SGF 2019 Papers - Index

364 papers. Source: SAS Global Forum conference proceedings.

#	Title ×	Authors ×	Abstract ×	Keywords ×	Extras	Pages
1	20 in 20: Quick Tips for Enterprise Guide Users	Kelly Gray	There are many time-saving and headache saving tips and tricks you can use to make working in SAS Enterprise Guide a breeze. Did you know that you can change your layout so that you can see your code and your results at the same time? You will learn 20 tips and tricks for working in SAS Enterprise Guide in 20 minutes. One tip per minute, and out of the twenty you are guaranteed to find at least one nugget that will make your life easier	sas enterprise guide, macro variable	source	14
2	5W1H Method: SAS Viya as Your Cloud Analytics Platform? Who, What, When, Where, Why, and How	Daniel Hatton	Defining an analytics strategy and selecting a vendor for your analytics software is a big deal for any organization-from start-ups to small and medium-sized enterprises (SMEs) to corporations. Making the right strategic investment decisions in the use of time, resources, and technology can be a make or break situation. In this paper, I present a high-level look at using the common questioning framework 5W1H, sharing insights on the business, technical, and external factors that can help you to make the right choices in software selection and cloud implementation. The Who, What, When, Where, Why, and How questioning method is used to identify key considerations that should be explored and that influence a good decision-making process that determines whether an investment in SAS Viya as a modern cloud analytics platform suits your organization's needs	sas viya, analytics platform, data science, machine learning, business intelligence	source	10
3	A Basic Introduction to SASPy and Jupyter Notebooks	Alan K. Churchill	Web services have become very popular over the last decade as enterprises face a need to integrate disparate systems. Additionally, new web server engines and database integration options have arrived that ease that system integration challenge. This paper will illustrate how a lightweight service infrastructure can be put in place that simplifies the process of getting data into and out of SAS and serving up the output of SAS programs in a very stream -lined fashion. Web services do not need large stacks of software or complex hardware systems to be effective	web service, proc sql	source	7
4	A Beginners Guide to ARRAYs and DO Loops	Jennifer L. Waller	If you are copying and pasting code over and over to perform the same operation on multiple variables in a sas `DATA STEP` you need to learn about arrays and do loops. Arrays and do loops are efficient and powerful data manipulation tools that you should have in your programmer's tool box. Arrays list the variables that you want to perform the same operation on and can be specified with or without the number of elements/variables in the array. Do loops are used to specify the operation across the elements in the array. This workshop will show you how to use array statements and do loops with and without specifying the number of elements in the array to perform the operation on in the do loop	do loop, do over, end, array	source	11
5	A Collection of Pre-installation Resource Documentation for SAS 9.4	Jim Kuell	Many requirements and guidelines need to be taken into consideration when you are setting up the compute infrastructure for SAS 9.4. Tracking down these requirements can be a daunting task because they are located across several discipline areas of the support.sas.com website. This paper serves as a collection of the pre-installation requirements and guidelines, and presents a general overview of the topics as well as links to additional documentation	note, grid computing	source	5
6	A Comparison of Algorithmic and Epidemiologic Approaches for Modeling Predictors of Type II Diabetes	Audrey Whittle	Algorithmic variable reduction techniques have the potential to reduce the intensive pre- processing required for regression. This paper employs algorithmic techniques as a variable selection method and an alternative to regression for identifying salient factors. The first approach utilized random forest as a substitute for standard pre-processing and variable reduction, however this method generated a theoretically invalid set of predictors and was not explored further. Subsequently, a set of logistic regression and decision tree models are compared for their efficacy in identifying predictors of Type II diabetes. The optimal decision tree model (a Chi-Squared model with default Enterprise Miner options) performed comparably to the logistic regression model in terms of Average Squared Error. These findings suggest that data mining methods can successfully be used to supplement traditional epidemiologic research methods in future research	regression analysis, data mining, proc hpforest, proc hpsplit, logistic regression, proc varreduce, logistic distribution, data cleaning, proc calis, sample, feature selection, stepwise selection, response variable	source	8
7	A Complete Introduction to SASPy and Jupyter Notebooks	Jason Phillips	Thanks to the welcome introduction and support of an official SASPy module over the past couple of years, it is now a trivial task to incorporate SAS into new workflows by leveraging the simple yet presentationally elegant Jupyter Notebook coding and publication environment, along with the broader Python data science ecosystem that comes with it. This paper and presentation begins with an overview of Jupyter Notebooks for the uninitiated, then proceeds to explain the essential concepts in SASPy that enable communicating seamlessly with a SAS session from Python code. Included along the way is an examination of Python DataFrames and their practical relationship to SAS data sets, as well as the unique advantages offered by bringing your SAS work into the Notebook workspace and into productive unity with the broad appeal of Python's syntax	machine learning, data science	source	9
