the performance of a generalized multinomial model. This shows the utility and necessity of the UNEQUALSLOPES option when building a cumulative logistic model. The procedures shown are produced using SAS Enterprise Guide 7.1	logistic distribution, logistic regression, predictor variable, regression analysis, proc logistic, proc step	source	5
155	Fitting a Flexible Model for Longitudinal Count Data Using the NLMIXED Procedure	Darcy Morris	Longitudinal count data arise when a subject's outcomes are measured repeatedly over time. Repeated measures count data have an inherent within-subject correlation that is commonly modeled with random effects in the standard Poisson regression. A Poisson regression model with random effects is easily fit in SAS using existing options in the `NLMIXED` procedure. This model allows for over-dispersion via the nature of the repeated measures; however, departures from equi-dispersion can also exist due to the underlying count process mechanism. We present an extension of the cross-sectional Conway-Maxwell- Poisson (COM-Poisson) regression model established by Sellers and Shmueli (2010) - a generalized regression model for count data in light of inherent data dispersion - to incorporate random effects for analysis of longitudinal count data. We detail how to fit the COM-Poisson longitudinal model via a user- defined log-likelihood function in `PROC NLMIXED`	poisson distribution, random intercept, poisson regression, proc mixed, regression analysis, proc step, likelihood function, binomial distribution, correlation, negative binomial regression, proc nlmixed, repeated measures, maximum likelihood estimation	source	6
156	Fitting Complex Statistical Models with NLMIXED and MCMC Procedures	Robin High		proc step, estimate, binomial distribution, likelihood function, proc mixed, macro variable, poisson distribution, probability distribution, binary variable, response variable, main effect, proc glimmix, nominal variable, fixed effects, normal distribution, array, sas log, anova, logistic distribution, censoring, predictor variable	source	14
157	Five Things You Didn't Know You Could Do with SAS Visual Analytics	Varsha Chawla, Renato Luppi	Do you ever wonder how to create a report with weighted averages, or one that displays the last day of the month by default? Do you want to take advantage of the one-click, relative-time calculations available in SAS Visual Analytics, or learn a few other creative ways to enhance your report? If your answer is yes, then this paper is for you. We not only teach you some new tricks, but the techniques covered here will also help you expand the way you think about SAS Visual Analytics the next time you are challenged to create a report	weighted mean, numeric functions	source	16
158	Five Things You Should Know about Quantile Regression	Robert N. Rodriguez, Yonggang Yao	The increasing complexity of data in research and business analytics requires versatile, robust, and scalable methods of building explanatory and predictive statistical models. Quantile regression meets these requirements by fitting conditional quantiles of the response with a general linear model that assumes no parametric form for the conditional distribution of the response; it gives you information that you would not obtain directly from standard regression methods. Quantile regression yields valuable insights in applications such as risk management, where answers to important questions lie in modeling the tails of the conditional distribution. Furthermore, quantile regression is capable of modeling the entire conditional distribution; this is essential for applications such as ranking the performance of students on standardized exams	regression analysis, proc quantreg, predictor variable, data cleaning	source	19
159	Five Ways to Create Macro Variables: A Short Introduction to the Macro Language	Arthur L. Carpenter	The macro language is both powerful and flexible. With this power, however comes complexity, and this complexity often makes the language more difficult to learn and use. Fortunately one of the key elements of the macro language is its use of macro variables, and these are easy to learn and easy to use. Macro variables can be created using a number of different techniques and statements. However the five most commonly methods are not only the most useful, but also among the easiest to master. Since macro variables are used in so many ways within the macro language, learning how they are created can also serve as an excellent introduction to the language itself. These methods include: `%LET` statement macro parameters (named and positional) iterative %DO statement using the INTO in `PROC SQL` * using the CALL SYMPUTX routine	macro variable, macro facility, macro put, macro loop, proc sql, put statement, do loop, macro call, proc step, missing value, macro parameter, macro definition, %macro parameters, automatic macro variable, symbol table, keyword parameter, select, call symput	source zip	12
160	Flow Riders Driving Below Traffic Flow: A Risk Analysis	Zhongjie Cai, Dixin Shen, Ken Chau		chi-square test, logistic distribution	source	12
161	Formats Are Your Friends	Anita Measey, Lei Sun	SAS formats can be used for more than just making your data look nice. Formats can be used as in memory look up tables as well as help you to create data driven code. This paper will show you, with code examples, how to build a format from a dataset; write a format out as a dataset; and how to use formats to help to make programs data driven	character variable, numeric variable, char.	source	9
162	Framework for Strategic Analysis in Higher Education	Glenn James	Higher education institutions have a plethora of analytical needs. However, the irregular and inconsistent practices in connecting those needs with appropriate analytical delivery systems has resulted in a patchwork that sometimes overlaps unnecessarily and sometimes exposes unaddressed gaps. The purpose of this paper is to examine a framework of components for addressing institutional analytical needs while leveraging existing institutional strengths, in order to maximize analytical goal attainment most effectively and efficiently. The core of this paper is a focused review of components for attaining greater analytical strength and goal attainment in the institution		source	6
163	Frequently Asked Questions about SAS Environment Manager on SAS 9.4	Zhiyong Li, Sam Chen, Fred Li	SAS Environment Manager is the predominant tool for managing your SAS environment. Its popularity is increasing quickly as evidenced by the increased technical support requests from our customers. This paper identifies the most frequently asked questions from customers by reviewing the support work completed by the development and technical support teams over the last few y ears. The questions range across topics such as web interface usage; alerts, controls, and resource discovery; Agent issues; and security issues	sas management console, error message	source	21
164	From Coder to Collaborator: Tips and Tricks for Being a Better Analyst	Emma Warrillow	Are you a marketing analyst who speaks SAS? Congratulations, you are in high demand! Or are you? Marketing analysts with programming skills are critical today. The ability to extract large volumes of data, massage it into a manageable format, and display it simply are necessary skills in the world of big data. However, programming skills are not nearly enough in today's world. In fact, some marketing managers are putting less and less weight on them and are focusing more on the softer skills that they require. The tips and tricks in this paper will help ensure that you are not left out. We provide practical advice on how to go from being good at coding to becoming a true collaborator with marketing-taking your marketing analytics to the next level. In 2016, Emma Warrillow's presentation at SAS Global Forum was very well received; this paper begins where 2016 leaves off and shares some further tips to help the analyst progress	business intelligence, big data	source	8
165	From Event Queues to Analytics	Bronwen Fairbairn	In the quest for valuable analytics, access to business data through message queues provides near real- time access to the entire data life cycle. This in turn enables our analytical models to perform accurately. What does the item a user temporarily put in the shopping basket indicate, and what can be done to motivate the user? How do you recover the user who has now unsubscribed, given that the user had previously unsubscribed and re-subscribed quickly? User behavior can be captured completely and efficiently using a message queue, which causes minimal load on production systems and allows for distributed environments. There are some technical issues encountered when attempting to populate a data warehouse using events from a message queue. This presentation outlines a solution to the following issues: the message queue connection, how to ensure that messages aren't lost in transit, and how to efficiently process messages with SAS software; message definition and metadata, how to react to changes in message structure; data architecture, which data architecture is appropriate for storing message data and other business data; late arrival of messages, how late arriving data can be loaded into slowly changing dimensions; and analytical processing, how transactional message data can be reformatted for analytical modelling. Ultimately, using an Enterprise Service Bus to populate a data warehouse can require less development than accessing source databases; however a robust architecture is required	data warehouse, etl	source	13
166	From Researcher to Programmer: Five SAS Tips I Wished I Knew Then	Crystal Carel	Presentation		source	6
167	From Source to Target: Hadoop Capabilities of SAS Data Integration Studio	Darryl Yewchin, Todd Foreman	This paper demonstrates how to use the capabilities of SAS Data Integration Studio to extract, load, and transform your data within a Hadoop environment. The transformations that can be used in each layer of the ELT process are illustrated using a sample use case, and the functionality of each is described. The use case steps through the process from source to target	proc sql, data merging, sample, proc step, alter table, big data, hive	source	18
168	From Stocks to Flows: Using SAS HASH objects for FIFO, LIFO, and other FO's	Mark Keintz	Tracking gains or losses from the purchase and sale of diverse equity holdings depends in part on whether stocks sold are assumed to be from earliest lots acquired (a FIFO queue) or the latest lots acquired (LIFO). Other inventory tracking applications have a similar need for application of either FIFO or LIFO rules. This presentation shows how a collection of simple ordered hash objects, in combination with a hash-of- hashes is a made-to-order technique for easy data-step implementation of FIFO, LIFO, and other less- likely rules, like HIFO (highest price first out) and LOFO (lowest price)	hash table, hash iterator	source	13
169	Fuzzy Matching and Predictive Models for Acquisition of New Customers	Daniel Marinescu	Acquisition of new customers is fundamental to the success of every business. Data science methods can greatly improve the effectiveness of acquiring prospective customers and can contribute to the profitability of business operations. In a consumer setting, a predictive model may be used to acquire a new donor for a charity or a new customer for a firm, for example by switching to another competitor. In the B2B domain, a predictive model may target business prospects as individual firms listed by an external database. A typical acquisition model can be defined using a binary response where the values categorize a firm's customers and non-customers, for which it is then necessary to identify which of the prospects are actually the customers of the firm. The methods of fuzzy logic, for example, based on the similarity distance between the strings, may help in matching customers' names and addresses with the overall universe of prospects	confidence level, sample, response variable, data mining	source	12
170	Geospatial Analysis: Linear, Nonlinear, or Both?	Kelsey Osterloo, Angela Wu	An important component of insurance pricing is the insured location and the associated riskiness of that location. Recently, we have experienced a large increase in the availability of external risk classification variables and associated risk factors by geospatial location. As additional geospatial data becomes available, it is prudent for insurers to take advantage of the new information to better match price to risk. Generalized additive models using penalized likelihood (GAMPL) have been explored as a way to incorporate new location-based information. This type of model can leverage the new geospatial information and incorporate it with traditional (non-location based) insurance rating variables in a regression-based model for rating. Our discussion demonstrates the use of the `LOESS` procedure as well as the `GAMPL` procedure in a combined solution. Both procedures are available in `SAS/STAT` software	regression analysis, proc gam, loess, pure premium, data cleaning, degrees of freedom, validation set, proc step, proc loess, insurer, response variable, champion model, risk classification	source	14
171	Get the Tangency Portfolio Using SAS/IML	Keshan Xia, Peter W. Eberhardt, Matthew J. Kastin	Mean-Variance Model (Modern portfolio theory) maybe the most famous model in financial field. It assesses a portfolio which's the expected return (mean) is maximized under a given risk (variance). It comes from assumption that investor want as high as return while as low as risk as he could when he invested a couple of assets (a portfolio is the collection of many assets).This model could give us the many optimal portfolio (efficient portfolio frontier) when we know every asset's expect return and their covariance matrix. The tangency portfolio is one of such kind of optimal portfolio after we included a riskless rate (government long-term bond rate), which means it has the maximal expect return (mean) and the minimal risk (variance) among all the optimal portfolios. This paper is trying to simulate some dummy data and get this tangency portfolio by `IML` code	covariance, standard deviation, sysrc, proc step	source pdf	6
172	Getting Classy: A SAS Macro for CLASS Statement Automation	Erica Goodrich, Daniel Sturgeon, Kathryn Schurr		proc step, proc sql, proc contents, create table, numeric variable, macro variable, interval variable, nominal variable, macro loop, macro put, %if-%then-%else	source pdf	12
173	Getting Started with Designing and Implementing a SAS 9.4 Metadata and File System Security Design	Philip Hopkins	SAS has been installed at your organization - now what? How do you approach configuring groups, roles, folders, and permissions in your environment? This presentation is built on best practices used within the U.S. SAS Consulting division and aims to equip new and seasoned SAS administrators with the knowledge and tools necessary to design and implement a SAS metadata and file system security model. We start by covering the basic building blocks of the SAS Intelligence Platform authorization framework. We discuss the SAS Metadata authorization facility, and highlight the differences between groups and roles, permissions and capabilities, access control entries and access control templates, and what content can be stored within metadata folders versus in file system folders. We review the various authorization layers in a SAS deployment that must work together to create a secure environment, including the metadata layer, the file system, and the data layer	access control, sas management console, sas enterprise guide, sas studio, data mining, sas administrator	source	20
174	Getting Started with SAS Prompts	Brian K. Varney	Allowing SAS users to leverage SAS prompts when running programs is a very powerful tool. Using SAS prompts makes it easier for SAS users to submit parameter driven programs and for developers to create robust, data driven programs. This presentation will demonstrate how to create SAS prompts from SAS Enterprise Guide and roll them out to users so they can take advantage of them from SAS programs and SAS Stored Processes	macro variable, sas enterprise guide, macro definition	source	13
175	Getting the Latest and Greatest from SAS 9.4: Best Practices for Upgrades and Migrations	Jamie Williams	SAS 9.4 provides three ways to upgrade: upgrade in place, automated migration with the SAS Migration Utility, and partial promotion. This paper focuses primarily on the different techniques and best practices for each. We also discuss the pros and cons of using the SAS Migration Utility and what is required for migrating users' content like projects, data, and code	sas management console, sas enterprise guide, data mining, sas enterprise guide project, proc step, access control, proc cport, dataset, sample	source	13
176	GMM Logistic Regression with Time-Dependent Covariates and Feedback Processes in SAS	Jeffrey Wilson	The analysis of longitudinal data requires a model which correctly accounts for both the inherent correlation amongst the responses as a result of the repeated measurements, as well as the feedback between the responses and predictors at different time points. Lalonde, Wilson and Yin (2013) developed an approach based on generalized methods of moments (GMM) for identifying and using valid moment conditions to account for time dependent covariate in longitudinal data with binary outcomes. However, the model developed using this approach does not provide information regarding the specific relationships that exist across time points. We present a SASTM macro which extends the work of Lalonde, Wilson and Yin by utilizing valid moment conditions to estimate and evaluate the relationships between the response and predictors at different time periods. The performance of this method is compared to previously established results	correlation, regression analysis, longitudinal data, estimate, logistic regression, macro call, logistic distribution, proc step	source	7
177	Go Ahead and _BREAK_-down: Advanced COMPUTE Block Examples	Cynthia L. Zender	When you look at examples of the `REPORT` procedure, you see code that tests _BREAK_ and _RBREAK_, but you wonder what's the breakdown of the COMPUTE block? And, sometimes, you need more than one break line on a report, or you need a customized or adjusted number at the break. Everything in `PROC REPORT` that is advanced seems to involve a COMPUTE block. This paper provides examples of advanced `PROC REPORT` output that uses _BREAK_ and _RBREAK_ to customize the extra break lines that you can request with `PROC REPORT`. Examples include how to get custom percentages with `PROC REPORT`, how to get multiple break lines at the bottom of the report, how to customize break lines, and how to customize LINE statement output. This presentation is aimed at the intermediate to advanced report writer who knows some about `PROC REPORT`, but wants to get the breakdown of how to do more with `PROC REPORT` and the COMPUTE block	proc report, proc step, by group processing, missing value, character variable, length statement, proc print, proc tabulate, where statement, response variable, put statement	source zip	30
178	Going Green With Your SAS Applications	Michael A. Raithel	This paper shows how you can reduce the computing footprint of your SAS applications without compromising your end- products. The paper presents the 15 axioms of going green with your SAS applications. The axioms are proven, real- world techniques for reducing the computer resources used by your SAS programs. When you follow these axioms, your programs will run faster, use less network bandwidth, use fewer desktop or shared server computing resources, and create more compact SAS data sets	dataset, modify statement, proc step, proc means, proc sql, character variable, numeric variable, set statement, flat file, in-database processing, options statement, raw data, sas programming, length statement, cpu time, libname statement	source	17
179	Good Programming Practice When Working Across SAS on a PC and SAS on UNIX	James Zhao	This paper examines good programming practices to address common issues across PC SAS and UNIX SAS. They include: 1, Define forward or back slash for file paths in `LIBNAME`, `FILENAME`, and `%INCLUDE` statements consistently or dynamically; 2, Avoid the use of mixed case for file path and dataset names; 3, Avoid the use of X commands in SAS code to execute statements on the operating system which only works on Windows but not on UNIX; 4 . Use appropriate SAS rounding function for numeric variables to avoid different results when dealing with 64 bit operating systems and 32 bit systems; 5, Use SAS Enterprise Guide to access and run PC SAS programs in UNIX efficiently	sas enterprise guide, numeric variable, numeric functions	source	5
180	Guidelines for Protecting Your Computer, Network, and Data from Malware Threats	Ryan Paul Lafler, Kirk Paul Lafler	Since many SAS Users either work for or own companies that house big data, the threat that malicious software poses becomes even more extreme. Malicious software, often abbreviated as "malware", includes many different classifications, ways of infection, and methods of attack. This e-Poster highlights the types of malware, detection strategies, removal methods, and provides guidelines to secure essential assets and prevent future malware breaches	big data, memory usage	source pdf	10
181	Hands-On Graph Template Language (GTL): Part A	Kriss Harris	Would you like to be more confident in producing graphs and figures? Do you understand the differences between the OVERLAY, GRIDDED, LATTICE, DATAPANEL, and DATALATTICE layouts? Finally, would you like to learn the fundamental Graph Template Language methods in a relaxed environment that fosters questions? Great-this topic is for you! In this hands-on workshop, you are guided through the fundamental aspects of the GTL procedure, and you can try fun and challenging SAS graphics exercises to enable you to more easily retain what you have learned	box plot, graph template language, histogram	source zip	13
182	Hands-on Graph Template Language (GTL): Part B	Kriss Harris	Do you need to add annotations to your graphs? Do you need to specify your own colors on the graph? Would you like to add Unicode characters to your graph, or would you like to create templates that can also be used by non-programmers to produce the required figures? Great, then this topic is for you! In this hands-on workshop, you are guided through the more advanced features of the GTL procedure. There are also fun and challenging SAS graphics exercises to enable you to more easily retain what you have learned	box plot, proc means, class variable, graph template language, ifn, proc step	source zip	15
183	Hash Objects: When LAGging Behind Just Doesn't Work	Andrea Wainwright-Zimmerman	When modeling time series data, we often use a LAG of the dependent variable. The LAG function works great for this, until you try to predict out into the future and need the model's predicted value from one record as an independent value for a future record. This paper examines exactly how the LAG function works, and explains why it doesn't in this case. It also explains how to create a HASH object that will accomplish a LAG of any value, how to load the initial data, how to add predicted values to the HASH object, and how to extract those values when needed as an independent variable for future observations	hash table, lag function, response variable, time series analysis, proc step, proc sql	source	5
184	Haven't I Seen You Before? An Application of DATA Step Hash for Efficient Complex Event Associations	John Schmitz	Data processing can sometimes require complex logic to match and rank record associations across events. This paper presents an efficient solution to generating these complex associations using the `DATA STEP` and data hash objects. The solution applies to multiple business needs including subsequent purchases, repayment of loan advance, or hospital readmits. The logic demonstrates how to construct a hash process to identify a qualifying initial event and append linking information with various rank and analysis factors, through the example of a specific use case of the process	hash table, conditional do loop, set statement, if-then-else	source zip	6
185	Health-Care Data Sharing and Innovative Analytic Development in Distributed Data Networks	Jennifer R. Popovic	Secondary use of administrative claims data, electronic health and medical record (EHR/EMR) data, registry data, and other sources within the health data ecosystem provide rich opportunity and potential to study topics ranging from public health surveillance to comparative effectiveness research to identifying patients who may be eligible for inclusion in a specific study or for a specific intervention. Data sourced from individual sites can be limited in their scope, coverage, and statistical power. Sharing and pooling data from multiple sites and sources, however, present administrative, governance, analytic, and patient- privacy challenges. Distributed data networks represent a paradigm shift in health-care data sharing. They have evolved at a critical time when big data and patient privacy often have competing priorities. A distributed data network is one for which no central repository of data exists. Data reside behind the firewalls of each data-contributing partner in a network	data cleaning, adverse events	source	9
186	Heat Maps: Graphical Displays for Big Data and Small Tables	Warren F. Kuhfeld	Heat maps use colors to communicate numeric data by varying the underlying values that represent red, green, and blue (RGB) as a linear function of the data. You can use heat maps to display spatial data, plot big data sets, and enhance tables. You can use colors on the spectrum from blue to red to show population density in a US map. In fields such as epidemiology and sociology, colors and maps are used to show spatial data, such as how rates of disease or crime vary with location. With big data sets, patterns that you would hope to see in scatter plots are hidden in dense clouds of points. In contrast, patterns in heat maps are clear, because colors are used to display the frequency of observations in each cell of the graph. Heat maps also make tables easier to interpret. For example, when displaying a correlation matrix, you can vary the background color from white to red to correspond to the absolute correlation range from 0 to 1	heatmap, proc step, correlation, proc sgplot, sg annotation	source	20
187	Help! My New Director of Analytics Wants to Get Rid of SAS! What Can I Do?	Melodie Rush	How would you answer this question? Most of us struggle to articulate the value of the tools, techniques, and teams used to harness analytics. How do you help the new director understand the value of SAS to you, your job, and your organization? In this interactive session, you will discover the components that make up total cost of ownership (TCO) as they apply to the analytics lifecycle. What should you consider when you evaluate total cost of ownership and why should you measure it? How can you help your management team understand the value that SAS provides? *When appropriate, replace with Director of IT, Chief Analytics Officer, Chief Information Officer, or similar title	market basket analysis, sample, score code	source	17
188	How Members Really Feel About Your Credit Card	Anirban Chakraborty, Surya Bhaskar Ayyalasomayajula	Immediately after a new credit card product is launched and in the wallets of cardholders, sentiment begins to build. Positive and negative experiences of current customers posted online generate impressions among prospective cardholders in the form of technological word of mouth. Credit card issuing companies can use sentiment analysis to understand how its product is being received by the consumers and by taking suitable measure can propel the card's market success. Wit h the help of text mining and sentiment analysis using SAS Enterprise Miner and SAS Sentiment Analysis Studio we are trying to answer: which aspects of a credit card garnered the most favor, and conversely, which generated negative impressions among the consumers. Credit Karma is a free credit and financial management platform for US consumers available on the web and major mobile platforms, it provides free weekly updated credit scores and credit reports from national credit bureaus TransUnion and Equifax	credit card, credit score, proc freq, data mining, parsing, training set, dataset	source	25
189	How SAS Customers Are Using Hadoop: Year in Review	Howard Plemmons Jr., Mauro Cazzari	Another year implementing, validating, securing, optimizing, migrating, and adopting the Hadoop platform. What have been the top 10 accomplishments with Hadoop seen over the last year? We also review issues, concerns, and resolutions from the past year as well. We discuss where implementations are and some best practices for moving forward with Hadoop and SAS releases	dataset, hadoop	source	11
190	How to Use SAS to Filter Stock for Trade	Brian Shen	Investors usually trade stocks or exchange-traded funds (ETFs) based on a methodology, such as a theory, a model or a specific chart pattern1,2. There are more than 10,000+ stocks/ETFs listed on the stock market. Picking the right one at the right moment based on a methodology from so many candidates is usually a big challenge. This paper presents the methodology on how to accomplish such task based on the CANSLIM3 theorem and momentum trading (MT) theorem4. With a high-speed computer (64G-128G memory), one scan and plot from 8,000 stocks might take 2-4 minutes if your programming logic is efficient. We have heard of Cup-n-Handle shape (CnH), Double/Multiple bottoms (DB/MB), support/resist lines (SRL), market direction (MD), fundamental analyses (FA) and technical analyses (TA). Those were all covered in CANSLIM theorem. MT is a trading theorem based on stock moving direction or momentum. Both theorems are easy to learn but difficult to apply without an appropriate tool. The brokers' application system usually could not provide such filtering due to its complicity. For example, for CnH, where is the handle? For the MB, where is the last bottom you should trade? Now, the challenge task could be accomplished through SAS. This paper presents the methods on how to apply those logics and graphically present them though SAS5	outlier, stop loss	source	9
191	How's Your Sport's ESP? Using SAS Event Stream Processing with SAS Visual Analytics to Analyze Sports Data	John Davis	In today's instant information society, we want to know the most up-to-date information about everything, including what is happening with our favorite sports teams. In this paper, we explore some of the readily available sources of live sports data and look at how SAS technologies, including SAS Event Stream Processing and SAS Visual Analytics, can be used to collect, store, process, and analyze the streamed data. A bibliography of sports data websites that are used in this paper is included, with emphasis on the free sources	stream processing, real-time processing, dataset	source	10
192	I Am Multilingual: A Comparison of the Python, Java, Lua, and REST Interfaces to SAS Viya	Xiangxiang Meng, Kevin D. Smith	The openness of SAS Viya, the new cloud analytic platform centered around SAS Cloud Analytic Services (CAS), emphasizes a unified experience for data scientists. You can now execute the analytic capabilities of SAS from different programming languages including Python, Java, and Lua, as well as use a RESTful endpoint to execute CAS actions directly. This paper provides an introduction to these programming language interfaces. For each language, we illustrate how the API is surfaced from the CAS server, the types of data that you can upload to a CAS server, and the result tables that are returned. This paper also provides a comprehensive comparison of using these programming languages to build a common analytical process, including connecting to a CAS server; exploring, manipulating, and visualizing data; and building statistical and machine learning models	cas action, cas, regression analysis, logistic regression, logistic distribution	source	16
193	I Spy PII: Detect, Protect, and Monitor Personally Identifiable Information with SAS Federation Server	Cecily Hoffritz	The clock is ticking! Is your company ready for May 25, 2018 when the General Data Protection Regulation that affects data privacy laws across Europe comes into force? If companies fail to comply, they incur very large fines and might lose customer trust if sensitive information is compromised. With data streaming in from multiple channels in different formats, sizes, and wavering quality, it is increasingly difficult to keep track of personal data so that you can risk assess it and protect it. SAS Data Management helps companies on their journey toward governance and compliance involving tasks such as personal data identification, protection, and monitoring. This paper focuses on using SAS Federation Server and SAS Data Management Studio from the SAS Data Management suite to surface and manage that hard-to find-personal data	proc sql, sas enterprise guide, dataset, sample, where statement, sas base, data science, data cleaning, select, sql server, libname statement	source	33