8	A Financial Statement Simulator to Aid Stress and Reverse Stress Testing	Shannon Clark	Forecasting balance sheet and income statement line items has long been a required activity, especially given the increased regulatory attention after the economic crisis of 2007-2010. Traditionally, financial institutions (FIs) have approached this activity through scenario and stress testing, which basically tests for outcomes arising from changes in circumstances. Unfortunately, the top-down approach and state of modeling to support line item level projections lag far behind other types of modeling being performed at FIs. Additionally, regulators have found additional weaknesses in approaches based on expert knowledge or historical evidence a priori and are increasingly advocating the exploration of tail risks that can render an FI's business models unviable, and likely cause the institute to default or become insolvent	reverse stress test, stress testing	source	6
9	A Hybrid Marketing Odyssey	Andy Bober	Hybrid vehicles let you use the best technology for a given scenario, blending fuel efficiency with long driving range, and low emissions with quick refueling. It's like two cars in one- two motors magically complementing each other under the hood, invisible to the driver. And as the driver, you have just one control panel that chooses the best engine for the circumstances, adjusting to changes in real time. We marketers face a similar dilemma: we have different engines with separate controls for different scenarios. Not only do we have to learn how to drive multiple systems to address the diversity of interactions we have with our customers, we must make them work together seamlessly to deliver delightful customer experiences. Sounds more like Marketing: Impossible! Are you thinking what I'm thinking? Yes! What the world needs now is a revolutionary approach-hybrid marketing! The good news is that SAS has been working hard on this and already has an answer for you	customer experience, financial services	source	11
10	A Neural Net Brain for an Autocompletion Engine: Improving the UX though Machine Learning	Alona Bulana, Igor Khorlo	AutocompletefeaturehasbeenpresentinIDEsandsourcecodeeditorsformorethanadecade. Itspeedsuptheprocessofcodingbyreducingtyposandothercommonmistakes. SASStu- dio, for example, suggests the next appropriate keyword in a context. Snippets are also an essential part of every decent IDE. That software usually uses the declarative approach to implement autocompletion. Mainly, the problem with this approach is the size of an imple- mentationwhichscaleswithalanguage'sgrammarsize,leadingtoverycomplexlogic. Inthispaper,wesolvetheautocompleteengineconstructionproblemusingamachinelearning way,whichautomaticallyderivesanautocompletionenginefromalanguagewithoutexplicitly beingprogrammed. TheideaistousegenerativemodelsbasedonRecurrentNeuralNetwork (RNN)trainedonaSASsourcecodetogeneratethesuggestions,likewhatyouaregoingto typenext,muchlikeyourphonepredictsyournextwords. Anoutputofagenerativemodelis anewpieceofsomethingthatlookslikeSASsourcecode	sample, probability distribution, proc step	source	11
11	A Practical Guide to Responsive Reactive Design Using SAS Visual Analytics	Elliot Inman	While it may seem as if you need to be an artist to create the kinds of beautiful, interesting, interactive visualizations you see on many commercial websites, you don't. All you need is a basic understanding of how HTML5 works and how human beings process visual information. In this paper, we provide guidelines for using SAS Visual Analytics to create websites that are responsive and reactive. Responsive design relies on HTML5 technologies to dynamically adjust HTML content to the screen size and orientation of a web-connected device. This enables websites to work well on many different devices, but it can cause problems. We present guidelines that reduce trial-and-error testing and describe common responsive design issues (resized legends, squished graphs, and more). We show how to easily test the responsiveness of a report using web developer views built into Google Chrome and Mozilla Firefox, and we provide warnings for some known issues with different browsers	box plot, bar chart, data science, dataset	source	18
12	A Quick Tour for SAS Visual Analytics 8.3: More Visual, More Analytics	Rajiv Ramarajan, Jordan Riley Benson	SAS Visual Analytics 8.3 introduces exciting new features to make it easier to craft beautiful reports. The new release improves efficiency by enabling report authors to reuse work done for data preparation, maintain report states across sessions, and use fewer clicks to get to a report. Users can create more compelling reports by using guides to lay out the report, visual tables with graphical representations in table cells, and enhanced geo- analytics to explore geographic data. The report playback feature is a new way to present the report. Integration with the new SAS Drive makes it easy to manage reports and share access with collaborators	response variable, bar chart	source	9
13	A Real-time Solution for Application Fraud Prevention	Prathaban Mookiah	Application fraud in the Finance industry is a form of fraud that is committed by obtaining various financial products via misinformation and deceit, malicious acts such as identity theft, synthetic IDs, and account takeovers. In this paper, we present a real-time solution for application fraud prevention that was developed within the Fraud and Security Intelligence division at SAS. This solution leverages some of our past successes in solving real-time fraud in other spaces such as payments as well as deposit monitoring. In addition, application fraud detection requires that we form a network view of the entities involved in the application to assess their risk. To this end, we have engineered a solution that combines well used techniques such as entity resolution, network and graph theory, dynamic Signatures, models based on machine learning to produce a score that represents the risk of an application, and the application of business rules for decisioning in real time	application fraud	source	11
14	A scoring algorithm to validate hashed ID linkages using SPEDIS and COMPGED	Nichole Sanders	The Arkansas All-Payers Claims Database (APCD) contains claims from multiple payer sources, as well as other available data sources. It does not have direct personal identifiers (DPI), such as name or date of birth (DOB). Instead it contains a hashed version of the concatenated last name and DOB which allows linkage of individuals across payers and other data sources in the APCD. An expected match rate was derived for matching birth and death certificates to claims in the APCD. DPI are contained on birth and death recor ds, as well as Medicaid claims in the Health Data Initiative (HDI) data warehouse that is housed at the Arkansas Center for Health Improvement (ACHI). Hashed IDs were created for individuals contained in these data sources. To calculate a match rate, a denominator of "true linkages" was determined using the known DPI contained on the HDI data sources. The match scoring algorithm compared first and last names, DOB, and gender between these sources	proc sql, create table, proc step	source	8
15	A Selective Overview of Panel Data with Applications in SAS	Oral Capps	The nature and use of panel data in the economics and business literature are discussed. Various model specifications are presented including variants of the Parks model, the Da Silva model, one-way or two-way random effects models (error component models), one-w ay or two-way fixed effects models, and seemin gly unrelated regression (SUR ) models. We illustrate these respective models with a practical example to demo nstrate the use of SAS in handling panel data. Additionally, emphasis is placed on the interpre tation of the empirical	residual, predictor variable, proc mixed, correlation, panel data, estimate, covariance, price elasticity, proc step, binary variable, regression analysis, proc panel, sample, fixed effects, null hypothesis, time series analysis, ancova, proc calis, proc model, ar(1), degrees of freedom, proc sort	source	26
16	Accessing Your Favorite Database with SAS and JDBC	Salman Maher	Have you ever wished you could use Java Database Connectivity (JDBC) with SAS? If you have, we have exciting news: `SAS/ACCESS` Interface to JDBC! JDBC is one of the most versatile database connectivity APIs available. JDBC is very popular, and it is common for vendors to release JDBC drivers before they release Open Database Connectivity (ODBC) drivers. Some vendors release only JDBC drivers for their products. Now is the time to learn about this important new technology. This paper shows you how to effectively use `SAS/ACCESS` Interface to JDBC. You will learn how to configure the JDBC driver, connect to your database, query data from a database, and write data to a database. This paper is the jump-start you need to learn how to effectively use SAS and JDBC with your database of choice	proc step, proc sql, libname statement, cpu time, sas viya, teradata, cas, sql pass-through, implicit sql pass-through, cas action, connection string, sas/access, sas 9	source	10
17	Add the "Where" to the "What" with Location Analytics in SAS Visual Analytics 8.3	Robby Powell	Seeing is believing-understand your data by seeing where it lives. Adding geographic context to your categorical and quantitative data through location analytics can present patterns to help you understand why things happen and what you can do to encourage that behavior or change outcomes. In this paper, you learn the geographic capabilities of SAS Visual Analytics 8.3 Location Analytics, how to use them, and the value they deliver	geospatial analytics, macro definition	source	18
18	Adding Data to Real-Time Decision-Making with SAS Decision Manager on SAS Viya	Ernest Jessee, Dean Taplin	SAS Decision Manager enables users to operationalize analytics in real-time settings. In these deployments, analytics can create efficiencies and optimize your business process. However, to properly leverage the full value of analytical decision-making processes, users need access to their data. SAS Decision Manager offers that ability, enabling database queries within their real-time flows. This paper tours this ability, showing how to configure a database connection within SAS Environment Manager and write custom DS2 code that queries a database within SAS Decision Manager. The results of the query are added to the data stream of the decision flow for further processing	connection string, rest api, proc sql	source	14
19	AI is Coming for Your BI: Automated Analysis in SAS Visual Analytics	Rick Styll	Automated analysis, a new feature in SAS Visual Analytics, uses machine learning to automatically suggest business intelligence (BI) visualizations for you. Powered by the SAS Platform, this analysis is performed quickly so that results are interactive. Once you choose a variable you are interested in, the most relevant factors are automatically identified and easy to compare. As a business analyst or manager, you can immediately understand the results in a report by using the automated narratives and familiar BI visualizations	predictive analytics, proc hpsplit, data science, group by, histogram	source	19
20	Airbnb New York City: Demystifying the Superhost Program	Soumya Ranjan Kar Choudhury	This paper investigates the relative importance of the qualifying factors stated by Airbnb to promote their Superhost program and makes an effort to discover other implicit factors like property name, descripti on, amenities, house rules that play a significant role in awarding the Superhost status. The goal of this project is to help hosts improve the experience of their guests and provide them with a framework that will guide them to increase their chances in the 'Superhost' program. The study utilizes the listings from New York City to predict the probability of becoming a Superhost based on text mining . Various statistical models such as Logistic Regression, LARS, SVM and Decision Tree were built to achieve this objective. Decision Tree was the best model with a misclassification rate of 12% and sensitivity of 52%	proc hpsplit, regression analysis, numeric variable, character variable, feature importance, odds ratio, stepwise selection, sas enterprise guide	source	7