194	I'm Normal, You're Normal, but Is Your Weather Normal?	Kyle Wood, Jason Wilson, Bradley Lawson, Rain Xie	The traditional view is that a utility's long term forecast must have a standard against which it is judged. Weather normalization is one of the industry standard practices that utilities use to assess the efficacy of a forecasting solution. While recent advances in probabilistic load forecasting techniques are proving to be a metho dology that brings many benefits to a forecast, many utilities still require the benchmarking process to determine the accuracy of their long term forecasts. Due to climatological volatili ty and the potentially large annual variances in temperature, humidity and other relevant weather variables, most utilities create normalized weather profiles through various processes in order to estimate what is traditionally called a weather normalized load profile. However, new research shows that due to the n onlinear response of electric demand to weather variations, a simple normal weather profile in many cases may not equate to a normal load	normalization, predictor variable, regression analysis	source	5
195	I've Got to Hand It to You; Portable Programming Techniques	Arthur L. Carpenter, Mary F. O. Rosenbloom	As technology expands, we have the need to create programs that can be handed off - to clients, to regulatory agencies, to parent companies, or to other projects, and handed off with little or no modification by the recipient. Minimizing modification by the recipient often requires the program itself to self-modify. To some extent the program must be aware of its own operating environment and what it needs to do to adapt to it. There are a great many tools available to the SAS programmer, which will allow the program to self-adjust to its own surroundings. These include location-detection routines, batch files based on folder contents, the ability to detect the version and location of SAS, programs that discern and adjust to the current operating system and the corresponding folder structure, the use of automatic and user defined environmental variables, and macro functions that use and modify system information. Need to create a portable program? We can hand you the tools	macro variable, options statement, sas log, automatic macro variable, macro put, macro function, macro definition, autocall macro, macro flow control, libname statement, put statement, macro call, macro facility	source	21
196	Identifying Abnormal Equipment Behavior and Filtering Data near the Edge for IoT Applications	Ryan Gillespie, Saurabh Gupta	This paper describes the use of a machine learning technique for anomaly detection and the SAS Event Stream Processing engine to analyze streaming sensor data and determine when performance of a turbofan engine deviates from normal operating conditions. Turbofan engines are the most popular type of propulsion engines used by modern airliners due to their high thrust and good fuel efficiency (National Aeronautics and Space Administration 2015). For this paper, we intend to show how sensor readings from the engines can be used to detect asset degradation and help with preventative maintenance applications	edge computing, outlier, sample, stream processing, real-time processing, iot, machine learning, time series analysis, score code, training set	source	5
197	If You Have to Process Difficult Characters: UTF-8 Encoding and SAS	Frank Poppe	Many SAS environments are set up for single-byte character sets (SBCS). But many organizations now have to process names of people and companies with characters outside that set, like in "Îf ÿøù have". You can solve this problem by changing the configuration to the UTF-8 encoding, which is a multi-byte character set (MBCS). But the commonly used text manipulating functions like SUBSTR, INDEX, FIND, and so on, act on bytes, and should not be used anymore. SAS has provided new functions to replace these (K-functions). Also, character fi elds have to be enlarged to make room for multi-byte characters. This paper describes the problems and gives guidelines for a strategy to change. It also presents code to analyze existing code for functions that might cause problems. For those interested, a short historic background and a description of UTF-8 encoding is also provided	input statement, character variable, character functions, sas data integration studio, macro function, substr function, macro variable, proc sql	source	12
198	Impact of Outbound SMS notifications on Inbound Interactive Voice Response Call Volume	Sumit Sukhwani, Krutharth Peravalli, Dmitriy Khots	In this technology driven era, multi-channel communication has become a pivotal part of an effective customer care strategy for companies. Old ways of delivering customer service are no longer adequate. To survive tough competitive market and retain current customer base, companies are spending massively to serve customers in the manner they wish to be served. West Corporation helps their clients in designing a strategy that would provide their customers a connected inbound and outbound communication experience. The purpose of this paper is to illustrate how Data Science team at WEST has measured effect of Outbound SMS notifications in I) Reducing Inbound Interactive Voice Response (IVR) call volume II) Increasing automation III) Improving customer satisfaction, for a leading Telecom services company. As part of seamless experience customers have an option of receiving SMS notifications at several stages while traversing inside IVR	data science, proc sql, proc step	source	7
199	Implementing Capacity Management Policies on a SAS LASR Analytic Server Platform: Can You Afford Not To?	Paul W. Johnson	Capacity management is concerned with managing, controlling and optimizing the hardware resources on a technology platform. Its primary goal is to ensure that IT resources are right-sized to meet current and future business requirements cost effectively. In other words, keeping those hardware vendors at bay! SAS LASR servers with its dependence on In-memory resources necessitate a revisit to the traditional IT server capacity management practices. A major UK based financial services institution operates a multi-tenanted Enterprise SAS platform. The 'tenants' share platform resources and as such, require quotas enforced with system limits and costs for their resource utilization, aligned to the business outcomes and agreed SLA's. This paper discusses the Implementation of System, Operational and Development polices applicable in a multi-tenanted SAS platform, in order to optimise their investment in the SASLASR platform and be in control as to when capacity uplifts are required	capacity planning, data mart	source	11
200	Implementing Role-Based Access Control and DSoD Authorization Schema on SAS	Perttu Muurimaki	Traditionally role based access control is implemented as group memberships. Access to SAS datasets or metadata libraries requires membership in the group that "owns" the resources. From the point of view of a SAS process these authorizations are additive. If a user is member in two distinct groups, user's SAS processes have access to the data resources of both groups simultaneously. This happens every time the user runs a SAS process - even when the code in question is meant to be used with only one group's resources. As a consequence, having master data source defining dataflows between groups becomes futile as any SAS process of the user can bypass said definitions. Additionally, as it is not possible to reduce user's authorizations to match those of only the relevant group, it becomes challenging to determine whether other members of the group have sufficient authorization	master data management, access control, data standard	source	15
201	Import and Export XML Documents with SAS	Fei Wang	XML data files are becoming increasingly popular for transporting data from different operating systems. In the pharmaceutical industry, the Food and Drug Administration (FDA) requires pharmaceutical companies to submit certain types of data in XML format. This paper provides insights into XML data files and introduces different methods of importing/exporting XML data files with SAS, including: using the XML `LIBNAME` engine to translate between the XML markup and the SAS format; creating an XMLMap file and utilizing XML `LIBNAME` engine to read in XML data files and create SAS datasets. An example of importing the EDC (Electronic Data Capture) data of CDISC ODM format into SAS by implementing the above methods is used to illustrate the process	dataset, libname statement, format statement, proc step, proc contents, proc print, sas library, cdisc	source	15
202	Improving Efficiency in SAS Enterprise Guide: Parallel Processing and Other Hidden Gems	Steven J. First, Benjamin First	In the past 15 years, SAS Enterprise Guide (EG) has developed into the go-to application to access the power of SAS. With each new release, SAS continues to add functionality that makes the SAS user's life easier. We will take a closer look at some of the built-in features within SAS Enterprise Guide and how they can make your life easier. One of the most exciting and powerful features we explore is allowing parallel execution on the same server, giving the ability to run multiple SAS processes at the same time regardless of whether you have a SAS Grid Computing environment or not. Other topics covered include conditional processing within SAS Enterprise Guide, how to securely store database login and password information, setting up autoexec files in SAS Enterprise Guide, exploiting process flows, the new EG `DATA STEP` debugger, and much more	sas enterprise guide, macro variable, data step, sas log, grid computing, out=, parallel processing, proc sql	source	21
203	Improving the Evaluation of Higher Education: Understanding the Myths, Methods, and Metrics	Sean W. Mulvenon	A growing need in higher education is the more effective use of analytics when evaluating the success of a postsecondary institution. The metrics currently used are simplistic measures of graduation rates, publications, and external funding. These measures offer a limited view of the effectiveness of postsecondary institutions. This paper provides a global perspective of the academic progress of students and the business of higher education. It presents innovative metrics using SAS and data architecture modeling that may be more effective in evaluating postsecondary-institutional effectiveness	index, sas enterprise guide	source	12
204	Increasing Revenue in Only Four Months with SAS Real-Time Decision Manager	Alvaro Velasquez	This paper describes an effective real-time contextual marketing system based on cases of use which was successfully implemented in a Chilean private communication company. Implementing real-time use cases for marketing purposes is becoming a major challenge within communication companies in Chile due to a stronger competition which generates an increase of churns, higher operational costs, among others. All of these may enormously affect revenue and profit. Just sending automated outbound campaigns periodically may be an inefficient task because it does not cover needs from customers in the right moment. Although, a set of predictive machine learning models may help to improve response rates of these outbound campaigns, it is not enough to be more proactive in this business. Our real-time system for contextual marketing uses the two pieces of SAS software called SAS Event Stream Processing (ESP) and SAS Real Time Decision Manager (RTDM) which are connected in cascade	big data, web service	source	13
205	Increasing Your Productivity with New Features in SAS Enterprise Guide	Casey Smith	SAS Enterprise Guide continues to add easy-to-use features that enable you to work more efficiently. For example, you can now debug your `DATA STEP` code with a `DATA STEP` debugger tool; upload data to SAS Viya with a point-and-click task; control process flow execution behavior when an error occurs; export results to Microsoft `EXCEL` and Microsoft PowerPoint destinations with the click of a button; zoom views; filter the data grid with your own WHERE clause; easily define case-insensitive filters; and automatically get the latest product updates. Come see these and more new features and enhancements in SAS Enterprise Guide 7.11, 7.12, and 7.13	sas enterprise guide, excel, sas enterprise guide project, sas base, cas	source	17
206	Instant Formats in a Blink with PROC FORMAT CNTLIN=	Christopher J. Boniface	Do you need to create a format instantly? Does the format have a lot of labels and it would take a long time to type all the codes and labels by hand? Sometimes a SAS programmer needs to create a user-defined format for hundreds or thousands of codes. There must be an easy way to accomplish this without having to type all of the codes. SAS does provide a way to complete a user-defined format without having to type any codes. If the codes and labels are in a text file, SAS dataset, `EXCEL` file, or in any file that can be converted to a SAS dataset, then a SAS user-defined format can be created on the fly. The CNTLIN=option of `PROC FORMAT` allows a user to create a user-defined format or informat from raw data or a SAS file. This paper will demonstrate a couple of quick tips to show how to create two user-defined format's instantly from a raw text file on our Census Bureau website	user-defined format, proc step, proc format, proc tabulate, txt, dataset, character variable, char.	source	6
207	Integrating SAS Visual Analytics with Google Maps for Analysis and Information Visualization	Marcus Palheta, Sergio Costa Cortes	Exploring, analyzing, and presenting information are SAS Visual Analytics strengths. However, when there is a need to extend this information overview to an unlimited audience, outside the boundaries of the Organization, it is necessary to aggregate geographic information to facilitate the interactivity and usage of the information. This application uses programming languages like JavaScript, HTML and CSS, in an integrated way with SAS programming, to show information about the 4,239 Brazilian graduate programs, evaluated by the Coordination for the Improvement of Higher Education Personnel - CAPES, in Brazil, with cartographic precision. The visualization of data generated in SAS-VA, integrated with Google Maps and Google Street View. Thus, its users can select postgraduate programs in Brazilian, knowing its information, theses and dissertations, its location in the Brazilian territory and the university campus of the educational institution	sas visual analytics, sas management console, web service	source	22
208	Interactive Modeling in SAS Visual Analytics	Donald R. Chapman, Jonathan Wexler	SAS Visual Analytics has two add-on offerings, SAS Visual Statistics and SAS Visual Data Mining and Machine Learning, that provide knowledge workers and data scientists an interactive interface for data partition, data exploration, feature engineering, and rapid modeling. These offerings are powered by the SAS Viya platform, thus enabling big data and big analytic problems to be solved. This paper focuses on the steps a user would perform during an interactive modeling session	proc hpsplit, logistic regression, regression analysis, data science, logistic distribution, feature engineering, machine learning, correlation, support vector machine, gradient boosting, neural network, data cleaning, response variable	source	16
209	Intermediate SAS ODS Graphics	Chuck Kincaid	This paper will build on the knowledge gained in the Intro to SAS `ODS` Graphics. The capabilities in `ODS` Graphics grow with every release as both new paradigms and smaller tweaks are introduced. After talking with the `ODS` developers, a selection of the many wonderful capabilities was selected. This paper will look at that selection of both types of capabilities and provide the reader with more tools for their belt. Visualization of data is an important part of telling the story seen in the data. And while the standards and defaults in `ODS` Graphics are very well done, sometimes the user has specific nuances for characters in the story or addition al plot lines they want to incorporate. Almost any possibility, from drama to comedy to mystery, is available in `ODS` Graphics if you know how. We will explore tables, annotation and changing attributes, as well as the BLOCK plot. Any use r o f Base SAS on any platform will find grea t value from the SAS O DS Graphics procedures	proc step, proc sgplot, bar chart, scatter plot, output delivery system, nominal variable, line chart	source zip	28
210	Interrupted Time Series Power Calculation using DO Loop Simulations	Nigel Rozario, Andrew McWilliams, Charity Moore	Interrupted time series analysis (ITS) is a statistical method that uses repeated "snap shots" over regular time intervals to evaluate healthcare interventions in settings where randomization is not feasible. This method can be used to evaluate programs aimed at improving patient outcomes in real-world, clinical settings. In practice, the number of patients and the timing of observations are restricted. This paper describes a statistical program, which will help statisticians identify optimal time segments within a fixed population size for an interrupted time series analysis. This program creates simulations using "DO loops" to calculate the power needed to detect changes over time that may be due to the interventions under evaluation. Parameters used in this program are total sample size in each time period, number of time periods, and the rate of the event before and after the intervention	sample size, time series analysis, sample, proc autoreg, proc step, estimate, regression analysis, do loop, backward selection, response variable, likelihood function	source	15
211	Introduction to Configuring and Managing SAS Grid Manager for Hadoop	Mark Lochbihler	"How can we run traditional SASÒ jobs, including S AS workspace servers on Hadoop worker nodes?" The answer is SASÒ Grid Manager for Hadoop. It has been integrated with the Hadoop ecosystem to provide resource management, high availability and enterprise scheduling for SAS customers. This paper will provide an introduction architect, configuration and management of SAS Grid Manager for Hadoop. Anyone involved with SAS and Hadoop should find the information in this paper useful. The first area to be covered will be a break down each required SAS and Hadoop component. From the Hadoop ecosystem, we will define the role of YARN Compute, HDFS Storage, and Hadoop Client services. We will review SAS metadata definitions for SAS Grid Manager, Object Spawner and Grid Workspace Servers. We will cover required Kerberos security, as well as SAS Enterprise Guide and the SASGSUB utility. YARN queues and the SAS Grid Policy file for optimizing job scheduling will be reviewed	sas grid, sas enterprise guide, grid computing, dataset, hadoop, sas batch, sas library	source	16
212	Introduction to ODS Graphics	Chuck Kincaid	This presentation teaches the audience how to use `ODS` Graphics. Now part of Base SAS, `ODS` Graphics are a great way to easily create clear graphics that enable any user to tell their story well. `SGPLOT` and SGPANEL are two of the procedures that can be used to produce powerful graphics that used to require a lot of work. The core of the procedures is explained, as well as some of the many options av ailable. Furthermore, we explore the ways to combine the individual statements to make more complex graphics that tell the story better. Any user of Base SAS on any platform will find great value in the SAS `ODS` Graphics procedures	proc step, series plot, sas base, numeric variable, proc sgplot, proc sgpanel	source zip	18
213	Introduction to SAS Data Connectors and SAS Data Connect Accelerators on SAS Viya	Salman Maher, Chris DeHart, Barbara Kemper	For many years now you have learned the ins and outs of using `SAS/ACCESS` software to move data into SAS to do your analytics. With the new open, cloud-ready SAS Viya platform comes a new set of data access technologies known as SAS Data Connectors and SAS Data Connect Accelerators. This paper describes what these new data access products are and how they integrate with the SAS Viya platform. After reading this paper, you will have the foundation needed to load data from third-party data sources into SAS Viya	proc step, sas viya, cas action, cas, sas log, sample, cross join, proc sql	source	17
214	Investigating Big-Data Crime Scenes	Theresa H. Ngo	Statistical analysis is like detective work, and a data set is like the crime scene. The data set contains unorganized clues and patterns that can, with proper analysis, ultimately lead to meaningful conclusions. Using SAS tools, a statistical analyst (like any good crime scene investigator) first performs a preliminary analysis of the data set through visualization and descriptive statistics. Based on the results of the preliminary analysis, both the crime scene investigator (CSI) and the statistical analyst (SA) can use subsequent, detailed analysis, along with scientific or analytical tools, to answer the key questions: What happened? What were the causes and effects? Why did this happen? Will it happen again? Applying the CSI analogy, this paper presents an example case study using a two-step process to investigate a big- data crime scene. Part I shows the general procedures that are used to identify clues and patterns and to obtain preliminary insights from those clues	proc means, proc freq, customer satisfaction, missing value, data science, raw data, histogram	source	10
215	Investigating Connections between Disparate Data Sources with SAS Visual Investigator	Gordon Robinson	In 1993, Erin Brockovich, a legal clerk to Edward L. Masry, began a lengthy manual investigation after discovering a link between elevated clusters of cancer cases in Hinkley, CA, and contaminated water in the same area due to the disposal of chemicals from a utility company. In this session, we combine disparate data sources-cancer cases and chemical spillages-to identify connections between the two data sets using SAS Visual Investigator. Using the map and network functionalities, we visualize the contaminated areas and their link to cancer clusters. What took Erin Brockovich months and months to investigate, we can do in minutes with SAS Visual Investigator	data wrangling, data standard	source	10
216	It All Started with a Mouse: Storytelling with SAS Visual Analytics	Tuesdi Helbig	Walt Disney once said, "Of all of our inventions for mass communication, pictures still speak the most universally understood language." Using data visualization to tell our stories makes analytics accessible to a wider audience than we can reach through words and numbers alone. Through SAS Visual Analytics, we can provide insight to a wide variety of audiences, each of whom see the data through a unique lens. Determining the best data and visualizations to provide for users takes concentrated effort and thoughtful planning. This session discusses how Western Kentucky University (WKU) uses SAS Visual Analytics to provide a wide variety of users across campus with the information they need to visually identify trends, even some they never expected to see, and to answer questions they might not have thought to ask	data visualization, missing value	source	16
217	J2SP: An Investigation in Social Factors That Might Influence National GDP	John Eacott, Parselvan Aravazhi, Sid Grover, Jayant Sharma		raw data, missing value	source	16
218	Jumping and Cutting: Using the Hash Object to Implement a Polygon Clipping Algorithm	Seth W. Hoffman	Data with a location component is naturally displayed on a general referenc e map. Base SAS 9.4 provides libraries of m ap data sets to assist in creating these images. Sometimes, a particular sub -region is all that needs to be displayed. The `SAS/GRAPH` `GPROJECT` procedure can create a new subset of the map using the minimu m and maximum latitude and longitude options. However, this method is only capable of cutting out a "rectangular" area. This paper implements the Greiner Horman P olygon Clipping Algorithm1 with the help of the `DATA STEP` Hash Object to create arbitrarily shaped custom map regions	hash table, create table, proc step, inner join, proc gmap, macro definition, proc sql, macro loop	source	10
219	KA Team: Crime in the City of Philadelphia	Edwin Baidoo, Christina Jones, Muniza Naqvi		data mining, bar chart, binary variable, k-nearest neighbors, proc freq	source	21
220	Kerberos Cross-Realm Authentication: Unraveling the Mysteries	Stuart J. Rogers	How do you enable strong authentication across different parts of your organization in a safe and secure way? We know that Kerberos provides us with a safe and secure strong authentication mechanism, but how does it work across different domains or realms? In this paper, we examine how Kerberos cross- realm authentication works and the different parts that you need ready in order to use Kerberos effectively. Understanding the principles and applying the ideas we present will make you successful at improving the security of your authentication system	grid computing, phase i, phase ii, sas administrator	source	18
221	Key Components and Finished Products Inventory Optimization for a Multi-Echelon Assembly System	Sherry Xu, Kansun Xia, Ruonan Qiu	A leading global information and communications technology solution company provides a broad range of telecom products across the world. Their finished products share commonality in key components, and, in most cases, are assembled after the customer orders are placed. Each finished product typically consists of a large number of key components, and the stockout of any components will cause delivery delays of customer orders. For these reasons, the optimal inventory policy of one component should be determined in conjunction with those of other components. Currently the company uses business experience to manage the inventory across their supply chain network for all of the components and finished products. However, the increasing variety of products and business expansion raise difficulties in inventory management. The company wants to explore a systematic approach to optimizing inventory policies, assuring customer service level and minimizing total inventory cost	inventory optimization, odds ratio	source	7
222	Know Thyself: Diabetes Trend Analysis	Taylor K. Larkin, Denise J. McManus	Presentation	trend analysis	source	6
223	Know Your Tools Before You Use	Justin Jia	When analyzing data with SAS, we often use SAS `DATA STEP` and `PROC SQL` to explore and manipulate data. Though they both are useful tools in SAS, however, many SAS users do not fully understand their differences, advantages and disadvantages and thus have unnecessary biased debates on them. Therefore, this paper is aimed to illustrate and discuss these aspects with real work examples, which will give SAS users deep insights in utilizing them. Using the right tool for a given circumstance not only provides an easier and more convenient solution, it will also save time and work in programming and thus improve work efficiency. Furthermore, the illustrated methods and advanced programming skills can be used in a wide variety of data analysis and business analytics fields	proc sql, create table, proc step, order by, set statement, data science, group by, moving average, where statement, cross join, retain, sas programming	source	16
224	Leads and Lags: Static and Dynamic Queues in the SAS DATA STEP, 2nd ed.	Mark Keintz	From stock price histories to hospital stay records, analysis of time series data often requires use of lagged (and occasionally lead) values of one or more analysis variable. For the SAS user, the central operational task is typically getting lagged (lead) values for each time point in the data set. While SAS has long provided a LAG function, it has no analogous "lead" function - an especially significant problem in the case of large data series. This paper will (1) review the lag function, in particular the powerful, but non-intuitive implications of its queue-oriented basis, (2) demonstrate efficient ways to generate leads with the same flexibility as the lag function, but without the common and expensive recourse to data re-sorting, and (3) show how to dynamically generate leads and lags through use of the hash object	lag function, hash table, merge statement, time series analysis, set statement, by group processing, missing value, hash iterator	source	15
225	Learn SAS Programming Features to Step Up toward Team Management	Peter Crawford	Managing your career future involves learning outside the box at all stages. The next step is not always on the path we planned as opportunities develop and must be taken when we are ready. Prepare with this paper, which explains important features of Base SAS that support teams. In this presentation, you learn about the following: concatenating team shared folders with personal development areas; creating consistent code; guidelines for a team (not standards); knowing where the documentation will provide the basics; thinking of those who follow (a different interface); creating code for use by others; and how code can "learn" about the SAS environment	sas base, sas enterprise guide, macro function, stored macro, options statement, libname statement, macro variable, proc sql	source	8
226	Learn to Please: Creating SAS Programs for Others	Peter Crawford	Programming for others involves new disciplines not called for when we write to provide results. There are many additional facilities in the languages of SAS to ensure the processes and programs you provide for others will please your customers. Not all are obvious and some seem hidden. The never-ending search to please your friends, colleagues, and customers could start in this presentation	options statement, proc sql, sas enterprise guide, proc step, sas studio, work library, where statement, macro variable, sas function, macro put, sas base, macro definition	source	13
227	Learning from Quality Improvement Data: Introduction to Statistical Process Control Charts	Ruth Croxford	Data is your friend. This paper discusses the use of data for quality improvement (QI). Measurement over time is integral to quality improvement, and statistical process control charts (also known as Shewhart or SPC charts) are a good way to learn from the way measures change over time in response to quality improvement efforts. The paper explains what an SPC chart is, how to choose the correct type of chart, how to create and update a chart using SAS, and how to learn from the chart. The examples come from QI projects in health care, and the material is based on the Institute for Healthcare Improvement's Model for Improvement. However, the material is applicable to other fields, including manufacturing and business. This paper is aimed at people newly considering a QI project, people who want to graph their data and need help with getting started, and at anyone interested in interpreting SPC charts created by someone else	proc step, standard deviation	source	16
228	Let SAS Do Your DIRty Work	Richann J. Watson	Making sure that you have saved all the necessary information to replicate a deliverable can be a cumbersome task. You want to make sure that all the raw data sets and all the derived data sets, whether they are Study Data Tabulation Model (SDTM) data sets or Analysis Data Model (ADaM) data sets, are saved. You prefer that the date/time stamps are preserved. Not only do you need the data sets, you also need to keep a copy of all programs that were used to produce the deliverable, as well as the corresponding logs from when the programs were executed. Any other information that was needed to produce the necessary outputs also needs to be saved. You must do all of this for each deliverable, and it can be easy to overlook a step or some key information. Most people do this process manually. It can be a time-consuming process, so why not let SAS do the work for you?	macro variable, dataset, cdisc	source	8
229	Let the System Do Repeating Work for You	Laurent de Walick, Bas Marsman	Developing software using agile methodologies has become the common practice in many organizations. We use the SCRUM methodology to prepare, plan and implement changes in our analytics environment. Preparing for the deployment of new release usually took a two days of creating packages, promoting them, deploying jobs, creating migration script and correcting errors made in the first attempt. A ten working days, two weeks, sprint was effectively reduced to barely seven. By automating this process we were able to reduce the time needed to prepare our deployment to less half a day, increasing the time we can spend developing by 25%. In this paper we present the process and system prerequisites to automate the deployment process, and process, code and scripts to automate metadata promotion and physical table comparison and update	proc step, data step, dataset	source	13
230	Linear Model Regularization	Shashank Hebbar, Lili Zhang, Dhiraj Gharana	Presentation	regression analysis, ridge regression	source	1
231	Localized Messages: Make Your SAS Applications Ready for Any Language	Lihsin Hwang	Presentation		source	4
232	Location Analytics: Minority Report Is Here. Real-Time Geofencing Using SAS Event Stream Processing	Frederic Combaneyre	Geofencing is one of the most promising and exciting concepts that has developed with the advent of the Internet of Things. Like John Anderton in the 2002 movie "Minority Report," you can now enter a mall and immediately receive commercial ads and offers based on your personal taste and past purchases. Authorities can track vessels' positions and detect when a ship is not in the area it should be, or they can forecast and optimize harbor arrivals. When a truck driver breaks from the route, the dispatcher can be alerted and can act immediately. And there are countless examples from manufacturing, industry, security, or even households. All of these applications are based on the core concept of geofencing, which consists of detecting whether a device's position is within a defined geographical boundary. Geofencing requires real-time processing in order to react appropriately	stream processing, real-time processing, sample	source	10
233	Making Graphs Easier to Validate - The Benefits of ODS Graphics	Philip R. Holland			source	15
234	Making SAS Education Relevant to the Future Workforce	Charlotte Baker, Matthew Dutton	SAS education is a mainstay across disciplines and educational levels in the United States. Along with other courses that are relevant to the jobs students want, independent SAS courses or SAS education integrated into additional courses can help a student be more interesting to a potential employer. The multitude of SAS versions (SAS University Edition, Base SAS, SAS Enterprise Guide, SAS Studio, and the SAS OnDemand offerings) provide the tools for education but reaching students where they are is the greatest key for making the education count. This presentation will discuss several roadblocks to learning SAS syntax or "point-and-click" from the student perspective and several solutions developed jointly by students and educators in one graduate educational program	sas syntax, sas enterprise guide	source	4
235	Managing Real-Time Data Streams to High-Performance Analytics Engines	Katherine Taylor	You're in the business of performing complex analysis on large amounts of data. This data changes quickly and often, so you've invested in a powerful high-performance analytics engine with the speed to respond to a real-time data stream. However, you realize an immediate problem upon the implementation of your software solution: your analytics engine wants to process many records of data at once, but your streaming engine wants to send individual records. How do you store this streaming data? How do you tell the analytics engine about the updates? This paper explains how to manage real-time streaming data in a batch processing analytics engine. The problem of managing streaming data in analytics engines comes up in many industries: energy, finance, health care, and marketing to name a few. The solution described in this paper can be applied in any industry, using features common to most analytics engines	proc step, batch processing	source	10
236	Managing the parking lot! Must-haves and good-to-haves for a highly effective analytics team	Aarti Gupta	Every organization-f rom the most mature, old-line company to a day-one startup-n eeds to grow organically. A deep understanding of internal customer and operational data is the single biggest catalyst to develop and sustain it. Advanced analytics and big data directly feed into this, and there are best practices that any organization (across the entire growth curve) can adopt to drive success. Analytics teams can be drivers of growth. But to be truly effective, key best practices need to be implemented. These practices include in-the-weeds details, like the approach to data hygiene, as well as strategic practices like team structure and model governance. When executed poorly, business leadership and the analytics team are unable to communicate with each other- they talk past each other and don't work together toward a common goal	model governance, data dictionary	source	3
237	Managing the SAS Development Life Cycle across Environments and within a Single Production Environment	Andrew Howell	How many environments does your organization have - three (Dev, Test, Prod), five (Dev, SIT, UAT, Pre-Prod, Prod), or maybe only one? Once you've built your SAS process - an ETL job, a model, an exploration, or a report - how should you promote it across these environments? If you have only one environment, is a development life cycle still possible? (Yes, it is.) Historically, the traditional systems development life cycle (SDLC) spans multiple environments (for example, Dev/Test/Prod). This approach has benefits-primarily to ensure that change in one environment does not adversely impact others, but costs and release time-frames mean this is not always practicable. Some sites now adopt a two-platform implementation: Non-Production and Production. Non-Prod exists for technology change, such as new software, hot fixes, database connections, and so on	sas enterprise guide, sas management console, access control, sas batch, spk file	source	15
238	Mapping Roanoke Island: From 1585 to Present	Barbara B. Okerson	One of the first maps of the present United States was John White's 1585 map of the Albemarle Sound and Roanoke Island, the site of the Lost Colony and the site of my present home. This presentation looks at advances in mapping through the ages, from the early surveys and hand-painted maps, through lithographic and photochemical processes, to digitization and computerization. Inherent difficulties in including small pieces of coastal land-o ften removed from map boundary files and data sets to smooth a boundary-a re also discussed. The paper concludes with several current maps of Roanoke Island created with SAS	proc step, proc report	source	15
239	Maximizing Cross-Sell Opportunities with Predictive Analytics for Financial Institutions	Nathaniel Derby	In the increasingly competitive environment for banks and credit unions, every potential advantage should be pursued. One of these advantages is to market additional products to our existing customers rather than to new customers, since our existing customers already know (and hopefully trust) us, and we have so much data on them. But how can this best be done? How can we market the right products to the right customers at the right time? Predictive analytics can do this by forecasting which customers have the highest chance of purchasing a given financial product. This paper provides a step-by-step overview of a relatively simple but comprehensive approach to maximize cross-sell opportunities among our customers. We first prepare the data for a statistical analysis. With some basic predictive analytics techniques, we can then identify those customers who have the highest chance of buying a financial product	data cleaning, predictive analytics, gain chart	source	16
240	Merging Marketing and Merchandising in Retail to Drive Profitable, Customer-Centric Assortments	Brittany Bullard	As a retailer, have you ever found yourself reviewing your last season's assortment and wondering, "What should I have carried in my assortment"? You are constantly faced with the challenge of product selection, placement and ensuring your assortment will drive profitable sales. With millions of consumers, thousands of products, and hundreds of locations, this question can often times be challenging and overwhelming. With the rise in omnichannel, traditional approaches just won't cut it to gain the insights needed to maximize and manage localized assortments as well as increase customer satisfaction. This presentation explores applications of analytics within marketing and merchandising to drive assortment curation as well as relevancy for customers	customer experience, customer satisfaction	source	4
241	Meta-Analysis of Human Trafficking in the United States	David J. Corliss, Heather Hill	Meta-analysis is a method for combining multiple independent studies on the same subject or question, producing a single large study with increased accuracy and enhanced ability to detect overall trends and smaller effects. This is done by treating the results of each study as a single observation and performing analysis on the set, while controlling for differences between individual studies. These differences can be treated as either fixed or random effects depending on context. This paper demonstrates the process and techniques used in meta-analysis using human trafficking studies. This problem has seen increasing interest in the past few years and there are now a number of localized studies for one state or a metropolitan area. This meta-analysis combines these to begin development of a comprehensive analytic understanding of human trafficking across the United States. Both fixed and random effects are described	fixed effects, proc mixed, proc step, regression analysis, data science	source	8
242	Methods of Multinomial Classification Using Support Vector Machines	Ralph Abbey, Taiping He, Tao Wang	Many practitioners of machine learning are familiar with support vector machines (SVMs) for solving binary classification problems. Two established methods of using SVMs in multinomial classification are the one-versus-all approach and the one-versus-one approach. This paper describes how to use SAS software to implement these two methods of multinomial classification, with emphasis on both training the model and scoring new data. A variety of data sets are used to illustrate the pros and cons of each	score code, support vector machine, binary variable, macro definition, machine learning, macro variable, response variable, proc step, data mining	source	20
243	Microservices and Many-Task Computing for High-Performance Analytics	Henry Bequet	A microservice architecture prescribes the design of your software application as suites of independently deployable services. In this paper, we detail how you can design your SAS 9.4 programs so that they adhere to a microservice architecture. We also describe how you can leverage Many-Task Computing (MTC) in your SAS programs to gain a high level of parallelism. Under these paradigms, your SAS code will gain encapsulation, robustness, reusability, and performance. The design principles discussed in this paper are implemented in the SAS Infrastructure for Risk Management (IRM) solution. Readers with an intermediate knowledge of Base SAS and the SAS Macro Language will understand how to design their SAS code so that it follows these principles and reaps the benefits of a microservice architecture	dataset, sas programmer, binomial distribution	source	7
244	Migrating Dashboards from SAS BI Dashboard to SAS Visual Analytics	Roc Zhang, Junjie Li, Wei Lu, Huazhang Shao	SAS BI Dashboard is an important business intelligence and data visualization product used by many customers worldwide. They still rely on SAS BI Dashboard for performance monitoring and decision support. SAS Visual Analytics is a new-generation product, which empowers customers to explore huge volumes of data very quickly and view visualized results with web browsers and mobile devices. Since SAS Visual Analytics is used by more and more regular customers, some SAS BI Dashboard customers might want to migrate existing dashboards to SAS Visual Analytics to take advantage of new technologies. In addition, some customers might hope to deploy the two products in parallel and keep everyone on the same page. Because the two products use different data models and formats, a special conversion tool is developed to convert SAS BI Dashboard dashboards into SAS Visual Analytics dashboards and reports	sas management console, proc sql, dataset	source	17
245	Migrating Large, Complex SAS Environments: In-Place versus New Build	Chris James	SAS migrations are the number one reason why SAS architects and administrators are fired. Even though this bold statement is not universally true, it has been at the epicenter of many management and technical discussions at UnitedHealth Group. The competing business forces between the desire to innovate and to provide platform stability drive difficult discussions between business leaders and IT partners that tend to result in a frustrated user-base, flustered IT professionals, and a stale SAS environment. Migrations are the antagonist of any IT professional because of the disruption, long hours, and stress that typically ensues	error message, business intelligence	source	9
246	Mind-Map the Gap: Sentiment Analysis of Public Transport	Tamas Bosznay	This paper presents a case study where social media posts by individuals related to public transport companies in the United Kingdom are collected from social media sites such as Twitter using SAS . The posts are then further processed in SAS Text Miner to retrieve brand names, means of public transport (underground, trains, buses) and their attributes mentioned. Relevant concepts and topics are identified using text mining techniques and visualized using concept links and word clouds. Later we aim to identify and categorize sentiments against public transport in the corpus of the posts. Finally, we approach to create an association map/mind-map of the different service dimensions/topics and the brands of public transport using correspondence analysis	proc corresp, proc step, data mining, libname engine, parsing, sample, proc freq	source	20
247	Model Risk: Learning from Others' Mistakes	Boaz Galinson	Lending is the core business of most banks. One of the key success factors in credit is to be able to differentiate between the good borrowers and the bad ones by using credit rating models (estimating probability of default, exposure at default and loss given default) you can trust. These models may be input to other models such as credit pricing models, concentration risk models, economic capital models, etc. As a result, a flawed model may have a severe impact on decision-making when it is based on models. Therefore development of corporate governance framework for models along with specific IT tools to monitor and mitigate that risk,is required	concentration risk, credit risk	source	4
248	Modeling Actuarial Risk using SAS Enterprise Guide: A Study on Mortality Tables and Interest Rates	Luiz Carlos Leao	This paper has the objective to present a methodology for interest rates, life tables and actuarial calculations using generational mortality tables and forward structure of interest rate for pension funds for making an analysis of long-term actuarial projections and their impacts on the actuarial liability. It was developed a computational algorithm in SAS Enterprise Guide and SAS Base for structuring the actuarial projections and then it was analysed the impacts of this new methodology. There was heavy use of `PROC IML` and SQL. Keywords: Pension funds. Mortality Tables. Interest Rates. Mathematical Reserves. Actuarial Risk	interest rate, sas enterprise guide, statistical software, proc sql, proc step	source	20
249	Modeling Best Practices: An IFRS 9 Case Study	Peter Baquero, Ling Xiang	A successful conversion to the International Financial Reporting Standards (IFRS) standard known as IFRS 9 can present many challenges for a financial institution. We discuss how leveraging best practices in project management, accounting standards, and platform implementation can overcome these challenges. Effective project management methodology ensures business stakeholders are actively engaged during the implementation of the solution. Business partnership with the implementation team and enablement for business stakeholders are vital to the success of the solution. Project management methodology must focus on effectively defining success criteria, all major decision points, and thorough documentation of the platform and traceability for unique configuration directly to specific business requirements	ifrs 9, data governance, expected loss, pd	source	8
250	Modeling Machiavelianism: Predicting Scores with Fewer Factors	Patrick Schambach	Presentation		source	5
251	Modeling the Merchandise Return Behavior of Anonymous and Non-Anonymous Online Apparel Retail Shoppers	Sunny Lam	This paper establishes the conceptualization of the dimension of shopping cart (or market basket) on apparel retail websites. It analyzes how the cart dimension (describing anonymous shoppers) and customer dimension (describing non-anonymous shoppers) impact merchandise return behavior. Five data-mining techniques - namely logistic regression, decision tree, neural network, gradient boosting and support vector machine - are used for predicting the likelihood of merchandise return. The target variable is a dichotomous response variable: return vs not return. The primary input variables are conceptualized as the constituents of the cart dimension, derived from engineering merchandise-related variables such as item style, item size and item color, as well as free-shipping-related thresholds	likelihood function, neural network, data mining, proc hpsplit, proc means, sample, logistic regression, create table, support vector machine, gradient boosting, regression analysis, logistic distribution, response variable, recommendation engine, proc sql, sas enterprise guide, data step, data transformation, data cleaning	source	20
252	Monitoring Dynamic Social Networks Using SAS/IML, SAS/QC, and R	Huan Li, Michael Porter	Presentation		source	6
253	More than a Report: Mapping the TABULATE Procedure as a Nested Data Object	Jason Phillips	The `TABULATE` procedure has long been a central workhorse of our organization's reporting processes, given that it offers a uniquely concise syntax for obtaining descriptive statistics on deeply grouped and nested categories withi n a data set. Given the diverse output capabilities of SAS, it oft en then suffices to simply ship the procedure' s completed output e lsewhere via the Output Delivery System (`ODS`). Yet there remain cases in which we want to not only obtain a formatted result, but also to acquire the full nesting tree and logic by which the computations were made. In these cases, we want to treat the details of the Tabulate statement s as data, not merely as presentation. I demonstrate how we have solved thi s problem by parsing our Tabulate statement s into a nested tree structure in JSON that can be transferred and easily queried for deep values elsewher e beyond the SAS program	proc tabulate, proc step, out=	source	8
254	More Than Just a Pretty Face: Using SAS Output Delivery System to Create Microsoft Excel Worksheets That Answer Those Difficult Questions	Chevell T. Parker	Microsoft `EXCEL` worksheets enable you to explore data that answers the difficult questions that you face daily in your work. When you combine the SAS Output Deliver System (`ODS`) with the capabilities of `EXCEL`, you have a powerful toolset you can use to manipulate data in various ways, including highlighting data, using formulas to answer questions, and adding a pivot table or graph. In addition, `ODS` and `EXCEL` give you many methods for enhancing the appearance of your tables and graphs. This paper, written for the beginning analyst to the most advanced programmer, illustrates first how to manipulate styles and presentation elements in your worksheets by controlling text wrapping, highlighting and exploring data, and specifying `EXCEL` templates for data. Then, the paper explains how to use the TableEditor tagset and other tools to build and manipulate both basic and complex pivot tables that can help you answer all of the questions about your data. You will also learn techniques for sorting, filtering, and summarizing pivot-table data	excel, output delivery system, sas studio, proc step, raw data, automatic variable, sas enterprise guide	source	27
255	More Than Matrices: SAS/IML Software Supports New Data Structures	Rick Wicklin	The `SAS/IML` language excels in handling matrices and performing matrix computations. A new feature in `SAS/IML` 14.2 is support for nonmatrix data structures such as tables and lists. In a matrix, all elements are of the same type: numeric or character. Furthermore, all rows have the same length. In contrast, `SAS/IML` 14.2 enables you to create a structure that contains many objects of different types and sizes. For example, you can create an array of matrices in which each matrix has a different dimension. You can create a table, which is an in-memory version of a data set. You can create a list that contains matrices, tables, and other lists. This paper describes the new data structures and shows how you can use them to emulate other structures such as stacks, associative arrays, and trees. It also presents examples of how you can use collections of objects as data structures in statistical algorithms	proc iml, proc step, estimate	source	19
256	Moving Along in Health Research	Rayna K. Matsuno	Presentation	proc step	source	7
257	Multi-Group Calibration in SAS: The IRT Procedure and SAS/IML	Kyung Yong Kim, Seohee Park, Jinah Choi, Hongwook Seo	Presentation	proc step	source	4
258	Multicollinearity: What Is It, Why Should We Care, and How Can It Be Controlled?	Deanna Naomi Schreiber-Gregory	Multicollinearity can be briefly described as the phenomenon in which two or more identified predictor variables in a multiple regression model are highly correlated. The presence of this phenomenon can have a negative impact on the analysis as a whole and can severely limit the conclusions of the research study. This paper reviews and provides examples of the different ways in which multicollinearity can affect a research project, and tells how to detect multicollinearity and how to reduce it once it is found. In order to demonstrate the effects of multicollinearity and how to combat it, this paper explores the proposed techniques by using the Youth Risk Behavior Surveillance System data set. This paper is intended for any level of SAS user. This paper is also written to an audience with a background in behavioral science or statistics	regression analysis, predictor variable, estimate, ridge regression, correlation, response variable, condition index, sample, proc reg, multiple regression, proc corr	source	12
259	Multivariate Time Series: Recent Additions to the VARMAX Procedure	Xilong Chen, Stefanos Kechagias	Recent advances in computing technology, monitoring systems, and data collection mechanisms have prompted renewed interest in multivariate time series analysis. In contrast to univariate time series models, which focus on temporal dependencies of individual variables, multivariate time series models also exploit the interrelationships between different series, thus often yielding improved forecasts. This paper focuses on cointegration and long memory, two phenomena that require careful consideration and are observed in time series data sets from several application areas, such as finance, economics, and computer networks. Cointegration of time series implies a long-run equilibrium between the underlying variables, and long memory is a special type of dependence in which the impact of a series' past values on its future values dies out slowly with the increasing lag	time series analysis, sample, null hypothesis, significance level, proc varmax, sample size, feature selection, proc freq, forecast horizon, stationarity	source zip	14
260	My SAS Grid Scheduler	Patrick Cuba	No Batch Scheduler? No problem! This paper describes the use of a SAS DI Studio job that can be started by a time dependent scheduler like Windows Scheduler (or crontab in UNIX) to mimic a batch scheduler on SAS Grid	sas data integration studio, sas management console, sas base, sas batch, sas grid, macro variable, work library, proc step, sas enterprise guide	source	17
261	N-Stage Machine Learning Analysis with the LUA Procedure Helps Solve Big Data Analysis Problems	Ryo Kiguchi, Eri Sakai, Yoshitake Kitanishi, Akio Tsuji	Data has to be a moderate size when you estimate parameters with machine learning. For data with huge numbers of records like healthcare big data (for example, receipt data) or super multi-dimensional data like genome big data, it is important to follow a procedure in which data is cleaned first and then selection of data or variables for modeling is performed. Big data often consists of macroscopic and microscopic groups. With these groups, it is possible to increase the accuracy of estimation by following the above procedure in which data is cleaned from a macro perspective and the selection of data or variables for modeling is performed from a micro perspective. This kind of step-wise procedure can be expected to help reduce bias. We also propose a new analysis algorithm with N-stage machine learning. For simplicity, we assume N =2. Note that different machine learning approaches should be applied; that is, a random forest method is used at the first step, and an elastic net method is used at the second step. For programming N-stage machine learning, we use the `LUA` procedure that is not only efficient, but also enables an easily readable iteration algorithm to be developed. Note that we use well-known machine learning methods that are implementable with SAS 9.4 and SAS product IMSTAT etc	machine learning, big data, elastic net, proc hpforest, predictor variable, feature selection, proc step, proc lua, proc glmselect, correlation, sample, &variable, regression analysis, %if-%then-%else	source	8
262	Name That Function: Punny Function Names with Multiple MEANings and Why You Do Not Want to MISS Out	Ben Cochran, Arthur L. Carpenter	The SAS `DATA STEP` is one of the best (if not the best) data manipulators in the programming world. One of the areas that gives the `DATA STEP` its flexibility and power is the wealth of functions that are available to it. With over 450 functions in the `DATA STEP` it is difficult to learn and remember them all, however understanding how to take advantage of the power of these functions is key to taking full advantage of the `DATA STEP`. This paper takes a PEEK at some of those functions whose names have more than one MEANing. While the subject matter is very serious, the material will be presented in a way that is guaranteed not to BOR the audience. Syntax will be discussed and examples of how these functions can be used to manipulate data will be demonstrated. With so many functions available and with less than an HOUR to present, we obviously will have to TRIM our list so that the presentation will fit within the allotted TIME	date variable, sysdate9, dataset, character functions, poisson distribution	source	11
263	Need a Graphic for a Scientific Journal? No Problem!	Charlotte Baker	Graphics are an excellent way to display results from multiple statistical analyses and get a visual message across to the correct audience. Scientific journals often have very precise requirements for graphs that are submitted with manuscripts. While authors often find themselves using tools other than SAS to create these graphs, the combination of the `SGPLOT` procedure and the Output Delivery System enables authors to create what they need in the same place as they conducted their analysis. This presentation focuses on two methods for creating a publication quality graphic in SAS 9.4 and provides solutions for some issues encountered when doing so	scatter plot, regression analysis, proc sgplot, proc step, proc reg	source	14
264	New for SAS 9.4: Including Text and Graphics in Your Microsoft Excel Workbooks, Part 2	Vincent DelGobbo	A new `ODS` destination for creating Microsoft `EXCEL` workbooks is available starting in the third maintenance release for SAS 9.4. This destination creates native Microsoft `EXCEL` XLSX files, supports graphic images, and offers other advantages over the older ExcelXP tagset. In this presentation you will learn step-by-step techniques for quickly and easily creating attractive multi-sheet `EXCEL` workbooks that contain your SAS output. The techniques can be used regardless of the platform on which SAS software is installed. You can even use them on a mainframe! Creating and delivering your workbooks on-demand and in real-time using SAS server technology is discussed. Although the title is similar to previous presentations by this author, this presentation contains new and revised material not previously presented. Using earlier versions of SAS to create multi-sheet workbooks is also discussed	excel, xls, adverse events, proc report, sas output, proc step, sas base, dataset, automatic variable, bar chart, proc tabulate, options statement, proc freq, sample	source zip	26
265	Nine Best Practices for Big Data Dashboards Using SAS Visual Analytics	Elena Snavely	Creating your first suite of reports using SAS Visual Analytics is like being a kid in a candy store-with so many options for data visualization, it is difficult to know where to start. Having a plan for implementation can save you a lot of time in development and beyond, especially when you are wrangling big data. This paper helps you make sure that you are parallelizing work (where possible), maximizing your data insights, and creating a polished end product. There are guidelines to common questions, such as "How many objects are 'too many'?" or "When should I use multiple tabs versus report linking?" to start any data visualizer off on the right foot	bar chart, time series analysis, proc freq, series plot, raw data, sample	source	7
266	Not So Simple: Intervals You Can Have Confidence In with Real Survey Data	David A. Vandenbroucke	Confidence intervals are critical to understanding your survey data. If your intervals are too narrow, you might inadvertently judge a result to be statistically significant when it is not. While many familiar SAS procedures, such as `PROC MEANS` and `PROC REG`, provide statistical tests, they rely on the assumption that the data come from a simple random sample. However, almost no real-world survey uses such sampling. Learn how to use the `SURVEYMEANS` procedure and its "SURVEY" cousins to estimate confidence intervals and perform significance tests that account for the structure of the underlying survey, including the replicate weights now supplied by some statistical agencies. Learn how to extract the results you need from the flood of output that these procedures deliver	sample, proc step, confidence interval, proc calis, random sampling, proc surveymeans, inferential statistics, proc freq, regression analysis, proc means, proc tabulate	source	12
267	ODS TAGSETS.EXCELXP and ODS EXCEL Showdown	Christopher J. Boniface	Do you create `EXCEL` files from SAS? Do you use `ODS` `TAGSETS`.EXCELXP or `ODS` `EXCEL`? The ExcelXP tagset and `ODS` `EXCEL` destinations are compared face to face. There's gonna be a showdown! We provide quick tips for each and show how to create `EXCEL` files for our Special Census program. The pros of each destination are explored. We'll show the added benefits of `ODS` `EXCEL`. We'll display how to create XML files with the ExcelXP tagset. We'll present how to use TAGATTR formats with the ExcelXP tagset to ensure that leading and trailing zeroes in `EXCEL` are preserved. Secondly, we'll demonstrate how to create the same `EXCEL` file with `ODS` `EXCEL` with SAS formats instead of TAGATTR formats. Additionally, we'll see that `ODS` `EXCEL` creates native `EXCEL` files. One of the drawbacks of an XML file created with the ExcelXP tagset is that a popup message is displayed in Microsoft `EXCEL` each time you open it	excel, format statement, output delivery system, character variable, ods tagsets.excelxp, proc report, ods excel, proc step	source	6
268	One SAS To Rule Them All...	William 'Gui' Zupko II	In order to display data visually, our audience preferred `EXCEL`'s compared to SAS's charts and graphs. However, to make the necessary 30 graphs in `EXCEL` took 2-3 hours of manual work, even having chart templates already created, and also led to mistakes due to human error. SAS graphs took much less time to create, but lacked key functionality that the audience preferred available in `EXCEL` graphs. Thanks to SAS, the answer came in X4ML programming. SAS can actually submit coding to `EXCEL` in order to create customized data reporting, create graphs or update templates' data series, and even populate word documents for finalized reports. This paper explores how SAS is used to create presentation-ready graphs in a proven process that takes less than one minute, compared to the earlier process that took hours. The following code will be utilized and/or discussed: `%MACRO`(macro_var), `FILENAME`, rc commands, `ODS`, X4ML, and VBA (Microsoft Visual Basic for Applications)	excel, dataset	source	8
269	Oops! You Did It Again with PROC DS2	Ryan Kumpfmiller, Maria Nicholson	Presentation	proc step	source	11
270	Open Your Mind: Use Cases for SAS and Open-Source Analytics	Tuba Islam	Data scientists need analytical tools and algorithms, whether commercial or open source, and will always have some favourites. But how do you decide when to use what? And how can you integrate their use to your maximum advantage? This paper provides best practices for deploying both SAS and open-source analytical tools to increase productivity and efficiency in your enterprise ecosystem. Further, an example is provided of an analytical business flow for marketing using SAS and R algorithms in SAS Enterprise Miner for developing a predictive model, and then operationalizing and automating that model for scoring, performance monitoring and retraining. There are also suggestions for using Python and SAS integration in a Jupyter Notebook environment. Seeing these examples will help you decide how to improve your analytics with similar integration of SAS and open source	data mining, score code, proc hpsplit, regression analysis	source	10