21	Amazing End-to End Demo in Time: But Wait, There's More	Gert Nissen, Vegard Hansen	Amazing End-to-End Demo in No Time: But Wait, There's More… The time is now, and with the help of this unique product, you can achieve your ultimate goal - all tools available in one solution. In this infomercial, you will see how amazing a product this is and how it's changing the whole game. But wait, there's more! This super- demo covers the entire analytical lifecycle end-to-end on one integrated platform in no time-from SAS Add-in for Microsoft Office through SAS Data Integration Studio, SAS Data Preparation on SAS Viya, Model Studio, SAS Visual Analytics, SAS Lineage, and SAS Enterprise Guide in one demo. You will not ever need anything else, not sold in stores anywhere	sas programmer, sas enterprise guide	source	11
22	An ETL Macro Toolbox: Simplify How You Add Class Variables and Low Volume Limits	Zeke Torres	The Center for Medicare and Medicaid Services (CMS) Virtual Research Data Center (VRDC) is a resource that has strict stipulations on how to access and report on data. The ETL- related macros covered in this paper help with understanding the data. Also included is a useful `SUMMARY` procedure utility to help with obtaining useful facts from data (not just VRDC data). This set of ETL macros also includes a way to set Low Volume Limits and satisfy the reporting requirements of the VRDC. That "Low Volume Limits" format and the function in this tool box allows for an easier way of reporting and downloading data and meeting the requirements of the VRDC	proc step, proc template, proc sql, proc format, proc freq, sas base, sample, sas enterprise guide, proc means, macro definition	source	22
23	An Introduction to the ODS Destination for Word	David W. Kelley	The SAS Output Delivery System (`ODS`) destination for Word enables customers to deliver SAS reports as native Microsoft Word documents. The `ODS` WORD statement generates reports in the Office Open XML Document (.docx) format, which has been standard in Microsoft Word since 2007. The .docx format uses ZIP compression, which makes for a smaller storage footprint and speedier downloading. `ODS` WORD is preproduction in the sixth maintenance release of SAS 9.4. This paper shows you how to make a SAS report with `ODS` WORD. You learn how to create your report's content (images, tables, and text). You also learn how to customize aspects of your report's presentation (theme and styles). And you learn how to enhance your report with reader-friendly features such as a table of contents and custom page numbering. If you're cutting and pasting your SAS output into Microsoft Word documents, then this paper is especially for you!	proc step, proc template, excel, proc print	source	17
24	An Introduction to the Process of Improving a Neural Network	YuTing Tian	The top-level goal of this paper is to suggest a template for the process of building a high performing Deep Neural Network in SAS. It is hoped that a reader can use the process, shown in this paper, as a template for their own work in deep neural networks. Even though deep neural networks are popular, not many papers discuss them in the context of a supporting, overall process of building a neural network. A second goal is to show details of several "tabs" in Enterprise Miner - especially the powerful, but often overlooked, Auto-neural tab. A lower level goal is to build a network that can outperform a network in a paper by one of my professors. Deep learning is a kind of neural network and a specific kind of machine learning (e.g . artificial intelligence). Deep learning is a recent and powerful machine learning algorithm that enables a computer to build a multi-layer non-linear model	data mining, neural network, proc hpsplit, missing value, feature selection, deep learning, confusion matrix, proc varclus, proc step, outlier, data cleaning	source	22
25	Analysis of Technological Adoption in Rural Healthcare Among Souther States	Jujit Kunwor	Presentation		source	8
26	Analyzing Amazon's Customer Reveiws using SAS Test Miner for Devising Product Launch Strategies	Manideep Mellachervu	Presentation		source	8
27	Analyzing Restricted Mean Survival Time Using SAS/STAT	Changbin Guo, So Ying	Survival analysis handles time-to-event data. Classical methods, such as the log-rank test and the Cox proportional hazards model, focus on the hazard function and are most suitable when the proportional hazards assumption holds. When it does not hold, restricted mean survival time (RMST) methods often apply. The RMST is the expected survival time subject to a specific time horizon, and it is an alternative measure to summarize the survival profile. RMST -based inference has attracted attention from practitioners for its capability to handle nonproportionality. This paper introduces RMST methods in `SAS/STAT` software: you can now use the `RMSTREG` procedure to fit linear and log-linear models, and you can use the RMST option in `PROC LIFETEST` to estimate the restricted mean survival time and make comparisons between groups	regression analysis, proc step, restricted mean survival time, accelerated failure time, predictor variable, rank test, likelihood function, proc reg, proc lifereg	source	21
28	Analyzing Structural Causal Models using the CALIS Procedure	Banoo Madhanagopal, John Amrhein	Structural Equation Modeling (SEM) is a statistical technique to model hypothesized relationships among observed (manifest) and unobserved (latent) variables. SEM is not only widely applied in the social sciences, but is also suitable in areas such business, ecology, engineering, finance, pharmaceutical, and research. Under certain assumptions, a SEM can support causal inference as a Structural Causal Model (SCM). Path diagrams, commonly used with SEM, are visual representations of the hypothesized associations and dependencies and are particularly useful when studying causality. This paper describes how to formulate and interpret structural models as causal models. Using the PATH modeling language within the `CALIS` procedure, we fit SEMs for causal inference; we focus on model hypothesis and modification using fit statistics, but also briefly describe how to interpret model estimates to infer causality from direct and indirect effects	covariance, proc step, proc calis, correlation, latent variable, causal inference, proc corr, sas log, goodness of fit, nominal variable, raw data, normal distribution, treatment effect, covariance structure, estimate	source	23
29	Anything You Can Do I Can Do Better: PROC FEDSQL VS PROC SQL	Cuyler Huffman	Structured Query Language (SQL) was implemented in SAS as `PROC SQL`. A benefit of the `SQL` Procedure is that you can write queries or execute SQL statements on a SAS dataset or in a database. Another benefit is the SQL language makes it capable to combine the functionality of a `DATA STEP` and multiple `PROC` steps all into one procedure. Although useful, `PROC SQL` is limited in that it can only make one database connection per query, and is not compliant with the American National Standards Institute (ANSI) SQL syntax. Due to this non-compliance interacting with ANSI standard compliant databases becomes more difficult. Due to these limitations with `PROC SQL` a new procedure, `PROC FEDSQL`, was introduced. `PROC FEDSQL` was introduced in SAS 9.4, and offers faster performance, ability to connect to multi ple databases in one query, increased security, broader support of data types, and full compliance with the ANSI SQL: 1999 core standard, among others	proc step, proc sql, select, libname statement, create table, sql pass-through, implicit sql pass-through, ansi sql	source	11
30	Application of Command-Line Interface Tools to Workflows Based on SAS	Shahriar Khosravi, Boone Tensuda	Presentation		source	7
31	Applying Propensity Score Methods to Complex Survey Data Using PROC PSMATCH	Patrick Karabon		propensity score, proc step, sample, sample weight, treatment effect, regression analysis, output, proc surveylogistic, propensity score matching, proc genmod, response variable, population, survey weight	source	10
32	Architecting and Administering an Automated Financial Reconciliation Methodology with SAS BI	Alejandro Farias, Drew Turner	Federal and state governments, and private industries spend countless hours performing manual reconciliations between multiple accounting and transactional systems. Pulling unstructured information from two or more systems and trying to manually determine unreconciled items was an area that Texas Parks and Wildlife Department (TPWD) targeted to improve. This paradigm shift, led by automated reconciliation, helped the agency achieve significant time efficiencies by re-designing processes and freeing up staff time to focus more on analyzing and addressing differences. Most SAS Business Intelligence (BI) implementations center on various types of data analytics. However, by expanding the SAS BI toolset into other areas such as financial reporting and automating reconciliation, TPWD further justified the cost of using SAS BI	sas enterprise guide, reconciliation report, data profiling	source	10
33	Are You a Lifelong Learner? Validate Your Skills and Stand Out with SAS Certifications!	Andrew Howell	Why get certified? What's new? How do you set yourself apart from the rest? And how to nail the SAS Certification exams? In 2019, SAS Certification celebrates 20 years. Just as SAS continues to evolve, so does its certification program, as demand increases for highly skilled SAS consultants. To date, most certification examinations have been multiple-choice tests. In 2019, more practical tests are coming-not just for the predictive modeling certification, but for programming certifications as well-requiring candidates to access SAS to create and execute code as part of the certification exam. In this session, we review everything you need to know to prove your worth as a SAS certified consultant: the certifications that are available in programming, data management, modeling, administration, visual analytics, data science, and more	sas viya, regression analysis, sas 9, data mining, logistic regression, sas studio, machine learning, macro facility, sas programming, logistic distribution, anova, macro definition, sas base, sas enterprise guide	source	12
34	Asking the Right Questions: A study in Multiclass Imbalanced Classification for H1-B Visas	Matthew Bunker, Owen Kelly	A shortage of skilled resident US workers has resulted in the need to import workers from other countries. The H1-B visa lottery system helps fill these needs. This paper investigates sampling methods that can be used to predict the outcome of an H1-B application with an aim to contain application processing costs for both government and private industry. It specifically investigates how the imbalanced nature of the outcome of cases can adversely affect the predictive power of differing models and ways to address this. Reclassifying the target into different binary variables for government and private industry showed benefits of different modeling techniques. The best model for the Government focuses on forecasting withdrawals with a Balanced Gradient Boosting model. For Private industry the best model focuses on predicting Certified results with an 80% Train Decision Tree model	response variable, proc hpsplit, training set, neural network, binary variable, data mining, regression analysis, sample, data cleaning, nominal variable, imbalanced data, stepwise selection	source	7
35	Augmented Reality (Ar) Predictive maintenance System with Artificial Intelligence (AI) for Industrial Moblie Robot	Yee Yang Tay, Ye Sheng Koh, Marvin Dares	Modern machines are equipped with a plethora of sensors, generating plenty of data. However, without the necessary analytical tools and work flow in place, the readings of these sensors often leave plenty of untapped potentials on the table. In addition, a factory could possibly deploy machines of different varieties and makes, which leads to increased complexity of maintenance, and an increased need for technical know-how. The lack of these would hamper maintenance efforts as well as prolong downtime. Thus, with the onset of Industry 4.0, the ubiquity of sensors leading to large volume of data together with the advancements made in artificial intelligence, will lead to increased productivity as well as enabling the automation of systems. This project aims to demonstrate the concept of predicting machine faults by manipulating advanced data analysis techniques and enhancing maintenance efforts through the use of Augmented Reality	gradient boosting, machine learning, predictive maintenance, data cleaning, raw data	source	12