271	Optimization of Interactive Voice Response Systems Using SAS	Padmashri Janarthanam, Vinoth Kumar Raja	Many communication channels exist for customers to engage with businesses, yet Interactive Voice Response (IVR) systems remain most popular of them. IVR acts as the front end to consumer interaction and is the most effective method for customers to resolve their issues before talking to an agent. If IVR interface is not designed properly customers would be stuck in an endless loop of pressing buttons that can lead to consumer frustration. The bottom line is this: IVR systems should be setup to quickly resolve as many routine inbound inquires as possible, and allow customers to speak to an agent when necessary. The purpose of this paper is to demonstrate how SAS tools helped optimize the IVR system for a book publishing company. The dataset used in this study was obtained from a telecom services company and contained IVR logs of more than 300,000 calls with 1.4 million observations	data mining, customer satisfaction	source	6
272	Optimize My Stock Portfolio! A Case Study with Three Different Estimates of Risk	Aric LaBarr	People typically invest in more than one stock to help diversify their risk. These stock portfolios are a collection of assets that each have their own inherit risk. If you know the future risk of each of the assets, you can optimize how much of each asset to keep in the portfolio. The real challenge is trying to evaluate the potential future risk of these assets. Different techniques provide different forecasts, which can drastically change the optimal allocation of assets. This talk presents a case study of portfolio optimization in three different scenarios - historical standard deviation estimation, capital asset pricing model (CAPM), and GARCH-based volatility modeling. The structure and results of these three approaches will be discussed	covariance, proc step, odds ratio, proc reg, proc autoreg, standard deviation, regression analysis, market risk, likelihood function	source	11
273	Optimizing Anti-Money Laundering Transaction Monitoring Systems Using SAS Analytical Tools	Stephen M. Overton, Eric Hale, Leigh Ann Herhold	Financial institutions are faced with a common challenge to meet the ever increasing demand from regulators to monitor and mitigate money laundering risk. Anti-Money Laundering (AML) Transaction Monitoring systems produce large volumes of work items, most of which do not result in quality investigations or actionable results. Backlogs of work items have forced some financial institutions to contract staffing firms to triage alerts spanning back months. Additionally, business analysts struggle to define interactions between AML models and explain what attributes make a model productive. There is no one approach to solve this issue. Analysts need several analytical tools explore model relationships, improve existing model performance, and add coverage for uncovered risk	machine learning, suspicious activity, sas studio, sas enterprise guide, aml, sample, customer segmentation, data mining, proc freq	source	10
274	Optimizing Delivery Routes with SAS Software	Ben Murphy, Bruce Bedford	Optimizing delivery routes and efficiently using delivery drivers are examples of classic problems in operations research, such as the Traveling Salesman Problem. In this paper, Oberweis and Zencos collaborate to describe how to leverage SAS software to solve these problems and optimize delivery routes for a retail delivery service. Oberweis Dairy specializes in home delivery service that delivers customers premium dairy products directly to their homes. Because freshness is critical to delivering an excellent customer experience, Oberweis is especially motivated to optimize their delivery logistics. As Oberweis works to develop an expanding footprint and a growing business, Zencos is helping to ensure that delivery routes are optimized and delivery drivers are used efficiently	proc step, sample, create table, proc sql, proc fastclus, proc means, odds ratio, logistic distribution, sas programming	source	13
275	Optimizing SAS Grid Computing with SAS Scalable Performance Data Server and Dynamic Data Partitioning	Andy Knight	Making optimal use of SAS Grid Computing relies on the ability to spread the workload effectively across all of the available nodes. With SAS Scalable Performance Data Server (SPD Server), it is possible to partition your data and spread the processing across the SAS grid computing environment. In an ideal world it would be possible to adjust the size and number of partitions according to the data volumes being processed on any given day. This paper discusses a technique that enables the processing performed in the SAS Grid Computing environment to be dynamically reconfigured automatically at run time in order to optimize the use of SAS Grid Computing and to provide significant performance benefits	sas grid, grid computing, parallel processing, record count, proc step	source	16
276	Optimizing SAS on Red Hat Enterprise Linux 6 and 7	Barry Marson	Today, companies are increasingly utilizing analytics to discover new revenue and cost-saving opportunities. Many business professionals turn to SAS, a leader in business analytics software and service, to help them improve performance and make better decisions faster. Analytics are also being employed in risk management, fraud detection, life sciences, sports, and many more emerging markets. However, to maximize the value to the business, analytics solutions need to be deployed quickly and cost-effectively, while also providing the ability to readily scale without degrading performance. Of course, in today's demanding environments, where budgets are still shrinking and mandates to reduce carbon footprints are growing, the solution must deliver excellent hardware utilization, power efficiency, and return on investment. To help solve some of these challenges, Red Hat and SAS have collaborated to recommend the best practices for configuring SAS 9 running on Red Hat Enterprise Linux	sas grid, red hat	source	20
277	Optimizing Your Optimizations by Maximizing the Financial and Business Impacts of SAS Marketing Optimization Scenarios	Erin McCarthy	Whether you are a current SAS Marketing Optimization user who wants to fine tune your scenarios, a SAS Marketing Automation user who wants to understand more about how SAS Marketing Optimization might improve your campaigns, or completely new to the world of marketing optimizations, this session covers ideas and insights for getting the highest strategic impact out of SAS Marketing Optimization. SAS Marketing Optimization is powerful analytical software, but like all software, what you get out is largely predicated by what you put in. Building scenarios is as much an art as it is a science, and how you build those scenarios directly impacts your results. What questions should you be asking to establish the best objectives? What suppressions should you consider? We develop and compare multiple what-if scenarios and discuss how to leverage SAS Marketing Optimization as a business decisioning tool in order to determine the best scenarios to deploy for your campaigns	sensitivity analysis, sample	source	14
278	Outline Outliers: Adding a Business Sense	Alex Glushkovsky	Outliers, such as unusual, violated, unexpected or rare events, have been intensively in focus by researchers and practitioners providing their impacts on estimated statistics and developed models. Today, some business disciplines are focusing primarily on outliers such as defaults of credit, operational risks, quality nonconformities, fraud, or even the results of marketing initiatives in highly competitive environments with low response rates of a couple percent or even less. This paper discusses the importance of detecting, isolating, and categorizing business outliers to discover their root causes and to monitor them dynamically. Addressing not only extreme values or multivariable densities detecting outliers, but also addressing distributions, patterns, clusters, combinations of items, and sequences of events will allow for opportunities to be established for business improvement. SAS Enterprise Miner can be used to perform such detections	outlier, response variable, sample, proc hpsplit, sample size, regression analysis, time series analysis, data mining, logistic regression, association rules, big data, data cleaning, logistic distribution, correlation, proc freq	source	17
279	Parallel Processing Your Way to Faster Software and a Big Fat Bonus: Demonstrations in Base SAS	Troy Martin Hughes	SAS software and especially extract-transform- load (ETL) systems commonly include components that must be serialized due to real process dependencies. For example, a transform module often cannot begin until the data extraction completes, and a corresponding load module cannot begin until the data transformation completes. While process dependencies such as these cannot be avoided in many cases and necessitate serialized software design, in other cases, programs or data can be distributed across two or more SAS sessions to be processed in parallel, facilitating significantly faster software. This text introduces the concept of false dependencies, in which software is serialized by design rather than necessity, thus needlessly increasing execution time and deprecating performance	macro variable, parallel processing, proc corr	source	7
280	Parsing JSON with SAS in 2017	John Kennedy	Technology service company BuiltWith reported that, as of March 2017, 94.1% of the top million most visited websites use JavaScript. All of these JavaScript websites use JSON as their standard data format to store and transmit data. Base SAS has many powerful features for interpreting JSON, including the JSON `LIBNAME`, introduced in SAS 9.4. This paper describes, in brief, several methods for reading in JSON data to SAS, weighing their pros and cons and guiding users on when to use each. It also involves Henry Winkler	proc step, proc groovy, proc json	source	4
281	Patching the Holes in SQL Systems with SAS	Chris Carson	As a programmer specializing in tracking systems for research projects, I was recently given the task of implementing a newly developed, web-based tracking system for a complex field study. This tracking system uses a SQL database on the back end to hold a large set of related tables. As I learned about the new system, I found that there were deficiencies to overcome to make the system work on the project. Fortunately, I was able to develop a set of utilities in SAS to bridge the gaps in the system and to integrate the system with other systems used for field survey administration on the project	proc sql, sql server, inner join, proc step, create table, sample, libname statement, order by, stored procedure, excel	source	16
282	Personally Identifiable Information Secured Transformation	Shuhua Liang, Zoe Bider-Canfield	Organizations that create and store Personally Identifiable Information (PII) are often required to de- identify sensitive data to protect individuals' privacy. There are multiple methods that SAS can be used to de-identify PII depending on data types and encryption needs. The first method is `DATA STEP` encryption, which uses a built-in SAS function to password protect datasets. The second method is to apply a crosswalk mapping by linking a dataset with PII to a secured dataset that contains the PII and its corresponding surrogate, which is then used to replace the PII in the original dataset. The third method is in-SAS encryption which involves translating PII into an encrypted string using SAS functions; this could be a one-byte-to-one-byte, a one-byte-to-multiple-byte swap, or performing a translation on an external databases like Teradata and Oracle	pii, sas function	source pdf	10
283	Pillars of a Successful SAS Implementation with Lessons from Boston Scientific	Brian Bell, Patricia S. Aanderud	Moving a workforce in a new direction takes a lot of energy. Your planning should include four pillars: culture, technology, process, and people. These pillars assist small and large SAS rollouts with a successful implementation and an eye toward future proofing. Boston Scientific is a large multi-national corporation that recently grew from a couple desktops to a global implementation for SAS. Boston Scientific's real world experiences reflect on each pillar both in what worked and lessons learned	flat file, data cleaning	source	7
284	Platform a la Carte: An Assembly Line to Create SAS Enterprise BI Server Instances with Ansible	Javor Evstatiev, Andrey Turlov	Installation and configuration of a SAS Enterprise BI platform in the requirements of the today's world depends on knowledge on a wide variety of subjects. Security requirements are growing, the number of involved components is increasing, time to delivery should be shorter and the quality improved. The expectations of the customers are based on cloud experiences where automated deployments with ready to use applications are state of the art. This paper describes an approach to address the challenges of deploying SAS 9.4 on Linux to meet today's customer expectations	data cleaning, sas 9	source	9
285	Please Come In: Social Login for SAS Web Applications	Michael Dixon	For customers providing SAS reporting to the public the ability to use a Social login opens several possibilities for providing richer services. Instead of everybody using a generic Guest access and being limited to a common subset of reports or other functionality, previously u nknown users can seamlessly login and access SAS Web content while SAS Administrators can continue to apply best-practice security. This paper focuses on integrating Google Sign-In, Microsoft Account Sign-In and Facebook Sign-In as alternative methods to log in from the SAS Logon Manager, as well as registering any new users in the SAS Metadata automatically	sas management console	source	15
286	Power to the People! Web Service Scoring for the Masses	Prasenjit Sen, Chris Upton	SAS Decision Manager includes a hidden gem: a web service for high speed online scoring of business events. SAS 9.4 M4 represents the third release of the SAS Micro Analytic Service for scoring SAS DS2 code decisions in a standard JSON web service. Users will learn how to create decisions, deploy modules to the web service, test the service, and record business events	web service, rest api, data validation rules, call execute, score code, service provider	source	13
287	Predicting Successful Math Teachers in Secondary Schools in the United States	Bogdan Gadidov	Presentation		source	5
288	Predicting the Completeness of Clinical Data through Claims Data Matching Using SAS Enterprise Miner	Catherine Olson, Thomas Horstman	Presentation	data mining	source	7
289	Predictive Accuracy: A Misleading Performance Measure for Highly Imbalanced Data	Josephine Akosa	The most commonly reported model evaluation metric is the accuracy. This metric can be misleading when the data are imbalanced. In such cases, other evaluation metrics should be considered in addition to the accuracy. This study reviews alternative evaluation metrics for assessing the effectiveness of a model in highly imbalanced data. We used credit card clients in Taiwan as a case study. The data set contains 30,000 instances (22.12% risky and 77.88% non-risky) assessing the likeliness of a customer defaulting on a payment. Three different techniques were used during the model building process. The first technique involved down-sampling the majority class in the training subset. The second used the original imbalanced data whereas prior probabilities were set to account for oversampling in the third technique. The same sets of predictive models were then bui lt for each technique after which the evaluation metrics were computed	imbalanced data, gradient boosting, proc hpsplit, proc hpforest, confusion matrix, type ii error, type i error, neural network, sample, support vector machine, credit card, model validation, roc curve, likelihood function, logistic regression, network optimization, key performance indicator, card fraud, correlation, geomean, feature selection, model training, logistic distribution, regression analysis	source	12
290	Predictive Models: Storing, Scoring, and Evaluating	Matthew Duchnowski	Predictive modeling may just be the most thrilling aspect of data science. Who among us can deny the allure of observing a naturally-occurring phenomenon, conjuring a mathematical model to explain it and then using that model to make predictions about the future? Though many SAS users are familiar with using a data set to generate a model, they may not utilize the awesome power of SAS to "store" their model and "score" other datasets. In this paper we will distinguish between parametric and non- parametric models and discuss the tools that SAS provides for storing each and using them to score a cross-validation set. We will end with a brief survey of common measures often used for evaluating models	proc step, roc curve, validation set, data cleaning, response variable, estimate, logistic distribution, sample, proc logistic, regression analysis, logistic regression, cohen's kappa, proc score	source	10
291	Preparing Analysis Data Model (ADaM) Data Sets and Related Files for FDA Submission with SAS	Sandra Minjoe, John K. Troxell	This paper compiles information from documents produced by the U.S. Food and Drug Administration (FDA), the Clinical Data Interchange Standards Consortium (CDISC), and Computational Sciences Symposium (CSS) workgroups to identify what analysis data and other documentation is to be included in submissions and where it all needs to go. It not only describes requirements, but also includes recommendations for things that aren't so cut-and-dried. It focuses on the New Drug Application (NDA) submissions and a subset of Biologic License Application (BLA) submissions that are covered by the FDA binding guidance documents. Where applicable, SAS tools are described and examples given	adam dataset, dataset, length statement, proc step, character variable, proc copy, data science, cdisc	source	12
292	Price Recommendation Engine for Airbnb	Praneth Guggilla, Singdha Gutha, Goutam Chakraborty	Airbnb is the world's largest home sharing company and has over 800,000 listings in more than 34,000 cities and 190 countries. Therefore, the pricing of their property, done by the Airb nb hosts, is crucial to the business. Setting low prices may hinder profits, while setting high prices may result in no bookings at all. In this paper, we have suggested a pricing methodology for Airbnb hosts that helps in overcoming the problems of overpricing and underpricing by observing the factor that determine occupancy rate and price. Through this methodology, we are trying to identify key factors related to Airbnb pricing such as: 1. Those influential in determining a price for a property 2. The relation between the price of a property and frequency of its booking 3. Similarities among successful and profitable properties As a part of this methodology, we built a scrapping tool to get details of New York City host user data along with their metrics	regression analysis, proc varclus, odds ratio, estimate, data cleaning	source	10
293	Pricing a Self-Funded Health Plan by Applying Generalized Linear Models Using SAS Enterprise Guide	Luiz Carlos Leao	This paper explores the utilization of medical services, which has a characteristic exponential distribution. Because of this characteristic, a variable generalized linear model can be applied to it to obtain self- managed health plan rates. This approach is different from what is generally used to set the rates of health plans. This new methodology is characterized by capturing qualitative elements of exposed participants that old ratemaking methods are not able to capture. Moreover, this paper also uses generalized linear models to estimate the number of days that individuals remain hospitalized. The method is expanded in a project in SAS Enterprise Guide in which the utilization of medical services by the base during the years 2012, 2013, 2014, and 2015 (the last year of the base) is compared with the Hospital Cost Index of Variation	exponential distribution, proc genmod, predictor variable, response variable, goodness of fit, statistical software, probability distribution, sas enterprise guide, histogram, poisson distribution	source	20
294	PROC DATASETS: The Swiss Army Knife of SAS Procedures	Michael A. Raithel	This paper highlights many of the major capabilities of `PROC DATASETS`. It discusses how it can be used as a tool to update variable information in a SAS data set; provide information on data set and catalog contents; delete data sets, catalogs, and indexes; repair damaged SAS data sets; rename files; create and manage audit trails; add, delete, and modify passwords; add and delete integrity constraints; and more. The paper contains examples of the various uses of `PROC DATASETS` that programmers can cut and paste into their own programs as a starting point. After reading this paper, a SAS programmer will have practical knowledge of the many different facets of this important SAS procedure	dataset, proc step, constraint, audit trail, run, key, libname statement, foreign key, sas library, proc datasets, modify statement, sas log, proc contents, sas programmer, proc sql, attrib statement, data integrity, proc append	source	25
295	PROC DS2: What's in It for You?	Viraj R Kumbhakarna	In this paper, we explore advantages of using `PROC DS2` procedure over the `DATA STEP` programming in SAS. DS2 is a new SAS proprietary programming language that is appropriate for advanced data manipulation. We explore use of `PROC DS2` to execute queries in databases using FED SQL from within the DS2 program. Several DS2 language elements accept embedded FedSQL syntax, and the run-time generated queries can exchange data interactively between DS2 and supported database. This action enables SQL preprocessing of input tables, which effectively allows processing data from multiple tables in different databases within the same query thereby drastically reducing processing times and improving performance. We explore use of DS2 for creating tables, bulk loading tables, manipulating tables, and querying data in an efficient manner	proc step, proc sql, data step, set statement, sas base, input statement, run, sas log, ansi sql, end, implicit loop, cpu time	source	11
296	PROC LOGISTIC: Using New SAS 9.4 Features for Cumulative Logit Models with Partial Proportional Odds	Paul Hilliard	Multicategory logit models extend the techniques of logistic regression to response variables with three or more categories. For ordinal response variables, a cumulative logit model assumes that the effect of an explanatory variable is identical for all modeled logits (known as the assumption of proportional odds). Past research supports the finding that as the sample size and number of predictors increase, it is unlikely that proportional odds can be assumed across all predictors. An emerging method to effectively model this relationship uses a partial proportional odds model, fit with unique parameter estimates at each level of the modeled relationship only for the predictors in which proportionality cannot be assumed. Building upon features first employed in `SAS/STAT` 12.1, `PROC LOGISTIC` in SAS 9.4 now extends this functionality for variable selection methods in a manner in which all equal and unequal slope parameters are available for effect selection. Previously, the statistician was required to assess predictor non-proportionality a priori through likelihood tests or subjectively through graphical diagnostics. Following a review of statistical methods and limitations of other commercially available software to model data exhibiting non-proportional odds, a public-use data set is used to examine the new functionality in `PROC LOGISTIC` using stepwise variable selection methods. Model diagnostics and the improvement in prediction compared to a general cumulative model are noted	logistic regression, estimate, proc logistic, logistic distribution, proc step, regression analysis, likelihood function, stepwise selection, degrees of freedom, chi-square test, response variable, sample, cohen's kappa, null hypothesis, proc calis, predictor variable, sample size, weighted kappa, binomial distribution, proc freq, feature selection	source zip pdf	16
297	Production Forecasting in the Age of Big Data in the Oil and Gas Industry	Vipin Prakash Gupta, Satyajit Dwivedi	Presentation	time series analysis, outlier, arma, time series data, moving average, unstructured data, sample	source	17
298	Profitability and Actuarial Overview of Health Insurance on SAS Visual Analytics	Janice Leal	The report brings a simple and intuitive overview on behavior of technical provision and profitability of health insurance segments, based on historical data of a major insurance company. The profitability analysis displays indicators consisting of claims, prices, and quantity of insureds and their performance separated by gender, region, and different pro ducts. The report's user can simulate more accurate premiums by inputting information about medical costs increasing and target claims rate. The technical provision view identifies the greatest impacts on the provision, such as claims payments and future claims payments and reports. Also, it compares the real health insurance costs with the provision estimated on a previous period. Therefore, the report enables the user to get a unique panorama of health insurance underwriting and evaluate its results in order to make strategic decision for the future	health insurance, incurred loss, lag function	source	9
299	Programming Weakly Informative Prior Distributions in SAS	Robert Lew, Hongsheng Wu, Chen-Hsiang Yu	Presentation	posterior probability, prior probability, sample	source	9
300	Propensity Score Methods for Causal Inference with the PSMATCH Procedure	Yang Yuan	In a randomized study, subjects are randomly assigned to either a treated group or a control group. Random assignment ensures that the distribution of the covariates is the same in both groups and that the treatment effect can be estimated by directly comparing the outcomes for the subjects in the two groups. In contrast, subjects in an observational study are not randomly assigned. In order to establish causal interpretations of the treatment effects in observational studies, special statistical approaches that adjust for the covariate confounding are required to obtain unbiased estimation of causal treatment effects. One strategy for correctly estimating the treatment effect is based on the propensity score, which is the conditional probability of the treatment assignment given the observed covariates	propensity score, treatment effect, propensity score matching, potential outcomes, response variable	source	17
301	Property and Casualty Insurance Predictive Analytics in SAS	Mei Najim	Although the statistical foundations of predictive analytics have large overlaps across the Property & Casualty (P&C) insurance, life insurance, banking, pharmaceutical, and genetics industries, etc., the business objectives, data availability, and regulations in these industries are different. Predictive analytics has been evolving rapidly in the P&C insurance industry for the past two decades. The use of predictive analytics has already become an industry best practice and will play an increasingly important role in the P&C insurance industry over the next decade. This paper will first provide an overview of predictive analytics in each of the following core business operations in the P&C insurance industry: marketing, underwriting, actuarial pricing, actuarial reserving, and claims. Then, a common P&C insurance predictive modeling process with large datasets will be illustrated with an example	data mining, predictive analytics, data cleaning, proc hpsplit, feature selection, response variable, regression analysis, property insurance, insurance, logistic regression, neural network, model validation, sas enterprise guide, sas base, loss ratio, univariate analysis, significance level, logistic distribution, casualty insurance, model testing, stepwise selection, validation set, proc genmod, data transformation, incurred loss, model training, feature engineering, correlation	source	17
302	Protecting the Innocent (and Your Data)	Stanley E. Legum	A recurring problem with large research databases containing sensitive information about an individual's health, financial, and personal information is how to make meaningful extracts available to qualified researchers without compromising the privacy of the individuals whose data is in the database. This problem is exacerbated when a large number of extracts need to be made from the database. In addition to using statistical disclosure control methods, this paper recommends limiting the variables included in each extract to the minimum needed and implementing a method of assigning request-specific randomized IDs to each extract that is both secure and self-documenting	macro definition, proc step	source	8
303	Protecting Your Programs from Unwanted Text Using Macro Quoting Functions	Michelle Buchecker	Face it, your data can occasionally have characters that wreak havoc on your macro code. Characters such as the ampersand in AT&T, or the apostrophe in McDonald's for example. This paper is designed for programmers who know most of the in's and out's of SAS Macro code already. Now let's take your macro skills a step farther by adding to your skillset. Specifically, %BQUOTE, %STR, %NRSTR, %SUPERQ what is up with all these quoting functions? When do you one over the other? And why would you need %UNQUOTE? The macro language is full of subtleties and nuances and the quoting functions represent the epitome of all of this. This paper will show you in which instances you would use the different quoting functions. Specifically, what is the difference between the compile time versus execution time functions. In addition to looking at the traditional quoting functions, you will learn how to use %QSCAN and %QSYSFUNC among other functions that apply the regular function and quotes the result	macro variable, macro trigger, macro quoting function, macro put, macro facility, %if-%then-%else	source	5
304	Quality Control Programming: A Lost Art?	Amber Randall, Bill Coar	Conferences for SAS programming are replete with the newest software capabilities and clever programming techniques. But, discussion about quality control (QC) is lacking. QC is fundamental to ensuring both correct results and sound interpretation of data. It is not industry specific, and it simply makes sense. Most QC procedures are a function of regulatory requirements, industry standards, and corporate philosophies. Good QC goes well beyond just reviewing results and should also consider the underlying data. It should be driven by a thoughtful consideration of relevance and impact. While programmers strive to produce correct results, it is no wonder that programming mistakes are common despite rigid QC processes in an industry where expedited deliverables and a lean workforce are the norm. This leads to a lack of trust in team members and an overall increase in resource requirements as these errors are corrected, particularly when SAS programming is outsourced	data cleaning, clinical trial, quality control, adam dataset, independent qc, pharmaceutical industry, final analysis, raw data, data science, clinical programming, sas programming, cdisc	source	15
305	Quick Results with PROC SQL	Kirk Paul Lafler	Structured Query Language (SQL) is a universal language that allows you to access data stored in relational databases or tables. This hands-on workshop presents core concepts and features on using `PROC SQL` to access data stored in relational database tables. Attendees learn how to define, access, and manipulate data from one or more tables using `PROC SQL` quickly and easily. Numerous code examples are presented on how to construct simple queries, subset data, produce simple and effective output, join two tables, summarize data with summary functions, construct BY-groups, identify FIRST. and LAST. rows, and create and use "virtual" tables	proc sql, proc step, dictionary tables, where statement, macro variable, select, create table, inner join, sas log, order by, sas library, group by, macro facility, view, macro call, relational database, right join	source zip	20
306	Quick Results with SAS Enterprise Guide	Kirk Paul Lafler, Mira Shapiro, Ryan Paul Lafler	SAS Enterprise Guide empowers organizations, programmers, business analysts, statisticians and end-users with all the capabilities that SAS has to offer. This presentation describes the built-in wizards to perform reporting and analytical tasks, access to multi-platform enterprise data sources, the delivery of data and results to a variety of mediums and outlets, data manipulation without the need to learn complex coding constructs, and support for data management and documentation requirements. Attendees see the graphical user interface (GUI) to access tab-delimited and `EXCEL` input files; subset and summarize data; join (or merge) two tables together; flexibly export results to HTML, PDF and `EXCEL`; and visually manage projects using flowcharts and diagrams	excel, sas enterprise guide, dataset, proc means, data governance, delimited file, sas output, proc sql	source	18
307	Quick Results with SAS Enterprise Guide	Kirk Paul Lafler, Mira Shapiro, Ryan Paul Lafler	SAS Enterprise Guide empowers organizations, programmers, business analysts, statisticians and end-users with all the capabilities that SAS has to offer. This presentation describes the built-in wizards to perform reporting and analytical tasks, access to multi-platform enterprise data sources, the delivery of data and results to a variety of mediums and outlets, data manipulation without the need to learn complex coding constructs, and support for data management and documentation requirements. Attendees see the graphical user interface (GUI) to access tab-delimited and `EXCEL` input files; subset and summarize data; join (or merge) two tables together; flexibly export results to HTML, PDF and `EXCEL`; and visually manage projects using flowcharts and diagrams	excel, sas enterprise guide, dataset, proc means, data governance, delimited file, sas output, proc sql	source zip	18
308	Quick Results with SAS University Edition	Ryan Paul Lafler	The announcement of SAS Institute's free "SAS University Edition" is an exciting development for SAS users and learners around the world! The software bundle includes Base SAS, `SAS/STAT`, `SAS/IML`, Designer Studio (user interface), and `SAS/ACCESS` for Windows, with all the popular features found in the licensed SAS versions. This is an incredible opportunity for users, statisticians, data analysts, scientists, programmers, students, and academics everywhere to use (and learn) for career opportunities and advancement. Capabilities include data manipulation, data management, comprehensive programming language, powerful analytics, high quality graphics, world-renowned statistical analysis capabilities, and many other exciting features. This paper presents the process of downloading and configuring the SAS University Edition	sas studio, proc step, sas base, data governance, proc sql, excel	source	15
309	Quickish Performance Techniques for Biggish Data	Nick Welke, Patricia S. Aanderud	Getting speedy results from your SAS programs when working with bulky datasets is more than elegant coding techniques. There are several different approaches to improving performance when working with biggish data. While you can upgrade your hardware, this only helps run inefficient code and bloated tables quicker. So, you should also consider what results tuning your database and adjusting your SAS platform can bring. In this paper, we will review the various options to give you some ideas on things you can do better	dataset, work library, proc step, proc sql, bufsize, options statement, cpu time, sas programmer, index	source	11
310	Raising the Bar: The Pending Section 508 Refresh and the ODS HTML5 Destination	Glen R. Walker	The U.S. Access Board will soon "refresh" the Section 508 accessibility standards. The new requirements are based on the Web Content Accessibility Guidelines (WCAG) 2.0 and include a total of 38 testable success criteria - 16 more than the current requirements. Is your organization ready? Don't worry…the fourth maintenance release for SAS 9.4 `ODS HTML5` destination has you covered. This paper describes the new accessibility features in the `ODS HTML5` destination, explains how to use them, and shows you how to test your output for compliance with the new Section 508 standards	proc step, proc report, proc print, output delivery system, ods html	source	20
311	Random Forests with Approximate Bayesian Model Averaging	Tiny Du Toit, Andre De Waal	A random forest is an ensemble of decision trees that often produce more accurate results than a single decision tree. The predictions of the individual trees in the forest are averaged to produce a final prediction. The question now arises whether a better or more accurate final prediction cannot be obtained by a more intelligent use of the trees in the forest. In particular, in the way random forests are currently defined, every tree contributes the same fraction to the final result (e.g. if there are 50 trees, each tree contributes 1/50th to the final result). This ignores model uncertainty as less accurate trees are treated exactly like more accurate trees. Replacing averaging with Bayesian Model Averaging will give better trees the opportunity to contribute more to the final result which may lead to more accurate predictions. However, there are several complications to this approach that have to be resolved, such as the computation of a SBC value for a decision tree	proc hpsplit, neural network, proc hpforest, training set, sample, degrees of freedom, data mining, data cleaning, feature selection, validation set, network optimization, raw data, posterior probability, regression analysis	source	11
312	Read SAS Metadata in SAS Enterprise Guide	Piyush K. Singh, Steven Randolph	SAS Management Console has been key tool to interact with SAS Metadata Servers. But sometime users need much more than what SAS Management Console can do. This paper contains couples of SAS macros which can be used in SAS Enterprise Guide/ PC SAS to read SAS Metadata. These macros read users, roles and groups registered in metadata. SAS macros given in this paper explain how these macros can be executed in SAS Enterprise Guide and how to change these macros to meet other business requirement. There may be some tools available in the market, which can be used to read SAS Metadata but this paper helps in achieving most of them within SAS client like PC SAS and SAS Enterprise Guide by avoiding any additional plugin	sas management console, sas enterprise guide, macro definition, macro variable	source pdf	8
313	Real AdaBoost: Boosting for Credit Scorecards and Similarity to WOE Logistic Regression	Paul Edwards	Adaboost is a machine learning algorithm that builds a series of small decision trees, adapting each tree to predict difficult cases missed by the previous trees and combining all trees into a single model. We will discuss the AdaBoost methodology and introduce the extension called Real AdaBoost. Real AdaBoost comes from a strong academic pedigree: its authors are pioneers of machine learning and the method has well-established empirical and theoretical support spanning 15 years. Practically speaking, Real AdaBoost is able to produce readable credit scorecards and offers attractive features including variable interaction and adaptive, stage-wise binning. We will contrast Real AdaBoost to the dominant methodology for creating credit scorecards: stepwise weight of evidence logistic regression (SWOELR)	logistic regression, machine learning, regression analysis, proc hpsplit, logistic distribution, scorecard, predictor variable, response variable, training set, macro parameter, data mining, data cleaning, likelihood function, proc step, validation set	source	12
314	Red Rover, Red Rover, Send Data Right Over: Exploring External Geographic Data Sources with SAS	Louise S. Hadden	The intrepid Mars Rovers have inspired awe and Curiousity - and dreams of mapping Mars using `SAS/GRAPH`. This presentation will demonstrate how to import SHP file data (using `PROC MAPIMPORT`) from sources other than SAS and GfK to produce useful (and sometimes creative) maps. Examples will include mapping neighborhoods, ZCTA5 areas, postal codes and of course, Mars. Products used are Base SAS and `SAS/GRAPH`. SAS programmers of any skill level will benefit from this presentation	proc step, proc gmap, response variable	source	9
315	Removing Duplicates Using SAS	Kirk Paul Lafler	We live in a world of data - small data, big data, and data in every conceivable size between small and big. In today's world data finds its way into our lives wherever we are. We talk about data, create data, read data, transmit data, receive data, and save data constantly during any given hour in a day, and we still want and need more. So, we collect even more data at work, in meetings, at home, using our smartphones, in emails, in voice messages, sifting through financial reports, analyzing profits and losses, watching streaming videos, playing computer games, comparing sports teams and favorite players, and countless other ways. Data is growing and being collected at such astounding rates all in the hopes of being able to better understand the world around us	proc step, proc sort, proc sql, proc means, group by	source	9
316	Removing Personally Identifiable Information	Stanley E. Legum	At the end of a project, many institutional review boards (IRBs) require project directors to certify that no personally identifiable information (PII) is retained by a project. This paper briefly reviews what information is considered PII and explores how to identify variables containing PII in a given project. It then shows a comprehensive way to ensure that all SAS variables containing PII have their values set to NULL and how to use SAS to document that this has been done	pii, libname statement, proc sql, txt, proc step	source	9
317	Reporting Rock Stars or Robots: They'll Never Know	Jacob Price	Do you ever feel like your office emails the same reports to the same people over, and over and over again? If your customers are anything like mine, you create reports, and lots of them. Our office is using macros, SAS email capabilities and other programming techniques, in conjunction with our trusty contact list, to automate report distribution. Customers now receive the data they need, and only the data they need, on the schedule they request. In addition, not having to send these emails out manually saves our office valuable time and resources that can be used for other initiatives. In this session, we will walk through a few of the SAS techniques we are utilizing to provide better service to our internal and external partners and, hopefully, make us look a little more like rock stars	macro variable, macro loop, do loop, end, proc step, filename statement, proc sql, macro definition, excel, macro conditional, dataset	source	15
318	Retracing My SAS Global Forum 2016 Steps: Visualizing iPhone Health App Step Data With a (What Else?) Step Plot	Ted Conway	If you've got an iPhone, you might have noticed that the Health app is hard at work collecting data on every step you take. And, of course, the data scientist inside you is itching to analyze that data with SAS. This paper and an accompanying E-Poster show you how to get step data out of your iPhone Health app and into SAS. Once it's there, you can have at it with all things SAS. In this presentation, we show you how a (what else?) step plot can be used to visualize the 73,000+ steps the author took at SAS Global Forum 2016	data science, sample	source pdf	8
319	Revenue Score: Forecasting Credit Card Products with Zero Inflated Beta Regression and Gradient Boosting	Marc Witarsa, Paulo Di Cellio Dias	Using the zero inflated beta regression and gradient boosting techniques (`PROC NLMIXED` and `PROC` treeboost), they have developed a solution for gross revenue forecast of credit card products. In this paper will be explored the solution of revenue score forecasting, divided in three sources of revenue: revolving, interchange, and products. Also will be explored the methods zero inflated beta regression and gradient boosting. Keywords: `PROC NLMIXED` , `PROC` treeboos t, z ero inflate d be ta regression , gradien t boosting , credi t `CARDS`, invoice variables, revenue	gradient boosting, regression analysis, response variable, proc step, credit card, predictor variable, sample, beta distribution, likelihood function, proc nlmixed, credit risk, proc means, outlier	source	20
320	Revolutionizing Statistical Computing in SAS with the Jupyter Notebook	Hunter Glanz	From state-of-the-art research to routine analytics, the Jupyter Notebook offers an unprecedented reporting medium. Historically, tables, graphics, and other types of output had to be created separately, and then integrated into a report piece by piece, amidst the drafting of text. The Jupyter Notebook interface enables you to create code cells and markdown cells in any arrangement. Markdown cells allow all typical formatting. Code cells can run code in the document. As a result, report creation happens naturally and in a completely reproducible way. Handing a colleague a Jupyter Notebook file to be re -run or revised is much easier and simpler for them than passing along, at a minimum, two files: one for the code and one for the text. Traditional reports become dynamic documents that include both text and living SAS code that is run during document creation. With the new SAS kernel for Jupyter, all of this is possible and more!	data science, sas log, txt	source	9
321	Run It in Parallel: Improving the Flow of Windows Services	David Kratz	SAS Job flows created by Windows operating system services have a problem: At present they can only execute jobs in series (one at a time). This can slow job processing down, and limits the utility of these flows. This paper shows how one can alter Windows operating system services flows after they have been generated to enable jobs to run in parallel (side by side). A high level overview of a SAS `PROC GROOVY` script which automates these changes is provided, as well as a summary of the positives and negatives of running jobs in parallel	proc step, proc groovy, parallel processing, sysrc	source zip	7
322	Sankey Diagram: A Compelling, Convenient, and Informational Path Analysis with SAS Visual Analytics	Abhilasha Tiwari	SAS Visual Analytics provides a complete platform for analytics visualization and exploration of the data. There are several interactive visualizations such as charts, histograms, heat maps, decision tree, and Sankey diagram. A Sankey diagram helps in performing the path analytics and offers a better understanding of complex data. It is a graphic illustration of flows from one set of values to another as a series of paths, where width of each flow represents the quantity. It is a better and more efficient way to illustrate which flows represent advantages and what flows are responsible for the disadvantages or losses. Sankey diagrams are named after Matthew Henry Phineas Riall Sankey, who first used this in a publication on energy efficiency of a steam engine in 1898. This paper begins with information regarding the essentials or parts of Sankey: nodes, links, drop-off links, and path	proc freq, proc hpsplit	source	17
323	SAS and Hadoop: The 6th Annual State of the Union	Paul Kent	The fourth maintenance release for SAS 9.4 and the new SAS Viya platform bring even more progress with respect to the interoperability between SAS and Hadoop (the industry standard for big data). This talk brings you up -to-date with where we are: more distributions, more data types, more options. And then there is the cloud. Come and learn about the exciting new developments for blending your SAS processing with your shared Hadoop cluster	sas viya, proc sql, proc step, cas, sql pass-through, dataset	source	16
324	SAS and UTF-8: Ultimately the Finest. Your Data and Applications Will Thank You!	Elizabeth Bales, Wei Zheng	SAS with Unicode UTF-8 encoding is ready to help you tackle the challenges of dealing with data in multiple languages. In today's global economy, software needs are changing. Companies are globalizing and consolidating systems from various parts of the world. Software must be ready to handle data from social media, international web pages, and databases that have characters in many different languages. SAS makes migrating your data to Unicode a snap! This paper helps you move smoothly from SAS using other encodings to the powerful SAS Unicode environment with UTF-8 support. Along the way, you will uncover secrets to successfully manipulate your characters, so that all of your data remains intact	proc step, character variable, sas library, string functions, libname statement, dataset, proc copy, sas log, constraint, format statement, proc contents, options statement, proc datasets, proc cimport, substr function, sample, proc cport, user-defined format	source	18
325	SAS Customer Intelligence 360 for Dummies	Fariba Bat-haee, Denise Sealy	Have you heard of SAS Customer Intelligence 360, the program for creating a digital marketing SaaS offering on a multi-tenant SAS cloud? Were you mesmerized by it but found it overwhelming? Did you tell yourself, "I wish someone would show me how to do this"? This paper is for you. This paper provides you with an easy, step-by-step procedure on how to create a successful digital web, mobile, and email marketing campaign. In addition to these basics, the paper points to resources that allow you to get deeper into the application and customize each object to satisfy your marketing needs	batch job, customer experience	source	15
326	SAS Data Integration Studio: Take Control with Conditional and Looping Transformations	Harry Droogendyk	SAS Data Integration Studio jobs are not always linear. While Loop transformations have been part of DI Studio for ages, only more recently has SAS Data Integration Studio included the Conditional Control transformations to control logic flow within a job. This paper will demonstrate the use of both the Loop and Conditional transformations in a real world example	macro variable, date variable, data step, dataset, txt, macro flow control	source	13
327	SAS Data Integration: a Capgemini Solution to Accelerate and Keeping It All 'in Sync'	Richard Hogenberg	A common issue in data integration is that often the documentation and the SAS data integration job source code start to diverge and eventually become out of sync. At Capgemini, working for a specific client, we developed a solution to rectify this challenge. We proposed moving all necessary documentation into the SAS Data Integration Studio job itself. In this way, all documentation then becomes part of the metadata we have created, with the possibility of automatically generating Job and Release documentation from the metadata. This presentation therefore focuses on the metadata documentation generator. Specifically, this presentation: 1) looks at how to use programming and documentation standards in SAS data integration jobs to enable the generation of documentation from the metadata 2) shows how the documentation is generated from the metadata, and the challenges that were encountered creating the code	data integration, sas base	source	6
328	SAS Hash Objects, Demystified	Dari Mazloom	The hash object provides an efficient method for quick data storage and data retrieval. Using a common set of lookup keys, hash objects can be used to retrieve data, store data, merge / join tables of data, and split a single table into multiple tables. This paper illustrates what a hash object is, why use hash objects, and basic programming instructions associated the construction and use of hash objects in a `DATA STEP`	hash table, in=	source	11
329	SAS In-Memory Analytics for Hadoop	Venkateswarlu Toluchuri	SAS In-Memory Statistics is an analytical programming environment that enables a user to address many components of an analytics project in a single environment, rather having to switch between different applications. Users can easily prepare the raw data for different types of analytics they can explore the data to enhance the information extractions. They can apply a large variety of statistical and machine learning techniques to the data to have different approaches strive. The model comparison capabilities let them quickly find the best model, which they can deploy and score in the Hadoop environment. All of these different components of the analytics project are supported in a distributed in-memory environment for lightning fas t processing. This paper aims to highlight the tips and my recent approaches on analytics experience to deal with `SAS/HDFS` data, it get loaded in to LASR Server to apply analytics models on top of that	proc step, sas base, dataset, sas studio, user-defined format	source pdf	11
330	SAS Macros for Binning Predictors with a Binary Target	Bruce S. Lund	Binary logistic regression models are widely used in CRM (customer relationship management) or credit risk modeling. In these models it is common to use nominal, ordinal, or discrete (NOD) predictors. NOD predictors typically are binned (reducing the number of their levels) before usage in a logistic model. The primary purpose of binning is to obtain parsimony without greatly reducing the strength of association of the predictor X to the binary target Y. In this paper two SAS macros are discussed. %NOD_BIN bins predictors with nominal values (as well as ordinal and discrete) by collapsing levels so as to maximize information value (IV). %ORDINAL_BIN is applied to predictors which are ordered and where collapsing can occur only for levels that are adjacent in the ordering of X. %ORDINAL_BIN finds all possible binning solutions by complete enumeration. These solutions are ranked by IV and monotonic solutions are identified. The purpose of this paper is to present these two macros	missing value, proc step, proc freq, character variable, numeric variable, proc print, likelihood function, proc hpsplit, proc rank, logistic distribution, sample, logistic regression	source	13
331	SAS Macros for Computing the Mediated Effect in the Pretest-Posttest Control Group Design	David P. MacKinnon	Mediation analysis is a statistical technique for investigating the extent to which a mediating variable transmits the relation of an independent variable to a dependent variable. Because it is useful in many fields, there have been rapid developments in statistical mediation methods. The most cutting-edge statistical mediation analysis focuses on the causal interpretation of mediated effect estimates. Cause - and-effect inferences are particularly challenging in mediation analysis because of the difficulty o f randomizing subjects to levels of the mediator (MacKinnon, 2008). The focus of this paper is how incorporating longitudinal measures of the mediating and outcome variables aides in the causal interpretation of mediated effects. This paper provides useful SAS tools for designing adequately powered studies to detect the mediated effect	sample, effect size, bootstrap, sample size, confidence interval, mediation analysis, power analysis, monte carlo, predictor variable, proc reg, proc step, regression analysis	source	10
332	SAS Macros: Advanced Techniques	Dari Mazloom	The SAS macro language provides a powerful tool to write a program once and re-use it many times in multiple places. A repeatedly-executed section of a program can be wrapped into a macro which can then be shared among many users. A practical example of a macro can be a utility that takes in a set of input parameters, performs some calculations, and sends back a result; such as an interest calculator. In general, a macro modularizes a program into smaller and more manageable sections, and encapsulates repetitive tasks into re-usable code. Modularization can help the code to be tested independently. This paper provides an introduction to writing macros. It introduces the user tothe basic macro constructs and statements	macro variable, macro put, data step, macro call, macro flow control, macro definition	source	7
333	SAS Masters: Exploratory Analysis of the Factors Related to Gun Mortality	Stephanie Mendoza, Gabrielle Ilenstine		proc hpforest, feature importance	source	23
334	SAS Metadata Security 201: Security Basics for a New SAS Administrator	Charyn Faenza	The purpose of this paper is to provide an overview of SAS metadata security for new or inexperienced SAS administrators. The focus of the discussion is on identifying the most common metadata security objects such as access control entries (ACEs), access control templates (ACTs), metadata folders, authentication domains, etc. and describing how these objects work together to secure the SAS environment. Based on a standard SAS Enterprise Office Analytics for Midsize Business installation in a Windows environment, this paper walks through a simple example of securing a metadata environment, which demonstrates how security is prioritized, the impact of each security layer, and how conflicts are resolved	access control, sas management console, sas enterprise guide, sas administrator, sas library	source	16
335	SAS Metadata Security 301: Auditing your SAS Environment	Michelle Homes, Charyn Faenza	You've got your SAS environments installed, configured, and running smoothly. Time to relax and put your feet up, right? Not so fast! There is still one more leg to go on your security journey. After the deployment of your initial security plan the security audit process provides active and regular monitoring and ensures your environment remains secure. There are many reasons to carry out security audits: for regulatory compliance, for business confidence, and to keeping your SAS platform as per the design specifications. This paper will at look some of the available ways to regularly review your environment to ensure protected resources are not at risk; to comply with security auditing requirements; and to answer quickly and easily the question "Who has access to what?" through efficient SAS metadata security management using Metacoda software	sas management console, sas enterprise guide, sas administrator, access control	source	23
336	SAS Visual Analytics to Inform FDA of Potential Safety Issues for CFSAN-Regulated Products	Manuel Kavekos	Web Intelligence is a business objects web-based application used by the FDA for accessing and querying data files, and ultimately creating reports from multiple databases. The system allows querying of different databases using common business terms, and in the case of the FDA's Center for Food Safety and Applied Nutrition (CFSAN), careful review of dietary supplement information. However, in order to create timely and efficient reports for detection of safety signals leading to adverse events, a more efficient system is needed to obtain and visually display the data. Using SAS Visual Analytics and SAS Enterprise Guide can assist with timely extraction of data from multiple databases commonly used by CFSAN and create a more user friendly interface for management's review to help make key decisions in prevention of adverse events for the public	sas enterprise guide, sas studio, adverse events, proc sql, sas visual analytics	source	5
337	SAS Visual Analytics Tricks We Learned from Reading Hundreds of SAS Community Posts	Patricia S. Aanderud, Ryan Kumpfmiller	After you know the basics of SAS Visual Analytics, you realize there are some situations that require unique strategies. Sometimes tables are not structured right or become too large for the environment. Maybe creating the right custom calculation for a dashboard can be confusing. Geospatial data is hard to work with if you haven't ever used it before. We looked through 100s of SAS Communities posts for the most common questions. These solutions (and a few extras) were extracted from the newly released	scenario analysis, sas base, sample, dataset	source	19
338	SAS Viya: What It Means for SAS Administration	Mark L. Schneider	Not only does the new SAS Viya platform br ing exciting advancements in high-performance analytics, it also takes a revolutionary step forward in the area of administration. The new SAS Cloud Analytic Services is accompanied by new platform management tools and techniques that are designed to ease the administrative burden while leveraging the open programming and visual interfaces that are standard among SAS Viya applications. Learn about the completely rewritten SAS Environment Manager 3.2, which supports the SAS Viya platform. It includes a cleaner HTML5-based user interface, more flexible and intuitive authorization windows, and user and group management that is integrated with your corporate Lightweight Directory Access Protocol (LDAP). Understand how authentication works in SAS Viya without metadata identities	sas viya, cas, sas 9, rest api, sas administrator, sas studio	source	14
339	SAS/ACCESS to PC Files: So Many Options for Microsoft Excel Files. Which Is Best for Me?	Joe Schluter, Henry Feldman	There are so many ways for `SAS/ACCESS` users to read and write data from and to Microsoft `EXCEL` files: `EXCEL`, SAS PC Files Server, XLS and XLSX engines, the SAS IMPORT and EXPORT procedures, various Microsoft `EXCEL` file formats (.xls, .xlsx, .xlsb, .xlsm), and many more. Many users ask, 'Which is best for me?' This paper explores the requirements and limitations of each engine, along with performance considerations and some not-so-obvious things to consider. It also includes a brief analogous discussion on Microsoft Access databases, which share some of the same mechanisms	excel, proc export, proc sql, dataset	source	8
340	SAS/GRAPH and GfK GeoMarketing Maps: a Subject Matter Expert Winning Combination	Louise S. Hadden	SAS has an amazing arsenal of tools to use and display geographic information that is relatively unknown and underutilized. High quality GfK Geocoding maps have been provided by SAS since SAS 9.3 M2, as sources for inexpensive map data dried up. SAS has been including both GfK and "traditional" SAS map data sets with licenses for `SAS/GRAPH` for some time, recognizing there will need to be an extended transitional period. However, for those of us who have been putting off converting our `SAS/GRAPH` mapping programs to use the new GfK maps, the time has come, as the "traditional" SAS map data sets are no longer being updated. If you visit SAS MapsOnline, you will find only GfK maps in current maps. The GfK maps are updated once a year. This presentation will walk through the conversion of a long-standing SAS program to produce multiple US maps for a data compendium to take advantage of GfK maps. Products used are Base SAS and `SAS/GRAPH`. SAS programmers of any skill level will benefit from this presentation	proc step, proc freq, create table, proc gmap, proc sql	source	21
341	SAS: A Unifying Tool That Manages Hospital and Research Pharmacy Data and Reporting	Robert MacArthur, Arman Altincatal	Hospital Information Technologists are faced with a dilemma: ho w to get the many pharmacy databases, dynamic datasets and software systems to communicate with each other and generate useful automated real time output. SAS serves as a unifying tool for our hospital pharmacy. It brings together data from multiple sources, generates output in multiple formats, analyzes trends, and generates summary reports to meet workload, quality and regulatory requirements. Datasets originate from multiple sourc es, including drug and device wholesalers, web based drug information systems, dumb machine output, pharmacy drug dispens ing platforms, hospital administration systems, and other sources	clinical trial, quality control, phase i, randomization	source pdf	8
342	Sentiment Analysis of Opinions about Self Driving Cars	Nachiket Kawitkar, Swapneel Deshpande	Presentation		source	4
343	Setting Relative Server Paths in SAS Enterprise Guide	Michelle Buchecker	Imagine if you will… a program, a program that loves its data to be in the same directory as the program itself. Together, in the same directory. True love. The program loves its data so much, it just refers to it by `FILENAME`. No need to say what directory the data is in; it is the same directory. Now imagine that program being thrust into the world of the grid. The grid knows not what directory this program resides. The grid is an uncaring, but powerful, soul. Yet, when the program is executing and the program refers to the data just by `FILENAME`, the grid bellows "nay, no path, no data". A knight in shining armor emerges, in the form of a SAS Macro, who says "lo, with the help of the Enterprise Guide macro variable minions, I can gift you with the location of the program directory and send that with you to yon mighty grid." And there was much rejoicing. Yay	macro variable, sas enterprise guide, sas enterprise guide project, macro definition, macro flow control, macro loop, %if-%then-%else	source	7
344	Share the Wealth of Information in SAS Environment Manager Logs	Jackie Iverson, Marty Flis	As a SAS administrator, have you ever wanted to look at data in SAS Environment Manager that spans a longer length of time? Has your management asked for access to this data so that they can use it to spot trends and make predictions? This paper shows you how to share the wealth of information found in SAS Environment Manager log data and explains how to save and store this data for use in SAS Visual Analytics. You will find tips on structuring data for easy analysis and examples of using the data to make business decisions	data mart, sas administrator	source	8