36	Automate in a DASH with SAS: Time Saving Techniques for Building Quality Improvement Dashboards	Shavonne J. Standifer	Building programs that leverage the analytic and reporting powers of SAS reduce the time to solution for critical tasks. This paper discusses, through example, how Base SAS tools, such as `FILENAME`, MACROS, and `ODS` combined with the "built in scheduler" housed in SAS Enterprise Guide can be used to automate the process between raw data to dashboard view quickly and efficiently. This paper is divided into two main parts. In the first part, we discuss how to use scripting language to bring data from a file location into the SAS environment, how to build programs that clean and subset the data, and how to transform the process with a MACRO. In the conclusion, we will discuss how to use SAS procedures and the `ODS` to transform the resulting data into a quality improvement dashboard view that can be set to automatically run and send to team members at a scheduled time	sas enterprise guide, raw data, filename statement, macro variable, options statement, call symput, proc freq	source	10
37	Automated Dynamic Data Exchange (DDE) Replacement Solution for SAS GRID Computing	Ajay Gupta	In the pharmaceutical and contract research organization (CRO) industries, Microsoft `EXCEL` is widely used to create mapping specs and in reporting. SAS enables you to export data into an `EXCEL` spreadsheet and do `EXCEL` formatting in many ways. But, Dynamic Data Exchange (DDE) is the only technique that provides total control over the `EXCEL` output. DDE uses a client/server relationship to enable a client application to request information from a server application. SAS is always the client. In this role, SAS requests data from server applications, sends data to server applications, or sends commands to server applications. Unfortunately, DDE is not supported on SAS Grid Computing. This paper explores a replacement solution for DDE on a SAS grid by using a SAS Stored Process, Microsoft Visual Basic for Applications (VBA), and SAS Add-in for Microsoft Office. The paper also explores the automation process and extends the solution to format Microsoft Word documents	excel, sample, sas enterprise guide, ods excel, grid computing, sas grid	source	15
38	Automatic LITI Rule Generation for Information Extraction	Teresa Jade, Xu Yang, Christina Hsiao, Aaron Arthur	Have you ever wished that you could get assistance with writing rules in LITI (language interpretation for textual information)? Now you can! Information extraction in SAS Visual Text Analytics uses the LITI syntax, which is a powerful way to uncover useful information hidden in textual data. The extracted information can be used as new structured data for predictive models, for faceted searches, or can be added to reports to enable better business decisions. In this presentation and paper, we demonstrate how to use the rule generation feature in SAS Visual Text Analytics. We also explain how the rules are generated in the background and what you can expect from the resulting rule set. The resulting rules can be used directly in the model, edited by the user, or used in an exploratory way to find new candidate rule elements. We provide tips and best practices for using rule generation to build your information extraction models faster and easier	adverse events, sample	source	14
39	Automating Migration from the SAS Macro Language to the LUA Procedure Using Transpiling	Igor Khorlo	ManysolutionsbasedonSASusetheSASmacrolanguagetostructure,automate,ormake SAS code more dynamic. When you scale out to a large application with thousands of lines of macro code, SAS macro usually becomes a bottleneck in the development and starts to hit its limitations. Maintenance, debugging, and testing can become a challenging and time- consumingprocessevenforexperiencedSASprogrammers. TheLualanguagewasdesigned tobeembeddedintoapplicationstoprovidescriptingfunctionality. Comparedtoalternatives likePROCGROOVYthatrunsinaseparateJVMprocess,PROCLUA(introducedinSAS9.4M3) runsinsideaSASprocessand,asaresult,hasaccesstotheCfunctionbindingsthatSAShas for reading, writing data sets, and so on. With that plus its performance, small footprint, elegant syntax, and support for data structures, the `PROC LUA` became a very promising replacementforaSASmacroforbigprojects	macro definition, proc step, proc lua, macro variable, macro loop, macro facility, %if-%then-%else, macro function	source	12
40	Automating SAS Viya Administrative Tasks Using Chatbot	Sandeep Grande	Presentation	sas viya	source	8
41	Avoid Stranger Danger: How to Leverage SAS Customer Intelligence Solutions to Avoid the Real Risks	Mia Patterson-Brennan, Amy Glassman	Do you know who is visiting your website? Have they lurked here before and are they ready to purchase-or worse yet, they just purchased and you don't even know it? Marketers know the risk of treating customers like strangers; research shows that consumers equate non-personalized brand interaction with a poor customer experience, and results show that personalized recommendations have a 5.5 times higher conversion rate. Marketers strive to provide the highly personalized, relevant messaging that consumers now expect with each interaction, but they are challenged to manage consumer identity across all channels in real time, and to use the real-time interactions to improve consumer profiles and targeting. In this paper, we share how SAS Customer Intelligence solutions helped a professional sports team identify and understand their prospects and customers better to improve consumer experience and marketing effectiveness across online and offline channels	customer experience, transaction data	source	14
42	Batch File Exchange between SAS and Excel - the Magic of Call Execute	Dadong Li, Eduardo Iturrate	Presentation	call execute, excel	source	3
43	Be Prepared: An Introduction to SAS Data Preparation	Mary Kathryn Queen	"Be Prepared" is not just a motto for the Boy Scouts of America; it is also an important concept for your data, on which you base your business decisions. SAS Data Preparation powered by SAS Viya provides self-service capabilities for preparing your data to create more consistent and accurate reports or analytic models, which ultimately lead to better and more informed business decisions. This presentation walks through using the profiling and data cleansing features of SAS Data Preparation to show how a non-technical person can use the point-and-click interface to prepare their data	data cleaning, sample, sas viya, data profiling	source	16
44	Best Practices for Business-Critical SAS Applications	Ian Munro	SAS is increasingly being used in many organizations for business-critical processes. The company I work for is a prime example. Kiwibank, New Zealand's fifth-largest bank, uses SAS for a wide range of risk-related processes, many of which are business-critical. These processes include the following: regulatory capital calculations on lending; account behavior scorecards; scorecard monitoring; IFRS 9 credit loss calculations; and loan -to-value ratio (LVR) lending restrictions monitoring. Ensuring that all these applications keep going every day, producing reliable and accurate results, is no mean feat. It starts by architecting and implementing a resilient and robust SAS environment. This environment then needs to be carefully aligned and integrated with the company's business and IT infrastructure. Next, disciplined operational practices are required, including essential housekeeping, to keep all systems running smoothly	sas administrator, sas enterprise guide, batch job, macro definition	source	7
45	Boop-Oop-A-Doop! It's Showtime with SAS on Apache Hadoop!	Cecily Hoffritz	Iconic Betty Boop in the 1930s cartoon Boop Oop A Doop tamed a lion. Nowadays, SAS has tamed the elephant, the yellow Apache Hadoop one, and this paper shows you how it is done! Some Hadoop elephants live on land and others in clouds, and with the right SAS tools, you can sneak up really close to tame that data of yours! This paper is your easy-to-read SAS on Hadoop jungle survival guide that explains Hadoop tools for SAS9 and SAS Viya, the main Hadoop landscapes, and good practices to access and turn your Hadoop data into top-notch quality information. It is showtime with SAS on Hadoop!	hadoop, data cleaning, proc sql, create table, hive, sas viya, machine learning, etl developer	source	25
46	Bring Analytics into Your Day Job	Atrin Assa	If you rely on data but aren't analytics oriented, you're missing out on insights. Even worse, you could be sabotaging the decision-making that builds on insights from data. In this paper, we explore how you can bring analytics and analytical thinking into your decision- making by using SAS Visual Analytics. We examine everything from smart, automated tools that can help you find insights within your data to simple charts and rules that can help you determine the usefulness of your insights	correlation, scatter plot, histogram	source	6
47	Building a Portfolio To Have Limited Maximum Loss with SAS Model Implementation Platform	João Pires da Cruz, Carla Tempera, Manuel Fortes	Building a solid financial entity is the entire purpose of having a portfolio that is able to hold environmental impacts because we expect that as some assets depreciate, others simultaneously appreciate due to diversification. Here we present a model that uses time as diversification factor to build a solid portfolio that holds its value under severe macro economical changes. A theoretical model was built under two very generic assumptions and SAS Model Implementation Platform was used to evaluate the model using empirical data taken from a real portfolio and to stress the assumptions to extreme environmental changes	credit risk, pd, credit portfolio, interest rate	source	12
48	Building a Propensity Score Model with SAS/STAT Software: Planning and Practice	Michael Lamm, Clay Thompson, Yiu-Fai Yung	Propensity score matching is an intuitive approach that is often used in estimating causal effects from observational data. However, all claims about valid causal effect estimation require careful consideration, and thus many challenging questions can arise when you use propensity score matching in practice. How to select a propensity score model is one of the most difficult questions that you are likely to encounter when you do matching. The propensity score model should consider both the tenability of the assumption of no unmeasured confounding and the covariate balance in the matched data. This paper discusses how you can use the `PSMATCH` procedure in conjunction with other procedures in `SAS/STAT` software to tackle some of these practical challenges. In particular, the paper demonstrates how you can use causal graphs to investigate questions related to ignorability and how you can incorporate propensity scores that are computed using approaches other than logistic regression	propensity score, treatment effect, potential outcomes, propensity score matching, standard deviation, proc adaptivereg	source	24
49	Calculating Lags and Leads in SAS	Andrew Gannon	This paper seeks to explain and demonstrate the many ways of calculating Leads , and to a lesser extent, Lags in SAS. `PROC EXPAND`, reverse sorting, data merge, `PROC SQL`, and application of data-set functions will be discussed. It will look at the practicality of each method and examples of the code structure of each. It will also analyze how much time and memory each method uses and present the findings to the readers	memory usage, proc step, proc sql, data merging	source	12
50	Can't Find the Right CAS Action? Create Your Own Action Using CASL	Brian Kinnebrew	SAS Cloud Analytic Services language (CASL) is a language specification in SAS Cloud Analytic Services (CAS) that provides a programming environment to manage the execution of CAS actions. The results of those CAS actions are processed with functions and expressions to prepare the parameters for subsequent CAS actions. CASL and the CAS actions provide the most control, flexibility, and options when interacting with CAS. In addition to the many CAS actions supplied by SAS , as of SAS Viya 3.4, you can create your own CAS actions using CASL. You specify the interface to your new CAS action, provide a name and parameters, and supply the CASL code that implements your new CAS action. Once created, your CAS action is available for use by any user that has permission. Your CAS actions look and feel just like CAS actions provided by SAS. Jump on board and find out how to create your own CAS actions that are tailor-made for your needs!	cas action, proc step, sas log, correlation, cas	source	10