345	Shared File Systems: Determining the Best Choice for your Distributed SAS Foundation Applications	Margaret A. Crevar	If you are planning on deploying SAS Grid Manager and SAS Enterprise BI (or other distributed SAS Foundation applications) with load balanced servers on multiple operating systems instances, , a shared file system is required. In order to determine the best shared file system choice for a given deployment, it is important to understand how the file system is used, the SAS I/O workload characteristics performed on it, and the stressors that SAS Foundation applications produce on the file system. For the purposes of this paper, we use the term "shared file system" to mean both a clustered file system and shared file system, even though" shared" can denote a network file system and a distributed file system - not clustered	sas grid, grid computing, metadata management	source	11
346	Shipping Container Roulette	Dan Tamburro	Presentation	machine learning, aml, case management, entity resolution, policy, model deployment, macro function	source	29
347	Show Off Your OAuth	Joseph Henry	Applications and websites are heavily relying on web services to serve up vast amounts of data. With such a substantial reliance on the web and the rapid increase of security threats, security is a necessary concern. OAuth 2.0 has become a go-to method for websites to allow secure access to the services they provide; however, increased security inherently increases complexity. Accessing web services that use OAuth 2.0 is not entirely straightforward, and can cause many users plenty of frustration and confusion. OAuth can be simple to use and pairs well with Base SAS to access the most popular web services available	web service, sample, proc http	source	8
348	Simplified Project Management Using a SAS Visual Analytics Dashboard	Andre Watts, Danae Barulich	Presentation		source	3
349	Simplifying Your %DO Loop with CALL EXECUTE	Arthur X. Li	One often uses an iterative %DO loop to execute a section of a macro repetitively. An alternative method is to utilize the implicit loop in the `DATA STEP` with the EXECUTE routine to generate a series of macro calls. One of the advantages in the latter approach is eliminating the needs of using indirect referencing. To better understand the use of the CALL EXECUTE, it is essential for programmers to understand the mechanism and the timing of macro processing to avoid programming errors. These technical issues will be discussed in detail in this paper	call execute, macro loop, macro call, do loop, macro facility	source	5
350	Snail Mail to Auto-Emails: Generating Emails from SAS with Attachments	Crystal Carel	Do you get tired of constantly creating new emails each and every time you run a report, frantically searching for the reports, attaching said reports, and writing emails while thinking there has to be a better way? Then have I got some code to share with you! This session will provide you with code to flee your old ways of emailing data and reports and instead have you set up your SAS code to send an email to your recipients that has attached the most current file(s) each and every time the code is ran without having to manually do anything after you run your SAS code. This session will provide a SAS programmer with instruction on how to create your own email that is in a macro that is based on your current report	macro variable, macro call	source	7
351	So You Think You Can Combine Data Sets?	Christopher J. Bost	The syntax to combine SAS data sets is simple: use the `SET` statement to concatenate, and use the `MERGE` statement to merge. The data sets themselves, however, might be complex. Combining the data sets might not result in what is needed. This paper reviews techniques to perform before combining data sets, including checking for: common variables; common variables with different attributes; duplicate identifiers; duplicate observations; and acceptable match rates	proc step, proc sql, group by, proc freq, missing value, create table, proc sort, order by, proc print, by group processing, credit card, set statement, where statement, raw data, proc contents	source	23
352	Some Tricks in Graph Template Language	Amos Shu	The SAS 9.4 Graph Template Language (GTL) Reference book has more than 1300 pages and hundreds of options and statements. It is no surprise that programmers sometimes experience unexpected twists and turns when using the GTL to draw figures. Understandably, it is easy to get frustrated when your program fails to produce the desired graphs despite your best effort. While SAS needs to continue improving the GTL product, this paper offers several tricks that help overcome some of the roadblocks in graphing	graph template language, bar chart	source	6
353	Something for Nothing? Adding Flag Variables Using Group Descriptive Statistics and PROC SQL	Sunil Kumar Gupta	Can you actually get something for nothing? With `PROC SQL`'s subquery and remerging features, yes, you can. When working with categorical variables, often there is a need to add flag variables based on group descriptive statistics such as group counts, minimum and maximum values. Instead of first creating the group count, minimum or maximum values and then merging the summarized dataset to the original dataset with conditional statements creating a flag variable, why not take advantage of `PROC SQL` to complete three steps in one? With `PROC SQL`'s subquery, CASE-WHEN clause and summary function s by the group variable, you can easily remerge the new flag variable back to the original dataset	proc sql, proc means, proc step, group by, class variable, create table, by group processing, numeric variable, flag variable	source	8
354	Stacked Ensemble Models for Improved Prediction Accuracy	Funda Güneş, Russell D. Wolfinger, Pei-Yi Tan	Ensemble modeling is now a well-established means for improving prediction accuracy; it enables you to average out noise from diverse models and thereby enhance the generalizable signal. Basic stacked ensemble techniques combine predictions from multiple machine learning algorithms and use these predictions as inputs to second-level learning models. This paper shows how you can generate a diverse set of models by various methods such as forest, gradient boosted decision trees, factorization machines, and logistic regression and then combine them with stacked-ensemble techniques such as hill climbing, gradient boosting, and nonnegative least squares in SAS Visual Data Mining and Machine Learning. The application of these techniques to real-world big data problems demonstrates how using stacked ensembles produces greater prediction accuracy and robustness than do individual models. The approach is powerful and compelling enough to alter your initial data mining mindset from finding the single best model to finding a collection of really good complementary models. It does involve additional cost due both to training a large number of models and the proper use of cross validation to avoid overfitting. This paper shows how to efficiently handle this computational expense in a modern SAS environment and how to manage an ensemble workflow by using parallel computation in a distributed framework	gradient boosting, cas action	source	19
355	Statistical Volunteering With SAS - Experiences and Opportunities	David J. Corliss	This presentation brings together experiences from SAS professionals working as volunteers for organizations, charities, and in academic research. Pro bono work, much like that done by physicians, attorneys and professionals in other areas is rapidly growing in statistical practice as an important part of a statistical career, offering the opportunity to utilize your skills in a places where they are so needed but cannot be supported in a for-pay position. Statistical volunteers also gain important learning experiences, mentoring, networking, and other opportunities for professional development. The presenter will share experiences from volunteering for local charities, NGO's and other organizations and causes, both in the US and around the world. The mission, methods and focus of some organizations are presented, including DataKind, Statistics Without Borders, Peace-Work and others	data science, cluster analysis	source	8
356	Step Through Your DATA Step: Introducing the DATA Step Debugger in SAS Enterprise Guide	Joe M. Flynn	Have you ever run SAS code with a `DATA STEP` and the results are not what you expected? Tracking down the problem can be a time-consuming task. To assist you in this common scenario, SAS Enterprise Guide 7.13 added a `DATA STEP` debugger tool. The simple and interactive `DATA STEP` debugger allows you to visually walk through the execution of your `DATA STEP` program. You can control the `DATA STEP` execution, view the variables, and set breakpoints to quickly identify data and logic errors. Come see the full capabilities of the new SAS Enterprise Guide `DATA STEP` debugger. You'll be squashing bugs in no time!	sas enterprise guide, input statement, delete, by group processing	source	14
357	Step Up Your Statistical Practice with Today's SAS/STAT Software	Robert N. Rodriguez, Phil Gibbs	Has the rapid pace of `SAS/STAT` releases left you unaware of powerful enhancements that could make a difference in your work? Are you still using `PROC REG` rather than `PROC GLMSELECT` to build regression models? Do you understand how the `GENMOD` procedure compares with the newer GEE and HPGENSELECT procedures? When should you turn to `PROC ICPHREG` rather than `PROC PHREG` for survival modeling? This paper will increase your awareness of modern tools in `SAS/STAT` by providing high-level comparisons with well-established tools and explaining the benefits of enhancements and new procedures. The paper focuses on new tools in the areas of regression model building, generalized linear models, survival analysis, and mixed models. When you see the advantages of these tools, you will want to put them into practice	regression analysis, proc step, proc quantreg, survival analysis, proc mixed, proc phreg, binomial distribution, poisson distribution, proc genmod, proc reg, proc glmselect, hazard function, proc hpgenselect, proc quantlife, data cleaning, likelihood function, logistic distribution, proc glm	source	19
358	Stored Processes or How to Make You Use SAS Without Even Knowing It!	Edmund Lee	Dealing with analysts and managers who do not know how to or want to use SAS can be quite tr icky if everything you are doing uses SAS. This is where stored processes using SAS Enterprise Guide comes in handy. Once you know what they want to get out of the code, prompts can be defined in a smart and flexible way to give all users (whether they are SAS or not) full control over the outpu t of the code . The key is having code that requires minimal maintenance and for you to be very flexible so that you can accommodate anything that the user comes up with. This session provides examples of credit risk stress testing where loss forecasting results were presented using different levels. Results were driven by a stored process prompt using a simple `DATA STEP`, `PROC SQL`, and `PROC REPORT`. This fun ctiona lity can be used in other industries where data is shown using different levels of granularity	stored procedure, sas enterprise guide, excel, proc step, proc report	source	13
359	Streamline Your Workflow: Integrating SAS, LaTeX, and R into a Single Reproducible Document	Lucy D'Agostino McGowan	There is an industry-wide push toward making workflows seamless and reproducible. Incorporating reproducibility into the workflow has many benefits; among them are increased transparency, time saving, and accuracy. We walk through how to seamlessly integrate SAS, LATEX, and R into a single reproducible document. We also discuss best practices for general principles such as literate programming and version control	logistic distribution, proc export	source	10
360	Stress Testing and Supplanting the LOCK Statement: Using Mutex Semaphores for Reliable File Locking	Troy Martin Hughes	The SAS LOCK Statement was introduced in SAS 7 with great pomp and circumstance, as it enabled SAS software to lock data sets exclusively. In a multiuser or networked environment, an exclusive file lock prevents other users and processes from accessing and accidentally corrupting a data set while it is in use. Moreover, because file lock status can be tested programmatically with the LOCK statement return code (&SYSLCKRC), data set accessibility can be validated before attempted access, thus preventing file access collisions and facilitating more reliable, robust software. Notwithstanding the intent of the LOCK statement, stress testing demonstrated in this text illustrates vulnerabilities in the LOCK statement that render its use inadvisable due to its inability to lock data sets reliably outside of the `SAS/SHARE` environment	parallel processing, stress testing, txt, dataset, sysrc, sas base, proc freq, cpu time, sample, macro variable, likelihood function, sas enterprise guide, %if-%then-%else	source	28
361	String Search in SAS Visual Analytics Records	Robbert Rahamat	It is possible to create an in-memory record search in SAS Visual Analytics (SAS VA), on an existing or uploaded table, allowing you or other users to quickly search for records containing certain string values somewhere in that table. The fields that you want to search, need to be selected and constructed into the solution, but can be changed. It is possible to search with multiple string values. Each additional string to search for, will narrow the search	sas visual analytics, character variable	source	4
362	Student Development and Enrollment Services Dashboard at the University of Central Florida	Carlos Piemonti	At the University of Central Florida (UCF), Student Development and Enrollment Services (SDES) combined efforts with Institutional Knowledge Management (IKM), which is the official source of data at UCF, to venture in a partnership to bring to life an electronic version of the SDES Dashboard at UCF. Previously, SDES invested over two months in a manual process to create a booklet with graphs and data that was not vetted by IKM; upon review, IKM detected many data errors plus inconsistencies in the figures that had been manually collected by multiple staff members over the years. The objective was to redesign this booklet using SAS Web Report Studio. The result was a collection of five major reports. IKM reports use SAS Business Intelligence (BI) tools to surface the official UCF data, which is provided to the State of Florida. Now it just takes less than an hour to refresh these reports for the next academic year cycle	lookup, fact table	source	9
363	Submitting Code in the Background Using SAS Studio	Jennifer Jeffreys-Chen, Amy Peters, Swapnil Ghan	As a SAS programmer, how often does it happen that you would like to submit some code but not wait around for it to finish? SAS Studio has a way to achieve this and much more! This paper covers how to submit and execute SAS code in the background using SAS Studio. The Background Submit feature in the SAS Studio interface enables you to submit code and continue with your work. You receive a notification when the job is finished, or you can disconnect from your browser session and check the status of the submitted code later. These techniques make it much easier to start long-running jobs and still get your other work done in the meantime	sas studio, mprint	source	21
364	Supplier Negotiations Optimized with SAS Enterprise Guide: Save Time and Money	Cameron Jagoe, Denise J. McManus	Presentation	sas enterprise guide	source	10
365	Survival Analysis of Lung Cancer Patients Using PROC PHREG and PROC LIFETEST	Yan Wang	Presentation	proc phreg, survival analysis, proc step	source	4
366	Tables and Graphics That Will FREQ You Out	Kathryn McLawhorn	Did you know that you could leverage the statistical power of the `FREQ` procedure and still be able to control the appearance of your output? Many people think they have to use procedures such as REPORT and TABULATE to be able to apply style options and control formats and headings for their output. However, if you pair `PROC FREQ` with a `TEMPLATE` procedure step, you can customize the appearance of your output and make enhancements to tables, such as adding colors and controlling headings. If you are a statistician, you know the many `PROC FREQ` options that produce high-level statistics. But did you also know that `PROC FREQ` can generate a graphical representation of those statistics? `PROC FREQ` can generate the graphs, and then you can use `ODS` Graphics and the Graph Template Language (GTL) to improve the appearance of the graphs. Written for intermediate users, this paper demonstrates how you can enhance the default output for `PROC FREQ`'s one-way and multi-way tables by modifying colors, formats, and labels. This paper also describes the syntax for creating graphs for multiple statistics, and it uses examples to show how you can customize these graphs	proc step, proc freq, proc template, cumulative frequency, odds ratio, ods graphics, output delivery system, label statement, graph template language, missing value, sample	source	21
367	Tag, You're It! Creating Accessible (Tagged) PDF Files with SAS 9.4 Output Delivery System	Glen R. Walker	In the game of tag, being "it" is bad, but where accessibility compliance is concerned, being tagged is good! Tagging is required for PDF files to comply with accessibility standards such as Section 508 and the Web Content Accessibility Guidelines (WCAG). In the fourth maintenance release for SAS 9.4, the preproduction option in the `ODS PDF` statement, ACCESSIBLE, creates a tagged PDF file. We look at how this option changes the file that is created and focus on the SAS programming techniques that work best with the new option. You'll then have the opportunity to try it yourself in your own code and provide feedback to SAS	proc step, proc report, ods pdf, output delivery system, proc print, bar chart	source	16
368	Tales from the Help Desk: Solutions to Common DATA Step Tasks	Bruce F. Gilsen	In 32 years as a SAS consultant at the Federal Reserve Board, I have seen some questions about common SAS tasks surface again and again. This paper collects the most common questions related to basic `DATA STEP` processing from my previous "Tales from the Help Desk" papers, and provides code to explain and resolve them. The following tasks are reviewed	sysdate9, date variable, lag function, macro variable, temporary array, character variable, if-then-else, input statement, numeric variable, proc sql, proc step, put statement, file statement, missing value, numeric functions	source	19
369	Telling the Story of Your Process with Graphical Enhancements of Control Charts	Bucky Ransdell	Have you ever used a control chart to assess the variation in a process? Did you wonder how you could modify the chart to tell a more complete story about the process? This paper explains how you can use the `SHEWHART` procedure in `SAS/QC` software to make the following enhancements: display multiple sets of control limits that visualize the evolution of the process, visualize stratified variation, explore within-subgroup variation with box-and- whisker plots, and add information that improves the interpretability of the chart. The paper begins by reviewing the basics of control charts and then illustrates the enhancements with examples drawn from real-world quality improvement efforts	phase i, phase ii, sample, proc step	source	13
370	Template Versatility: Using SAS Macro Language to Generate Dynamic RTF Reports	Patrick Leon	Presentation	macro facility	source	5
371	Temporal Text Mining: A Thematic Exploration of Don Quixote	Ray Wright	Temporal text mining (TTM) is the discovery of temporal patterns in documents that are collected over time. It involves discovery of latent themes, construction of a thematic evolution graph, and analysis of thematic patterns. This paper uses text mining and time series analysis techniques to explore Don Quixote de la Mancha, a two-volume master work of Western literature. First, it uses singular value decomposition in SAS Text Miner to discover 25 key themes that characterize the two volumes. Then it treats the chapters of the two books as time-ordered documents and creates a semiautomated visual summary of the two volumes. It also explores the trajectory of individual themes over the course of the chapters and identifies episodes, recurring themes, and climaxes. Finally, it uses time series clustering in SAS Enterprise Miner to group chapters that have similar themes and to group themes that have similar trajectories	time series analysis, dimensionality reduction, data mining	source	16
372	Ten Tips to Unlock the Power of Hadoop with SAS	Wilbram Hazejager, Nancy A. Rausch	This paper discusses a set of practical recommendations for optimizing the performance and scalability of your Hadoop system using SAS. Topics include recommendations gleaned from actual deployments from a variety of implementations and distributions. Techniques cover tips for improving performance and working with complex Hadoop technologies such as YARN, techniques for improving efficiency when working with data, methods to better leverage the SAS in Hadoop components, and other recommendations. With this information, you can unlock the power of SAS in your Hadoop system	proc sql, sas library, libname statement, sas management console, big data, proc step, data wrangling	source	17
373	Testing the Night Away	Laurent de Walick, Bas Marsman, Stephan Minnaert	Testing is a weak spot in many data warehouse environments. A lot of the testing is focused on the correct implementation of requirements. But due to the complex nature of analytics environments, a change in a data integration process can lead to unexpected results in totally different and untouched areas. We developed a method to identify unexpected changes often and early by doing a nightly regression test. The test does a (complete) ETL run, compares all output from the test to a baseline, and reports all the changes. This paper describes the process and the SAS code needed to back up existing data, trigger ETL flows, compare results, and restore situations after a nightly regression test. We also discuss the challenges we experienced while implementing the nightly regression test framework	proc step, proc contents, create table, full join, test set, regression analysis	source	13
374	Text Generation Data Sets (Text GDS)	Kannan Deivasigamani	This paper offers a way to fill the void that SAS currently has with respect to the missing feature in the language, to create generational datasets of TEXT files that is identical to the syntax available to create SAS-Generational-Data-Sets (SAS GDS) especially in UNIX & AIX environments	macro variable, macro call, txt, macro parameter	source pdf	7
375	Text Mining of Movie Synopsis by SAS Enterprise Miner	Yiyun Zhou	This project described the method to classify movie genres based on synopses text data by two approaches：term frequency and inverse document frequency (tf-idf) and C4.5 decision tree. Using the performance comparison of the classifiers by manipulating the different parameters, the strength and improvement of this method in substantial text analysis were also interpreted. As the result, these two approaches are powerful to identify movie genres. Data The dataset was downloaded from the Public IMDB dataset which contained 1527 synopses and 10 genres: action, comedy, documentary, drama, horror, kids/family, mystery, romance, scifi, and suspense. The dataset contained the synopsis of reviews on each movie, and the movies were assigned to a maximum of 5 gen res based on relevancy of the movie's content	proc hpsplit, proc freq, data mining	source pdf	3
376	The Architecture of the SAS Cloud Analytic Services in SAS Viya	Jerry C. Pendergrass	SAS Cloud Analytic Services (CAS) is the cloud-based run-time environment for data management and analytics in SAS. By run-time environment, we refer to the combination of hardware and software where data management and analytics take place. CAS is a platform for high-performance analytics and distributed computing. CAS provides data management and an analytics framework that can run in the cloud, that can act as a cloud, and that provides the best-in-class analytics that SAS is known for. This new architecture functions as a public API, allowing access from many different clients such as Lua, Python, Java, REST, and yes, even SAS. CAS provides user-level sessions, which provides security, allows the user to share data between sessions, and provides fault tolerance. Fault tolerance allows a worker node to crash without losing data and allows the user request to continue running to completion	access control, proc step, by group processing, parallel processing, proc genmod	source	18
377	The Art of Overlaying Graphs to Create Advanced Visualizations	Vineet Raina	SAS provides an extensive set of graphs for different needs. But as a SAS programmer or someone who uses SAS Visual Analytics Designer to create reports, the number of possible scenarios you have to address outnumber the available graphs. This paper demonstrates how to create your own ad vanced graphs by intelligently combining existing graphs	series plot, scatter plot, standard deviation, bar chart	source	20
378	The Development and Application of a Composite Score for Social Determinants of Health	Paul A. LaBrec, Ryan C. Butterfield	Socioeconomic status (SES) is a major contributor to health disparities in the United States. Research suggests that those with a low SES versus a high SES are more likely to have lower life expectancy; participate in unhealthy behaviors such as smoking and alcohol consumption; experience higher rates of depression, childhood obesity, and ADHD; and experience problems accessing appropriate health care. Interpreting SES can be difficult due to the complexity of data, multiple data sources, and the large number of socioeconomic and demographic measures available. When SES is expanded to include additional social determinants of health (SDOH) such as language barriers and transportation barriers to care; access to employment and affordable housing; adequate nutrition, family support and social cohesion; health literacy; crime and violence; quality of housing; and other environmental conditions, the ability to measure and interpret the concept becomes even more difficult	proc step, proc reg, risk adjustment, regression analysis, confirmatory factor analysis, insurance claims, proc princomp, correlation, factor analysis, proc factor, heatmap, proc sgplot, scree plot, stage 1, r-squared, sample	source	16
379	The Effects of Socioeconomic, Demographic Variables on US Mortality Using SAS Visual Analytics	Catherine Loveless-Schmitt	The NLMS is a database developed for the purpose of studying the effects of demographic and socio-economic characteristics on differentials in US mortality rates. The NLMS is based on a multistage stratified sample of the non-institutionalized population of the United States. The NLMS consists of US Census Bureau data from Current Population Survey (CPS) and a subset of the 1980 Census, combined with state-based death certificate information to identify mortality status and cause of death. The file contains a subset of the 39 NLMS cohorts included in the full NLMS that is followed prospectively for 11 years. The file contains approximately 1,222,000 records with over 112,000 identified mortality cases. This presentation demonstrates the differential effects of mortality rates in visual displays	proc hpsplit, sas visual analytics, data science, outlier, regression analysis, likelihood function	source pdf	22
380	The Elusive Data Scientist: Real-World Analytic Competencies	Gregory S. Barnes Nelson	You've all seen the job posting which looks more like an advertisement for the ever-elusive unicorn. They begin by outlining the required skills that include a mixture of tools, technologies and masterful "things that you should be able to do." Unfortunately, many such postings begin with restrictions to those with advanced degrees in math, science, statistics, or computer science and experience in your specific industry. They must be able to perform predictive modeling, natural language processing and, for good measure, candidates should only apply if they know artificial intelligence, cognitive computing, and machine learning. The candidate should be proficient in SAS, R, Python, Hadoop, ETL, real-time, in-cloud, in-memory, in-database and must be a master storyteller. I know of no-one who would be able to fit that description and still be able to hold a normal conversation with another human	machine learning, artificial intelligence	source	14
381	The Flamingos: NFL Data Analytics For A New Era	Jonah Muresan, Daniel Savage, Gus Moir		logistic regression, logistic distribution, regression analysis	source	14
382	The Future of Transpose: How SAS Is Rebuilding its Foundation by Making What Is Old New Again	Scott Mebust	As computer technology advances, SAS continually pursues opportunities to implement state-of-the-art systems that solve problems in data preparation and analysis faster and more efficiently. In this pursuit, we have extended the `TRANSPOSE` procedure to operate in a distributed fashion within both Teradata and Hadoop, using dynamically generated DS2 executed by the SAS Embedded Process and within SAS Viya, using its native transpose action. With its new ability to work within these environments, `PROC TRANSPOSE` provides you with access to its parallel processing power and produces results that are compatible with your existing SAS programs	proc transpose, proc step, sas log, by group processing, cas, sas viya, hash table, options statement, casl, parallel processing, proc sql	source	16
383	The Ins and Outs of %IF	Michelle Buchecker	Have you ever had your macro code not work and you couldn't figure out why? Even something as simple as %if &sysscp=WIN %then `LIBNAME` libref 'c:\temp'; ? This paper is designed for programmers who know `%LET` and can write basic macro definitions already. Now let's take your macro skills a step farther by adding to your skillset. The %IF statement can be a deceptively tricky statement due to how we are used to thinking IF statements are processed in the `DATA STEP` and how that differs from how %IF statements are processed by the macro processor. Focus areas of this paper will be: Emphasizing the importance of the macro facility as a code generation facility How a `DATA STEP` IF statement differs from a macro %IF statement and when to use which * Why semicolons can be misinterpreted on a %IF statement	%if-%then-%else, if-then-else, macro facility, macro variable, keep statement, macro quoting function, proc step, macro definition	source	6
384	The JSON LIBNAME Engine: Real-World Applications Using Open APIs	Michael Drutar, Eric Thies	JSON is quickly becoming the industry standard for data interchanges, especially in supporting REST APIs. But until now, importing JSON content into SAS software and leveraging it in SAS has required significant custom code. Developing that code can be laborious, requiring transcoding, manual text parsing, and creating handlers for unexpected structure changes. Fortunately, the new JSON `LIBNAME` engine (in the fourth maintenance release for SAS 9.4 and later) delivers a robust, efficient method for importing JSON content into SAS data structures. This paper demonstrates several real-world examples of the JSON `LIBNAME` engine using open data APIs. The first example contrasts the traditional custom code and the JSON `LIBNAME` engine approach using big data from the United Nations Comtrade Database. The two approaches are compared in terms of complexity of code, time to execute, and the resulting data structures	libname engine, sas programmer, filename statement, proc step, libname statement, proc http, txt, proc json	source	10
385	The ODS EXCEL statement: Tips and Tricks for the TABULATE and REPORT Procedures	Richard A. DeVenezia	You might scream in pain or cry with joy that SAS software can directly produce output as `EXCEL` workbooks in .xlsx format. `EXCEL` is an excellent vehicle for delivering large amounts of summary information that needs to be partitioned for human review, exploratory filtering and sorting. `ODS` `EXCEL` as a production destination means there is dedicated SAS support for it. This paper will discuss using `ODS` `EXCEL` and `PROC TABULATE` in the domain of summarizing cross-sectional data extracted from a medical claims database. The discussion will cover data preparation, report preparation and tabulation statements such as CLASS, CLASSLEV and TABLE. The effects of STYLE options and the TAGATTR sub-option for inserting `EXCEL` specific features such as formulas, formats and alignment will be covered in detail. A short discussion of reusing these concepts in `PROC REPORT` statements such as DEFINE, COMPUTE and CALL DEFINE will also be covered	excel, user-defined format, proc step, nominal variable, sample, binary variable, proc tabulate, macro definition, class variable, interval variable, label statement, left join	source	17
386	The Orange Lifestyle	Mohit Singhi	Being a freshman at a large university, life can be fun as well as stressful. The choices a freshman makes while in college may impact his/her overall health. In order to examine the overall health and different behaviors of students at Oklahoma State University a survey was conducted among the freshmen students. The survey focused on capturing the psychological, environmental, diet, exercise, alcohol and drug use among students. A total of 795 out of 1,036 freshman students completed the survey which included around 270 questions that covered the range of issues mentioned above. An exploratory factor analysis identified 26 factors. For example, two factors that relate to the behavior of students under stress are eating and relaxing. Further understanding the variables which contribute to alcohol and drug use may help the university in planning appropriate interventions and preventions	factor analysis, pearson correlation	source	10
387	The REPORT Procedure and ODS Destination for Microsoft Excel: The Smarter, Faster Way to Create First-Rate Excel Reports	Jane Eslinger	Does your job require you to create reports in Microsoft `EXCEL` on a quarterly, monthly, or even weekly basis? Are you creating all or part of these reports by hand, referencing another sheet containing rows and rows and rows of data? If so, stop! There is a better way! The new `ODS` destination for `EXCEL` enables you to create native `EXCEL` files (XLSX) directly from SAS. Now you can include just the data you need, create great-looking tabular output, and do it all in a fraction of the time! This paper shows you how to use `PROC REPORT` to create polished tables that contain formulas, colored cells, and other customized formatting. Also presented in the paper are the destination options used to create various workbook structures, such as multiple tables per worksheet. Using these techniques to automate the creation of your `EXCEL` reports will save you hours of time and frustration, enabling you to pursue other endeavors	excel, proc report, proc step, character variable, numeric variable, output delivery system	source	20
388	The Rise of Chef Curry: Studying Advanced Basketball Metrics with Quantile Regression in SAS	Taylor K. Larkin, Denise J. McManus	Presentation	proc quantreg, regression analysis	source	8
389	The Rise of the Machines, Analytics, and Humans: Artificial Intelligence in the Age of the IIoT	Kathy Ball, Robert Silverman	In the next 10 years, the Industrial Internet of Things (IIoT) will dramatically alter nearly all sectors of the industrial economy, which accounts for nearly two-thirds of the global gross domestic product, according to findings at the World Economic Forum in Davos, Switzerland. The IIoT will radically change how humans, machines and our current infrastructure operate to achieve results and compete in the new digital world. Considered a disruptive technology, the IIoT will create new value streams for industries, including automated decisions and reactions in real time, massively improved operational efficiencies, connected infrastructure platforms and much better interaction between machines and humans	deep learning, machine learning, neural network, artificial intelligence	source	10
390	The SAS Ecosystem - A Programmer's Perspective	Thomas E. Billings	You may encounter people who used SAS long ago (perhaps in university) or through very limited use in a job . Some of these people with limited knowledge /experience think that the SAS system is "just a statistics package" or "just a GUI" , the latter usually a reference to SAS Enterprise Guide or if a dated reference , to (legacy) `SAS/AF` or `SAS/FSP` applications. The reality is that the modern SAS system is a very large , complex ecosystem , with hundreds of software products and a diversity of tools for programmers and users. This poster provides a set of diagrams and tables that illustrate the complexity of the SAS system , from the perspective of a programmer	proc step, proc sql, sas base, hadoop, macro facility	source	1
391	The SAS Visual Analytics Environment: Behind the Scenes	Beena Mathew, Zuzu Williams, Amy Gabig	As a SAS Visual Analytics administrator, how do you efficiently manage your SAS LASR environment? How do you ensure reliable data availability to your end users? How do you ensure that your users have the proper permissions to perform their tasks in SAS Visual Analytics? This paper covers some common management issues in SAS Visual Analytics, why and how they might arise, and how to resolve them. It discusses methods of programmatically managing your SAS LASR Analytic Server and tables, as well as using SAS Visual Analytics Administrator. Furthermore, it provides a better understanding of the roles in SAS Visual Analytics and demonstrates how to set up appropriat e user permissions. Using the methods discussed in this paper can help you improve the end-user experience as well as system performance	proc step, sas management console, sas enterprise guide	source	12
392	The Three Amigos Factors: Determining Term Deposit Purchases: How a Bank Can Get Other People's Money	Gina Colaianni, Bogdan Gadidov, Matthew Mitchell		logistic regression, regression analysis, logistic distribution, data cleaning, proc hpsplit, validation set, odds ratio, roc curve, nominal variable, interval variable, predictor variable, confidence interval, sample, training set, likelihood function, histogram, proc freq	source	59
393	The Top Ten SAS Studio Tips for SAS Grid Manager Administrators	Edoardo Riva	SAS environments are evolving in multiple directions. Modern web interfaces such as SAS Studio are replacing the traditional SAS Display Manager system. At the same time, distributed analytic computing, centrally managed by SAS Grid Manager, is becoming the standard topology for many enterprises. SAS administrators are faced with the task of providing business users properly configured, tuned, and monitored applications. The tips included in this paper provide SAS administrators with best practices to centrally manage SAS Studio options and repositories, proper grid tuning, effective monitoring of user sessions, high-availability considerations and more	sas studio, sas grid, sas base, sas enterprise guide	source	16
394	The Truth Is Out There: Leveraging Census Data Using PROC SURVEYLOGISTIC	Richard Dirmyer	The advent of robust and thorough data collection has resulted in the term "big data." With census data become richer, more nationally representative, and voluminous, we need methodologies that are designed to handle the manifold survey designs that Census data sets implement. The relatively nascent `PROC SURVEYLOGISTIC`, an experimental procedure in SAS 9 and fully supported in SAS 9.1, addresses some of these methodologies, including clusters, strata, and replicate weights. `PROC SURVEYLOGISTIC` handles data that are not a straightforward random sample. Using a Census data set, this paper provides examples highlighting the appropriate use of survey weights to calculate various estimates, as well as the calculation and interpretation of odds ratios between categorical variable interactions when predicting a binary outcome	odds ratio, proc surveylogistic, nominal variable, proc step, predictor variable, logistic regression, likelihood function, sample, logistic distribution, regression analysis	source	5
395	The Well-Equipped Student: Using SAS University Edition and E-Learning to Gain SAS Skills	Randy Mullis, Allison Mahaffey	SAS programming skills are much in-demand, and numerous free tools are available for students who want to develop those skills. This paper introduces students to SAS Studio and the Jupyter Notebook interface within SAS University Edition. To make this introduction more tangible, the paper uses a large data set of baseball statistics as an example. In particular, statistical analysis using SAS Studio examines the relationship between salary and performance for major leaguers. From importing text files to creating basic statistics to doing a more advanced analysis, this paper shows multiple ways to carry out tasks so that you can choose whichever method works best for you. Additional statistics that use t tests and linear regression are simple with SAS University Edition. For completeness, the paper shows the same code that is used in SAS Studio examples in the context of Jupyter Notebook in SAS University Edition	sas studio, scatter plot, sas programming, proc sql, proc step, regression analysis, macro definition, data science, macro facility	source	19
396	Time Series Analysis and Forecasting in SAS University Edition	Christopher Battiston	Time series analysis and forecasting have always been popular as businesses realize the power and impact they can have. Getting students to learn effective and correct ways to build their models is key to having successful analyses as more graduates move into the business world. Using SAS University Edition is a great way for students to learn analysis, and this talk focuses on the time series tasks. A brief introduction to time series is provided, as well as other important topics that are key to building strong models	time series analysis, sample, proc sql, imputation, missing value, proc step	source	10
397	Time-Frequency Analysis Using SAS	Michael J. Leonard, Wei Xiao, Arin Chaudhuri	Many organizations need to analyze large numbers of time series that have time-varying or frequency-varying properties (or both). The time-varying properties can include time-varying trends, and the frequency-varying properties can include time-varying periodic cycles. Time-frequency analysis simultaneously analyzes both time and frequency; it is particularly useful for monitoring time series that contain several signals of differing frequency. These signals are commonplace in data that are associated with the Internet of Things (IoT). This paper introduces techniques for large-scale time-frequency analysis and uses SAS Visual Forecasting and `SAS/ETS` software to demonstrate these techniques	time series analysis, proc freq, frequency analysis, windowing, heatmap, time series data, series plot, proc princomp	source	5
398	Timing is Everything: Detecting Important Behavior Triggers	Daymond Ling	Predictive analytics is powerful in its ability to predict likely future behavior and is used widely in marketing. However, the adage "timing is everything" continues to hold true-t he right customer and the right offer at the wrong time is less than optimal. In life, timing matters a great deal, but predictive analytics seldom take timing into account explicitly, we should be able to do better. Financial service consumption changes often have precursor changes in behavior, and a behavior change can lead to multiple subsequent consumption changes. One way to improve our awareness of the customer situation is to detect significant events that have meaningful consequences that warrant proactive outreach. This paper presents the use of Change Point Analysis in event detection that has proven to work successfully. Real case studies are discussed to illustrate the approach and implementation	time series analysis, type i error, credit card, interest rate, time series data, standard deviation, predictive analytics, outlier	source	12
399	Tips for Mastering Relational Databases Using SAS/ACCESS	Andrew W. Clapson	Presentation	relational database	source	7
400	To Hydrate or Chlorinate: A Regression Analysis of the Levels of Chlorine in the Public Water Supply	Drew Doyle	Presentation	regression analysis, output delivery system	source	7
401	Tracking Your SAS Licensed Product Usage	Victor L. Andruskevitch	This paper offers a methodology for tracking who is using your licensed SAS products and which products are being used. Knowing how often each product is used helps your organization determine the number of SAS product licenses needed. The source code included in this paper is Linux specific but can easily be modified to run on the Windows platform	create table, proc sql, sql server	source pdf	8
402	Transitioning Health Care Data Analytic Platforms to the Cloud	Jay Baker	As the IT industry moves to further embrace cloud computing and the benefits it enables, many companies have been slow to adopt these changes due to concern around data compliance. Compliance with state and federal law, and relevant regulations often leads decision makers to insist that systems dealing with protected health information or similarly sensitive data remain on-premises, as the risks for noncompliance are so high. In this paper, we will detail BNL's standard practices for transitioning HIPAA compliant solutions from on- premises to a cloud based environment hosted by Amazon Web Services (AWS). We explain that by following best practices, and doing plenty of research, HIPAA compliance in a cloud environment is no more challenging than compliance in an on-premises environment. We will review the role of best in-practice DevOps tools like Docker, Consul, ELK stack, and others which improve the reliability and the repeatability of your HIPAA compliant solutions	phi, risk and compliance	source	8
403	Transport Layer Security (TLS) Configuration for SAS 9.4 and SAS Viya Components Made Easy	Heesun Park	Transport Layer Security (TLS) configuration for SAS components is essential to protect data in motion. All necessary encryption arrangement is established through a TLS handshake between the client and the server side. Many SAS 9.4 and SAS Viya components can be a client side, a server side, or both. SAS documentation primarily provides how-to steps for the configuration. This paper examines the X.509 certificate and the TLS handshake protocol, which are the basic building blocks of the secure communication. The paper focuses on the logic behind the setup and how various types of certificates are used in the configuration. Many unique client and server combinations of SAS components are illustrated and explained with the best-practice suggestions. INTRODUCTION TO PUBLIC KEY CRYPTOGRAPHY AND THE RSA ALGORITHM The Public Key Cryptography (PKC) was first introduced in 1978 by the paper published by Rivest, Shamir, and Adelman (later known as RSA) of MIT [1]	cas, sas studio, sas viya	source	9
404	Twelve Cluster Technologies Available in SAS 9.4	Robert Collum	We are always looking for ways to improve the performance, efficiency, and availability of our investment in SAS solutions. To address those needs, SAS offers the ability to cluster many of its constituent software components. This session identifies twelve components of SAS software components that can be clustered and describes how they are designed to boost the capabilities of SAS to function in the enterprise	grid computing, in-memory analytics, sas enterprise guide	source	25
405	Understanding SAS Embedded Process with Hadoop Security	David Ghazaleh	SAS Embedded Process enables user-written DS2 code and scoring models to run inside Hadoop. It taps into the massively parallel processing (MPP) architecture of Hadoop for scalable performance. SAS Embedded Process explores and complies with many Hadoop components. This paper explains how SAS Embedded Process interacts with the existing Hadoop security technology, such as Apache Sentry and RecordService	sas base, libname statement, proc sql, proc step, macro variable, parallel processing, proc contents, proc print, data mining, score code	source	16
406	Use Internal SAS Metadata User and Authentication Domain to Connect to an FTP Server	Sebastian Scanzi	A SAS based application was implemented in a SoX1 environment by a bank. The system requires up to date data as input from different sources (internal and external). One of the external data source was held on an external third party FTP server and the last available working day data of each month needed to be downloaded. One of the requirements for the FTP process was that the transfer of data needed to be automated and to be SoX compliant. The latter point was the most challenging one as the main FTP credentials could not be coded within the process itself. The normal approach of having the FTP password encrypted with `PROC PWENCODE` in conjunction with `FILENAME` FTP engine was not suitable. A series of options were considered (ie: Oracle wallet) but because of time and constraints none appeared viable for this project. A different approach needed to be taken and because the SAS Metadata was available to us we decided to use the SAS Authentication Domain	sas base, sas log	source	5
407	Use Machine Learning to Discover Your Rules	David R. Duling	Machine learning is not just for data scientists. Business analysts can use machine learning to discover rules from historical decision data or from historical performance data. Decision tree and logistic regression scorecard learning are available for standard data tables, and associations' analysis is available for transactional event tables. These rules can be edited and optimized for changing business conditions and policies, and then deployed into automated decision making systems. Users will see demonstrations using real data and learn how to apply machine learning to business problems	data validation rules, machine learning, proc hpsplit, sample, response variable, flag variable, logistic regression, logistic distribution, regression analysis, data mining, score code	source	10
408	Use SAS Enterprise Guide and SAS Add-In for Microsoft Office to Support Enrollment Forecasting	Andre Watts, Lisa Sklar	This paper w ill explore the steps taken by a large public research institution to develop a five-year enrollment forecasting model to support the critical enroll ment management process at the institution. A key component of the process is providing university stakeholders w ith a self-service, secure, and flexible tool that enables them to quickly generate different enrollment projections using the most up- to-date information as possible in Microsoft `EXCEL`. This paper w ill show how w e integrated both SAS Enterprise Guide and the SAS Add- In for Microsoft Office to support this critical process w hich had very specific stakeholder requirements and expectations	excel, sas enterprise guide	source	5
409	User-Written versus System-Generated SAS Source Code	Thomas E. Billings	The traditional model of SAS source code production is for all code to be directly written by users or indirectly written, i.e., generated by user-written macros, Lua code, or via DATA steps. This model was recently extended to allow SAS macro code to operate on arbitrary text (e.g., html) via `PROC STREAM`. In contrast, the modern SAS system includes many products that operate in the client-server environment and function as follows: 1) the user interacts with the product via a GUI to specify the processing desired, 2) the product saves the user-specifications in metadata and generates SAS source code for the target processing, 3) the source code is then run (per user directions) to perform the processing. Many of these products give users the ability to modify the generated code and/or insert their own user-written code. Also, the target code (system generated plus optional user-written) can be exported or deployed to be run as a stored process, in batch, or in another SAS environment	sas enterprise guide, exception handling	source	8
410	Using a Dynamic Panel Estimator to Model Change in Panel Data	Roberto Gutierrez	Panel data, which are collected on a set (panel) of individuals over several time points, are ubiquitous in economics and other analytic fields because their structure allows for individuals to act as their own control groups. The `PANEL` procedure in `SAS/ETS` software models panel data that have a continuous response, and it provides many options for estimating regression coefficients and their standard errors. Some of the available estimation methods enable you to estimate a dynamic model by using a lagged dependent variable as a regressor, thus capturing the autoregressive nature of the underlying process. Including lagged dependent variables introduces correlation between the regressors and the residual error, which necessitates using instrumental variables. This paper guides you through the process of using the typical estimation method for this situation-the generalized method of moments (GMM)-and the process of selecting the optimal set of instrumental variables for your model	response variable, regression analysis, panel data, correlation, proc panel, proc reg	source	10
411	Using a Population Average Model to Investigate the Success of a Customer Retention Strategy	Colleen E. McGahan	In many healthcare settings, patients are like customers - they have a choice. An example is whether or not to receive a procedure. In population-based screening where the goal is to reduce deaths, the success of a screening program hinges on the patient's choice to accept the procedure. Like in many other industries, this not only relies on the program's ability to attract new eligible patients, it also relies on its ability to retain existing customers. The success of a new customer retention strategy within the breast screening environment is examined. A population averaged (PA) model (aka marginal models) using Generalized Estimating Equations (GEE) in the `GENMOD` procedure is used to account for the lack of independence of the observations. This business case provides a great introductory session for people who are not familiar with mixed effects models or hierarchical modelling with a binary outcome	correlation, proc calis, customer retention, regression analysis, logistic regression, logistic distribution, odds ratio, proc genmod, proc mixed, proc step, likelihood function, feature selection, response variable, covariance, confidence interval, random intercept	source	19
412	Using a SAS Macro to Calculate Kappa and 95% CI for Several Pairs of Nurses of Chemical Triage	Abbas S. Tavakoli, Joan Culley, Jane Richter, Sara Donevant, Jean Craig	It is often necessary to assess multi-rater agreement for multiple observation categories in case controlled studies. The Kappa statistic is one of the most common agreement measures for categorical data. The purpose of this paper is to show an approach for using the SAS 9.42 procedures, including Macro language, to estimate Kappa with 95% CI for pairs of nurses that used two different triage systems during a computer simulated chemical mass casualty incident (MCI). This paper used data from the Validating Triage for Chemical Mass Casualty Incidents - A First Step R01 grant to assess the performance of a typical hospital triage system called the Emergency Severity Index (ESI) 14 compared with an Irritant Gas Syndrome Agent Syndrome (IGSA) Triage Algorithm, being developed from this grant, to quickly prioritize the treatment of victims of IGSA incidents	proc freq, cohen's kappa, macro facility, weighted kappa, proc step, frequency analysis, sas syntax	source pdf	10
413	Using Analytics to Prevent Fraud Gives HDI Fast and Real-Time Approval for Claims	Rayani Melega	The objective of this paper is to introduce the project for Insurance Fraud Detection, developed in HDI Seguros \| Brazil, that aims at improving fraud prevention in claim processes, implementing SAS Anti- Fraud platform, composed by SAS SFF and SAS RTDM solutions. The main goals are: To support online decision making; To reduce false-positives and improve the accuracy of claim classification (the decision to set a claim as "to be investigated" or not); To design an interactive dashboard to manage claims to be investigated; 2 To use relationship networks to visualize and investigate suspicious connections; To identify the best analysts to analyze each claim, depending on one's specialization; To include the business expertise into business rules; To select claims with high propensity to be a fraud with models developed by HDI Analytics team; To consult multiple datasets simultaneously; * To manage a list of suspects, avoiding new activities, such as new policy issues or	data validation rules, regression analysis	source	9
414	Using Big Data to Visualize People Movement Using SAS Basics	Stephanie R. Thompson	Visualizing the mo vement of people o ver time in an animation can pro vide insights that tables and static graphs cannot. There are many options, but what if you want to base the visualization on large amounts of data from several sources? SAS is a great tool for this type of project. This paper summarizes how visualizing mo vement is accomplished using se veral data sets, large and small, and using various SAS procedures to pull it together. The use of a custom shape file is also highlighted. The end result is a GIF, which can be shared, that pro vides insights not available with other methods	proc step, macro variable, proc gmap, create table, macro loop	source	11
415	Using Graph Analytics for Predictive Modeling in Life Insurance	Robert G. Moore	This paper discusses a specific example of utilizing graph analytics or social network analysis (SNA) in predictive modeling in the life insurance industry. The methods of social network analysis are applied to agents that share compensation, and the results are used to derive input variables for a model to predict the likelihood of certain behavior by insurance agents. Both SAS code and SAS Enterprise MinerTM are used to illustrate implementing different graph analytical methods. This paper assumes the reader is familiar with the basic process of creating predictive models using multiple (linear or logistic) regression, and, in some sections, familiarity with SAS Enterprise Miner	link analysis, data mining, social network analysis, life insurance, network analytics, agent, financial services	source	6
416	Using Hash Tables for Creating Electronic Code Books	Raghav Adimulam	In projects that span multiple years, (e.g., longitudinal studies) there are usually thousands of new variables introduced at the end of every year or at the end of each phase of the project. These variables usually have the same stem or core as the previous year's variables. However, they differ only in a digit or two that usually signifies the year number of the project. Therefore, every year, there is this extensive task of comparing thousands of new variables to older variables for the sake of carrying forward key database elements corresponding to the previously defined variables. These elements can include the length of the variable, data type, format, discrete or continuous flag, and so on. In our SAS program, hash objects are efficiently used to cut down not only the time lapsed, but also the number of DATA and `PROC` steps used to accomplish the task. The resulting clean and lean code is much easier to understand	hash table, proc sql, proc step, data step, excel, if-then-else	source	8
417	Using International Character Sets with SAS and Teradata	Greg Otto, Salman Maher, Austin Swift	If you run SAS and Teradata software with default application and database client encodings, some operations with international character sets appear to work because you are actually treating character strings as streams of bytes instead of streams of characters. As long as no one in the "chain of custody" tries to interpret the data as anything other than a stream of bytes, then data can sometimes flow in and out of a database without being altered, giving the appearance of correct operation. But when you need to compare a particular character to an international character, or when your data approaches the maximum size of a character field, then you might run into trouble. To correctly handle international character sets, every layer of software that touches the data needs to agree on the encoding of the data. UTF-8 encoding is a flexible way to handle many single-byte and multi-byte international character sets	teradata, proc sql, create table, translate, proc step, sas viya	source	14
418	Using ODS EXCEL to Integrate Tables, Graphics, and Text into Multi-Tabbed Microsoft Excel Reports	Caroline Walker	Do you have a complex report involving multiple tables, text items, and graphics that could best be displayed in a multi-tabbed spreadsheet format? The Output Delivery System (`ODS`) destination for `EXCEL`, introduced in SAS 9.4, enables you to create Microsoft `EXCEL` workbooks that easily integrate graphics, text, and tables, including column labels, filters, and formatted data values. In this paper, we examine the syntax used to generate a multi-tabbed `EXCEL` report that incorporates output from the REPORT, PRINT, `SGPLOT`, and SGPANEL procedures	excel, proc step, options statement, proc report, xls, proc sgpanel, output delivery system, proc print, proc sgplot, title statement, ods excel	source	16
419	Using PROC SEVERITY to Evaluate Quantile Approximation Techniques for Compound Distributions	Helgard Raubenheimer, Riaan de Jongh	The objective of this paper is to use a simulation comparison to evaluate quantile approximation methods in terms of their practical usefulness and potential applicability in an operational risk context. A popular method in modelling the aggregate loss distribution in risk and insurance is the Loss Distribution Approach (LDA). Many banks currently use the LDA for estimating regulatory capital for operational risk. The aggregate loss distribution is a compound distribution resulting from a random sum of losses, where the losses are distributed according to some severity distribution and the number (of losses) distributed according to some frequency distribution. In order to estimate the regulatory capital, an extreme quantile of the aggregate loss distribution has to be estimated. A number of numerical approximation techniques have been proposed to approximate the extreme quantiles of the aggregate loss distribution	proc step, proc freq, monte carlo, frequency analysis, operational risk, poisson distribution, sample, probability distribution, proc fcmp	source	11
420	Using Python with SAS Cloud Analytic Services	Kevin D. Smith, Xiangxiang Meng	With SAS Viya and SAS Cloud Analytic Services (CAS), SAS is moving into a new territory where SAS Analytics is accessible to popular scripting language s using open APIs. Python i s one of those client languages. We demonstrate how to connect t o CAS, run CAS actions, explore data, build analytic al models, and then manipulate and visualize the results using stan dard Python packages such as Pandas and Matplotlib. We cover a wide variety of topics t o give you a bird's eye view of what is possibl e when you combine the best of S AS with the best of open source	cas action, proc hpsplit, cas, regression analysis, by group processing, proc reg, correlation, proc hpforest, variable list, sample, feature importance, gradient boosting, proc means, proc genmod, score code	source	23
421	Using SAS Data Management Advanced to Ensure Data Quality for Master Data Management	Brian Smith	Data is a valuable corporate asset that when managed improperly can detract from a company's ability to achieve strategic goals. At 1-800-Flowers.com, Inc. (18F) we have embarked on a journey towards Data Governance through embracing Master Data Management (MDM). Along the path we've recognized that in order to protect and increase the value of our data we must take data quality into consideration at all aspects of data movement in the organization. This document will discuss the ways that SAS Data Management is being leveraged by the team at 18F to create and enhance our data quality strategy to ensure data quality for MDM	data cleaning, master data management, data governance, data integration	source	20
422	Using SAS Software to Shrink the Data Footprint in Your Applications	Ahmed Al-Attar	This paper discusses the techniques I used at the Census Bureau to overcome the issue of dealing with large amounts of data while modernizing some of their public-facing web applications by using service oriented architecture (SOA) to deploy JavaScript web applications powered by SAS. The paper covers techniques that resulted in reducing 1,753,926 records (82 MB) down to 58 records (328 KB), a 99.6% size reduction in summarized data on the server side	proc step, macro definition	source	8
423	Using SAS to Analyze Emergency Department Visits: Medicaid Patients Compared to Other Pay Sources	Bradley Casselman, Taylor K. Larkin, Denise J. McManus		proc hpsplit, regression analysis, proc step, proc freq, data mining, imputation, sample, proc sql, proc hpforest, data cleaning, stepwise selection, response variable	source	16
424	Using SAS to Estimate Rates of Disease from Nationally Representative Databases	Jessica Rudd	One of the research goals in public health is to estimate the burden of diseases among the US population. We describe burden of disease by analyzing the statistical association of various diseases with: hospitalizations, emergency department (ED) visits, ambulatory/outpatient (doctors' office) visits, and deaths. In this short paper, we discuss the use of large, nationally representative databases, such as those offered by the National Center for Health Statistics (NCHS) or the Agency for Healthcare Research and Quality (AHRQ), to produce reliable estimates of disease for studies. In this example, we use SAS and SAS-callable SUDAAN to analyze the Nationwide Emergency Department Sample (NEDS), offered by AHRQ, to estimate Hand, Foot, and Mouth Disease (HFMD) associated emergency department (ED) visits in children less than 5 years old	sample, adam dataset	source pdf	10
425	Using SAS to Estimate SE, SP, PPV, NPV, and Other Statistics of Chemical Mass Casualty Triage	Abbas S. Tavakoli, Joan Culley, Jane Richter, Sara Donevant, Jean Craig	Chemical incidents involving irritant chemicals such as chlorine pose a significant threat to life and require rapid assessment. This paper used data from the Validating Triage for Chemical Mass Casualty Incidents - A First Step R01 grant to determine the most predictive signs and symptoms (S/S) for a chlorine mass casualty incident. SAS 9.4 was used to estimate sensitivity, specificity, positive and negative predictive values, and other statistics of irritant gas syndrome agent S/S for two exiting systems designed to assist emergency responders in hazardous material incidents (Wireless Information System for Emergency Responders [WISER] and CHEMM Intelligent Syndromes Tool [CHEMM-IST]). The result for WISER showed the sensitivity was .72 to1.0; specificity .25 to .47; and the positive predictive value and negative predictive value were .04 to .87, and .33 to 1.0; respectively	negative predictive value, precision, type i error, type ii error, sas output, proc freq	source pdf	11
426	Using SAS Viya Microservices Logging for Performance Analysis of SAS Visual Analytics 8.1 Reports	Scott C. Sweetland	Your SAS Visual Analytics users begin to create and share reports. As an administrator, you want to track performance of the reports over time, analyzing timing metrics for key tasks such as data query and rendering, relative to total user workload for the system. Logging levels can be set for the SAS Visual Analytics reporting services that provide timing metrics for each report execution. The log files can then be mined to create a data source for a time series plot in SAS Visual Analytics. You see report performance over time with peak workloads and how this impacts the user experience. Isolation on key metrics can identify performance bottlenecks for improvement. First we look at how logging levels are modified for the reporting services and focus on tracking a single user viewing a report. Next, we extract data from a long running log file to create a report performance data source	time series analysis, cas, key performance indicator, sas viya	source	9
427	Using SAS/OR Software to Optimize the Capacity Expansion Plan of a Robust Oil Products Distribution Network	Shahrzad Azizzadeh	A Middle Eastern company is responsible for daily distribution of over 230 million liters of oil products. For this distribution network, a failure scenario is defined as an occurring when oil transport is interrupted or slows down, and/or when products demands fluctuates outside the normal range. Under all failure scenarios, the company plans to provide additional transport capacity at minimum cost so as to meet all point-to-point product demands. Currently, the company uses a wait-and-see strategy, which carries a high operating cost and depends on the availability of third-party transportation. This paper describes the use of the `OPTMODEL` procedure to implement a mixed integer programming model to model and solve this problem. Experimental results are provided to demonstrate the utility of this approach. It was discovered that larger instances of the problem, with greater numbers of potential failure scenarios, can become computationally extensive	odds ratio, proc step	source	9
428	Using SASv9.cfg, autoexec.sas, SAS Registry, and Options to Set Up Base SAS	Peter W. Eberhardt, Mengting Wang	Are you frustrated with manually setting options to control your SAS Display Manager sessions but become daunted every time you look at all the places you can set options and window layouts? In this paper, we look at various files SAS accesses when starting, what can (and cannot) go into them, and what takes precedence after all are executed. We also look at the SAS Registry and how to programmatically change settings. By the end of the paper, you will be comfortable in knowing where to make the changes that best fit your needs	proc step, libname statement	source	15
429	Using Segmentation to Build More Powerful Models with SAS Visual Analytics	Darius S. Baer, Sam Edgemon	What will your customer do next? Customers behave differently; they are not all average. Segmenting your customers into different groups enables you to build more powerful and meaningful predictive models. You can use SAS Visual Analytics to instantaneously visualize and build your segments identified by a decision tree or cluster analysis with respect to customer attributes. Then you can save the cluster/segment membership and use that as a separate predictor or as a group variable for building stratified predictive models. Dividing your customer population into segments is useful because what drives one group of people to exhibit a behavior may be quite different than what drives another group. By analyzing the segments separately, you are able to reduce the overall error variance or noise in the models. As a result, you improve the overall performance of the predictive models	proc hpsplit, logistic regression, regression analysis, logistic distribution, heatmap, response variable, data validation rules, cluster analysis, proc fastclus, likelihood function, customer satisfaction, score code, proc freq	source	18
430	Using Shared Accounts in Kerberized Hadoop Clusters with SAS: How Can I Do That?	Michael Shealy	Using shared accounts to access third-party database servers is a common architecture in SAS environments. SAS software can support seamless user access to shared accounts in databases such as Oracle and MySQL, via group definitions and outbound authentication domains in Metadata. However, the configurations necessary to leverage shared accounts in Kerberized Hadoop clusters are more complicated. Kerberos tickets must often be generated and maintained in order to simply access the Hadoop environment, and those tickets must allow access as the shared account instead of an individual user's account. In all cases, key prerequisites and configurations must be put into place in order for seamless Hadoop access to function with the shared account. Methods for implementing these arrangements in SAS environments can be non-intuitive		source	12
431	Using Text Analysis to Improve the Quality of Scoring Models with SAS Enterprise Miner	Piotr Małaszek	Transformation of raw data into sensible and useful information for prediction purposes is a priceless skill nowadays. Vast amounts of data, easily accessible at each step in a process, gives us a great opportunity to use it for countless applications. Unfortunately, not all of the valuable data is available for processing using classical data mining techniques. What happens if textual data is also used to create the analytical base table (ABT)? The goal of this study is to investigate whether scoring models that also use textual data are significantly better than models that include only quantitative data. This thesis is focused on estimating the probability of default (PD) for the social lending platform kokos.pl. The same methods used in banks are used t o evaluate the accuracy of reported PDs. Data used for analysis is gathered directly from the platform via the API. This paper describes in detail the steps of the data mining process that is built using SAS Enterprise Miner	roc curve, character variable, logistic regression, data mining, logistic distribution, regression analysis, parsing, sas base, proc step, proc logistic, credit risk, sas enterprise guide, proc freq	source	11
432	Using the CLP Procedure to Solve the Agent-District Assignment Problem	Stephen B. Sloan, Kevin K. Gillette	The Challenge: assigning outbound calling agents in a telemarketing campaign to geographic districts. The districts have a variable number of leads and each agent needs to be assigned entire districts with the total number of leads being as close as possible to a specified number for each of the agents (usually, but not always, an equal number). In addition, there are constraints concerning the distribution of assigned districts across time zones, in order to maximize productivity and availability. Our Solution: uses the `SAS/OR` procedure `PROC CLP` to formulate the challenge as a constraint satisfaction problem (CSP), since the objective is not necessarily to minimize a cost function, but rather to find a feasible solution to the constraint set	proc step, macro variable	source	10
433	Using the LOGISTIC or SURVEYLOGISTIC Procedure and Weighting of Public-Use Data in the Classroom	Tyler C. Smith, Besa Smith	The rapidly evolving informatics capabilities of the past two decades have resulted in amazing new data-based opportunities. Large public use datasets are now available for easy download and utilization in the classroom. Days of classroom exercises based on static, clean, easily maneuverable samples of 100 or less are over. Instead we have large and messy "real-world data" at our finger tips allowing for educational opportunities not available in years past. There are now hundreds of public use data sets available for download and analysis in the classroom. Many of these sources are survey-based and require the understanding of weighting techniques. These techniques are necessary for proper variance estimation allowing for sound inferences through statistical analysis. This example uses the California Health Interview Survey to present and compare weighted and non-weighted results using SAS `PROC SURVEYLOGISTIC`	proc step, logistic distribution, proc logistic, sample, logistic regression, regression analysis, proc surveylogistic, odds ratio, confidence interval, dataset, predictor variable	source	9
434	Using the New ODS EXCEL Destination in SAS 9.4 When Working with Remote Servers	Thomas Bugg	The `ODS` `EXCEL` destination has made sharing SAS reports and graphs much easier. What is even more exciting is that this destination is available for use regardless of the platform. This is extremely useful when reporting is performed on remote servers. This presentation goes through the basics of using the `ODS` `EXCEL` destination and shows specific examples of how to use this in a remote environment. Examples for both SAS on Windows and in SAS Enterprise Guide are provided	excel, proc export, proc step, sas enterprise guide	source	7
435	Using the ODS EXCEL Destination with SAS University Edition to Send Graphs to Microsoft Excel	William E. Benjamin Jr.	Students now have access to a SAS learning tool called SAS University Edition. This online t ool is freely available to all, for non-commercial use. This means it is basically a free version of SAS that can be used to teach yourself or someone else how to use SAS. Since a large part of my body of writings has focused upon moving data between SAS and Microsoft `EXCEL`, I thought I would take some time to highlight the tasks that permit movement of data between SAS and `EXCEL` using SAS University Edition. This paper is directed toward sending graphs to `EXCEL` using the new `ODS` `EXCEL` destination software	excel, xls, sas studio, output delivery system, cdisc	source	13
436	Using the OPTGRAPH Procedure: Transformation of Transactional Data into Graph for Cluster Analysis	Linh H. Le, Jennifer Priestley	Graph is a mathematic structure capable of representing a network of objects and their relationships. Clustering is an area in graph theory where objects are split into groups based on information of their connections. Depending on the field of the data, object clusters have various meanings, e.g., in the market basket analysis, clusters are families of products that are frequently or likely purchased together, clustering the user log of a website may provide groups of webpages that are often viewed together, etc. This paper provides a SAS Macro that helps transformation of transactional data, or any data with a many-to-many relationship between two entities, into graph data of two types, followed by sample clustering analyses in the graph data using the `OPTGRAPH` procedure	market basket analysis, proc freq, sample, macro definition, proc step	source	12
437	Using the Power of SAS from JMP	Philip A. Mason	JMP integrat es very nicely with SAS software, so you can do som e pretty amazing things by combining the power of JMP and SAS. Yo u can submit some code to run something on a SAS server and bring the results back as a JMP table. T hen you can do lots of things wit h the JMP table to analyze the data returned. This workshop shows you how to access data via SAS servers, r un SAS code and bring data back to JMP, and use JMP to do many things very quickly an d easily. Explore the synergies between these tools; havin g both is a powerful combination that far outstrips just having one, or not using them together	sample, dataset, sas log	source zip	14
438	Using the SAS Customer Intelligence 360 Hybrid Cloud Capabilities for True Omnichannel Marketing	Toshi Tsuboi, Stephen Cuppett	More than ever, customers are demanding consistent and relevant interaction across all channels. Businesses are having to develop omnichannel marketing capabilities to please these customers. Implementing omnichannel marketing is often difficult, especially when using digital channels. Most products designed for digital channels lack capabilities to integrate with traditional channels that have on- premises processes and data. SAS Customer Intelligence 360 is a new offering that enables businesses to leverage both cloud and on-premises channels and data. This is possible due to the solution's hybrid cloud architecture. This paper discusses the SAS Customer Intelligence 360 approach to the hybrid cloud, and covers key capabilities on security, throughput, and integration	stream processing, real-time processing	source	9
439	Using the SYMPUT Function to Automatically Choose Reference for Bivariate Cox Proportional Models	Zhongjie Cai	Bivariate Cox Proportional Models are used when you test the association between a single covariate and the outcome for survival analysis. This procedure repeats for each covariate of interest. As SAS uses the last category as the default reference in CLASS statement, you will encounter problems when you would like to use other categories as references. If a format is associated with that covariate, you will have to use the corresponding format instead of the raw numeric data. This problem becomes even worse when you have to repeat the procedure and hand type the reference every single time. This presentation demonstrates one way of fixing the problem using SAS MACRO and the CALL SYMPUT function	call symput, proc contents	source zip	3
440	Using Vibration Spectral Analysis to Predict Failures by Integrating R into SAS Asset Performance Analytics	Adriaan Van Horenbeek	In industrial systems, vibration signals are the most important measurements indicating asset health. Based on these measurements, an engineer with expert knowledge on the assets, industrial process and vibration monitoring can perform spectral analysis to identify failure modes. However, this is still a manual process that heavily depends on the experience and knowledge of the engineer analyzing the vibration data. Moreover, when measurements are performed continuously, it becomes impossible to act in real time on this data. Therefore, the objective of this paper is to use analytics to perform vibration spectral analysis in real time to predict asset failures. The first step in this approach is to translate engineering knowledge/features into analytic features to perform predictive modeling. This is done by converting the time signal into the frequency domain by applying a Fast Fourier Transform	proc freq, predictive maintenance, iot, proc iml, proc step, time series analysis	source	15
441	Visualizing Market Structure Using Brand Sentiments	Praveen Kumar Kotekal, Amit K Ghosh, Goutam Chakraborty	Increasingly, customers are using Social media and other Internet-based applications such as review sites and discussion boards to voice their opinions and express their sentiments about brands. Such spontaneous and unsolicited customer feedback can provide brand managers with valuable insights about competing brands. There is a general consensus that listening and reacting to the "voice of the customer" is a vital component of brand management. However, the unstructured, qualitative, and textual nature of customer data that is obtained from customer's poses significant challenges for data scientists and business analysts. In this paper we propose a methodology that can help brand managers visualize the competitive structure of a market based on an analysis of customer perceptions and sentiments that are obtained from blogs, discussion boards, review sites, and other similar sources	proc freq, parsing, regression analysis, predictor variable	source	9
442	Visualizing Reports with SAS Theme Designer in SAS Visual Analytics 8.1	Aniket Vanarase	SAS Theme Designer provides a rich set of colors and graphs that enables customers to create a custom application and report themes. Users can also preview their work within SAS Visual Analytics. The features of SAS Theme Designer enable the user to bring a new look and feel to their entire application and to their reports. Users can customize their reports to use a unique theme across the organization, yet they can customize these reports based on their individual business requirements. Providing this customization involves meeting the customers' demands from the theming perspectives of customization, branding, and logo, and making them seamless within their application. The paper walks the user through the process of using SAS Theme Designer and SAS Visual Analytics. It further highlights the following features of SAS Theme Designer: creating and modifying application and report themes, previewing output in SAS Visual Analytics, and importing and exporting themes for reuse	sas viya, bar chart	source	19
443	Visualizing the Demographics of a Large Healthcare Provider's Membership using SAS	Don McCarthy, Michael Santema	Visualization of complex data can be a valuable tool for researchers and policy makers, and Base SAS has powerful capabilities for such data exploration. In particular, `SAS/GRAPH` software is a flexible tool that enables the analyst to create a wide variety of data visualizations. This paper presents work done using SAS to visualize complex demographic data related to the membership of a large American healthcare provider, Kaiser Permanente (KP). We use SAS to create a number of geographic visualizations of KP demographic data related to the provider's membership	proc gmap, health insurance	source	16
444	War and Peace: SAS Platform Support. Can We Make It Easier?	Sergey Iglov	Over the years, the use of SAS has grown immensely within Royal Bank of Scot land (R BS) , mak ing platform support and maintenance overly complicated and time consuming. At RBS, we realized that we have been living "war and peace" every day for many years and that the time has come to re -th ink how we support SAS platforms. With our approach to rationalize and consolidate the ways our organizat ion uses SAS came the need to review and improve the processes and procedures we have in place . Th is paper explains why we did it, what we've changed or reinvented, and how all these have changed our way of operation by bringing us closer to DevOps and helping us to impro ve our relationship with our customers as well as building trust in the service we deliver	data mining, sas management console	source	12
445	Weight of Evidence Coding for the Cumulative Logit Model	Bruce S. Lund	Weight of evidence (WOE) coding of a nominal or discrete variable X is widely used when preparing predictors for usage in binary logistic regression models. The concept of WOE is extended to ordinal logistic regression for the case of the cumulative logit model. If the target (dependent) variable has J levels, then J-1 WOE variables are needed to recode X. The appropriate setting for implementing WOE coding is the cumulative logit model with partial proportional odds. As in the binary case it is important to bin X to achieve parsimony before the WOE coding. SAS code to perform this binning is discussed. An example is given that shows the implementation of WOE coding and binning for a cumulative logit model with target variable having three levels	logistic regression, logistic distribution, correlation, proc logistic, proc step, regression analysis, likelihood function, degrees of freedom, ordinal logistic regression, binary variable	source	15
446	What Is Needed for True Population Health Intelligence?	Laurie Rose, Dan Stevens	Health care has long been focused on providing reactive care for illness, injury, or chronic conditions. The rising cost of providing health care has forced many countries, health insurance payers, and health care providers to shift approaches. A new focus on patient value includes providing financial incentives that emphasize clinical outcomes instead of treatments. This focus also means that providers and wellness programs are required to take a segmentation approach to the population under their care, targeting specific people based on their individual risks. This session discusses the benefits of a shift from thinking about health care data as a series of clinical or financial transactions, to one that is centered on patients and their respective clinical conditions. This approach allows for insights pertaining to care delivery processes and treatment patterns, including identification of potentially avoidable complications and variations in care	insurance claims, big data, clinical endpoint	source	8
447	What Statisticians Should Know about Machine Learning	David Richard Cutler	In the last few years machine learning/statistical learning methods have gained increasing popularity among data scientists and analysts. Statisticians have sometimes been reluctant to embrace these methodologies, partly due to a lack of familiarity, and partly due to concerns with interpretability and usability. In fact, statisticians have a lot to gain by using this these modern, highly computational tools. For certain kinds of problems machine learning methods can be much more accurate predictors than traditional methods for regression and classification and some of these methods are particularly well suited for the analysis of "big" and "wide" data. Many of these methods have origins in statistics or at the boundary of statistics and computer science and some are already well established in statistical procedures, including LASSO and elastic net for model selection and cross-validation for model evaluation	regression analysis, logistic regression, logistic distribution, predictor variable, machine learning, sample, feature selection, proc step, multiple regression, data cleaning, response variable, proc reg, proc hpforest, proc hpsplit, proc logistic, backward selection	source	13
448	What's Love Gotta Do WITH It	Jason O'Day	It has become a need-it-now world, and many managers and decision-makers need their reports and information quicker than ever before to compete. As SAS developers, we need to acknowledge this fact and write code that gets us the results we need in seconds or minutes, rather than in hours or days. SAS is a great tool for extracting, transferring, and loading data, but as with any tool, it is most efficient when used in the most effective way. Using the SQL pass-through techniques presented in this paper can reduce run time by up to 90% by passing the processing to the database instead of moving the data back to SAS to be consumed. You can reap these benefits with only a minor increase in coding difficulty	proc sql, create table, inner join, sql pass-through, proc step, implicit sql pass-through, order by, group by, libname statement	source pdf	12
449	What's New in SAS Data Management	Nancy A. Rausch	The latest releases of SAS Data Management software provide a comprehensive and integrated set of capabilities for collecting, transforming, and managing your data. The latest features in the product suite include capabilities for working with data from a wide variety of environments and types including Hadoop, cloud data sources, RDBMS, files, unstructured data, streaming, and others, and the ability to perform ETL and ELT transformations in diverse run-time environments including SAS, database systems, Hadoop, Spark, SAS Analytics, cloud, and data virtualization environments. There are also new capabilities for lineage, impact analysis, clustering, and other data governance features for enhancements to master data and support metadata management. This paper provides an overview of the latest features of the SAS Data Management product suite and includes use cases and examples for leveraging product capabilities	stream processing, real-time processing, amazon s3, big data, proc sql, data integration, sas management console, filename statement, amazon redshift, implicit sql pass-through, data governance, proc step, sas viya	source	15
450	What? I am the Linux Administrator for SAS Visual Analytics?	Ryan Kumpfmiller	Whether you are a new SAS administrator or switching to a Linux environment, you have a complex mission. This job becomes even more formidable when you are working with a system like SAS Visual Analytics that requires multiple users loading data daily. Eventually a user will have data issues or create a disruption that causes the system to malfunction. When that happens, what do you do next? In this paper, we will go through the basics of a SAS Visual Analytics Linux environment and how to troubleshoot the system when issues arise	sas administrator	source	8
451	When ANY Function Will Just NOT Do	Richann J. Watson, Karl Miller	Have you ever been working on a task and wondered whether there might be a SAS function that could save you some time? Let alone, one that might be able to do the work for you? Data review and validation tasks can be time-consuming efforts. Any gain in efficiency is highly beneficial, especially if you can achieve a standard level where the data itself can drive parts of the process. The ANY and NOT functions can help alleviate some of the manual work in many tasks such as data review of variable values, data compliance, data formats, and derivation or validation of a variable's data type. The list goes on. In this poster, we cover the functions and their details and use them in an example of handling date and time data and mapping it to ISO 8601 date and time formats	raw data, time variable	source pdf	8
452	Where Does Cleopatra Really Belong? An Analysis of Slot Machine Placement and Performance Using SAS	Stephen Tam	In the world of gambling, superstition drives behavior, which can be difficult to explain. Conflicting evidence suggests that slot machines, like BCLC's Cleopatra, perform well regardless of where they are placed on a casino floor. Other evidence disputes this, arguing that performance is driven by their strategic placement (for example, in high-traffic areas). We explore and quantify the location sensitivity of slot machines by leveraging SAS to develop robust models. We test various methodologies and data import techniques (such as casino CAD floor plans) to unlock some of the nebulous concepts of player behavior, product performance, and superstition. By demystifying location sensitivity, key drivers of performance can be identified to aid in optimizing the placement of slot machines	proc step, proc glmselect, predictor variable, estimate, proc corr, correlation, proc sql	source	12
453	Wrangling Your Data into Shape for In-Memory Analytics	Gary Mehler	High-quality analytics works best with the best-quality data. Preparing your data ranges from activities like text manipulation and filtering to creating calculated items and blending data from multiple tables. This paper covers the range of activities you can easily perform to get your data ready. High-performance analytics works best with in-memory data. Getting your data into an in-memory server, as well as keeping it fresh and secure, are considerations for in-memory data management. This paper covers how to make small or large data available and how to manage it for analytics. You can choose to perform these activities in a graphical user interface or via batch scripts. This paper describes both ways to perform these activities. You'll be well-prepared to get your data wrangled into shape for analytics!	cas, dataset, xls, txt, excel	source	15
454	Writing SAS Code on the Fly Using SAS Code as Character Variables	Peter Timusk	A SAS program with the extension .SAS is simply a text file. This fact opens the door to many powerful results. You can read a typical SAS program into a SAS data set as a text file with a character variable, with one line of the program being one record in the data set. The program's code can be changed, and a new program can be written as a simple text file with a .SAS extension. This presentation shows an example of dynamically editing SAS code on the fly and generating statistics about SAS programs	character variable, txt, data step, macro variable, dataset	source	4
455	Yet Another Lookup Method: Updatable Indexed Data Sets	Jack Hamilton	SAS has many methods of doing table lookups in data steps: formats, arrays, hash objects, the SASMSG function, indexed data sets, and so forth. Of those, hash objects and indexed data sets allow you to specify multiple lookup keys and return multiple table values. Both can be updated dynamically in the middle of a `DATA STEP` as you obtain new information (such as reading new keys from an input file, or creating new synthetic keys). Hash objects are very flexible and fast, and fairly easy to use, but are limited to the amount of data that can be held in memory. Indexed data sets may be slower, but are not limited to what can fit into memory, and so in some circumstances may be your only option. This presentation discusses how to use an indexed data set for table lookup and update it dynamically using the MODIFY statement and its allies	hash table, proc sql, lookup, key, proc step, create table, data step, macro variable, set statement, sample, order by, %if-%then-%else	source	15
456	You Do Not Have to Step On the Same Rake: SAS RAKING Macro, Fourth Generation	David Izrael, Michael P. Battaglia, Ann Battaglia, Sarah Ball	Presentation		source	7
457	You Imported What? Supporting International Trade with Advanced Analytics	Susan Trueman	Global trade and more people and freight moving across international borders present border and security agencies with a difficult challenge. While supporting freedom of movement, agencies must minimize risks, preserve national security, guarantee that correct duties are collected, deploy human resources to the right place, and ensure that additional checks do not result in increased delays for passengers or cargo. To meet these objectives, border agencies must make the most efficient use of their data, which is often found across disparate intelligence sources. Bringing this data together with powerful analytics can help them identify suspicious events, highlight areas of risk, process watch lists, and notify relevant agents so that they can investigate, take immediate action to intercept illegal or high-risk activities, and report findings	entity resolution, suspicious activity, case management	source	9
458	Your Database Can Do Complex String Manipulation Too!	Harry Droogendyk	Since databases often lacked the extensive string handling capabilities available in SAS, SAS users were often forced to extract complex character data from the database into SAS for string manipulation. As database vendors make regular expression functionality more widely available for use in SQL, the need to move data into SAS for pattern matching, string replacement and character extraction is no longer (as) necessary. This paper will cover enough regular expression patterns to make you dangerous, demonstrate the various REGEXP* SQL functions and provide practical applications for each	proc sql, character functions	source	19
459	Your Database Can Do SAS Too!	Harry Droogendyk	How often have you pulled oodles of data out of the corporate data warehouse down into SAS for additional processing? Additional processing, sometimes thought to be uniquely SAS, that might include FIRST. / LAST. logic, cumulative totals, lag functionality, specialized summarization or advanced date manipulation. Using the Analytical (or OLAP) and Windowing functionality available in many databases, all of this processing can be performed directly in the database without moving and reprocessing detail data unnecessarily. This paper illustrates how to increase your coding and execution efficiency by using the database's power through your SAS environment	proc sql, order by, group by, data step, data warehouse, proc step, excel	source	16
460	Zeroing In on Effective Member Communication: An Rx Education Study	Brian Mitchell	In 2013, the Centers for Medicare & Medicaid Services (CMS) changed the pharmacy mail-order education process so that Humana Pharmacy may only call a member with cost savings to educate the member on the potential savings and instruct the member to call back. The Rx Education call center asked for analytics work to help prioritize member outreach, improve education success, and decrease the number of members who are unable to be contacted. After a year of contacting members using this additional model insight, the education rate rose significantly and the unable to contact rate fell by more than half; this case study takes you on an analytics journey from the initial problem diagnosis and analytics solution, followed by refinements, as well as test and learn campaigns	sample, proc surveyselect	source	4

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