51	Capture-Recapture Databases for Data for Good Projects	David J. Corliss	Capture-recapture (CRC) is a statistical methodology using multiple independent samples to estimate the size of an entire population. It is typically employed to estimate the size of hard-to-count populations. Originally developed by ecologists to estimate animal populations, capture-recapture has become an important analytic tool for social justice. CRC is used to estimate the number of people affected by a wide variety of problems, including crimes and natural disasters. In this method, careful design, development, and management of the underlying database are critical tasks. This paper demonstrates development and management of databases for capture-recapture analysis, including database organization, integrating additional data sources, addressing privacy issues, and database management and governance	sample, record linkage, unique key	source	10
52	Causal Analyses Using Structural Equation Models	Banoo Madhanagopal, John Amrhein	Structural Equation Modeling (SEM) is a statistical technique to model hypothesized relationships among observed (manifest) and unobserved (latent) variables. SEM is not only widely applied in the social sciences, but is also suitable in areas such business, ecology, engineering, finance, pharmaceutical, and research. Under certain assumptions, a SEM can support causal inference as a Structural Causal Model (SCM). Path diagrams, commonly used with SEM, are visual representations of the hypothesized associations and dependencies and are particularly useful when studying causality. This paper describes how to formulate and interpret structural models as causal models. Using the PATH modeling language within the `CALIS` procedure, we fit SEMs for causal inference; we focus on model hypothesis and modification using fit statistics, but also briefly describe how to interpret model estimates to infer causality from direct and indirect effects	covariance, proc step, proc calis, correlation, latent variable, causal inference, proc corr, sas log, goodness of fit, nominal variable, raw data, normal distribution, treatment effect, covariance structure, estimate	source	23
53	Causal Graph Analysis with the CAUSALGRAPH Procedure	Clay Thompson	Valid causal inferences are of paramount importance both in medical and social research and in public policy evaluation. Unbiased estimation of causal effects in a nonrandomized or imperfectly randomized experiment (such as an observational study) requires considerable care to adjust for confounding covariates. A graphical causal model is a powerful and convenient tool that you can use to remove such confounding influences and obtain valid causal inferences. This paper introduces the `CAUSALGRAPH` procedure, new in `SAS/STAT` 15.1, for analyzing graphical causal models. The procedure takes as its input one or more causal models, represented by directed acyclic graphs, and finds a strategy that you can use to estimate a specific causal effect. The paper provides details about using directed acyclic graphs to represent and analyze a causal model	treatment effect, response variable, proc step, causal inference, instrumental variables	source	21
54	Celebrity Makeover: A Fresh and Modern Look for SAS Enterprise Guide	David Bailey, Samantha DuPont, Casey Smith	The next generation of SAS Enterprise Guide is here! The redefined SAS Enterprise Guide 8.1 is both sexy and intelligent. SAS Enterprise Guide 8.1 sports a modern and attractive interface, tab-based organization of content, and flexible window management such as floating, docking, and leveraging multiple monitors. Want to view your code, log, and results at the same time? SAS Enterprise Guide 8.1 has you covered. Want to view four data sets at the same time? Sure, no problem. Want to just code, without a project? You got it. See these features and more at the big reveal of SAS Enterprise Guide!	sas enterprise guide, sas studio	source	15
55	Choice Model Analysis Using JMP	Swarup C. Jacob, Marc A. Tower	Choice experiments help discover which product or service attributes a potential customer prefers. Choice models are an excellent tool to analyze the results of choice experiments. Choice models help a researcher to estimate the probability of individuals making a particular choice from presented alternatives. A choice experiment studies customer preference for a set of product attributes. Consumers are presented sets of product attributes, called profiles. Each respondent is shown a small set of profiles, called a choice set, and asked to select the preference that he or she most prefers. Each respondent is usually presented with several choice sets. The choice model presented in this study aims to analyze the business travelers' choice of travel options given the options available. A randomized set of options are presented to the user from a predefined choice set. The attributes for each travel choice includes Mode, Time, Cost, Privacy, Flexibility and Productivity	likelihood function, missing value	source	6
56	Choosing the Best Method for Provider Performance Issue Tracking: SAS or IBM Cognos	Ana Brito Skalos, Meghna Parthasarathy, Mark J. Mihalyo, Mary Doyle	Community Care Behavioral Health Organization (CCBH) in Pittsburgh, Pennsylvania is a behavioral health managed care organization with a large provider network that works with people to obtain clinical assistance and connects them to mental health and substance use treatment options. Studying the performance of different providers in this large network is of utmost importance because of the significant role these providers play in ensuring that our patients get the best possible treatment. Community Care's Quality Department designed specifications around different levels of care delivered by these providers and the Decision Support team at CCBH developed a report using IBM Cognos Business Intelligence. However, programming, executing, and delivering the reports via IBM Cognos proved to be challenging, and the paper addresses the various ways in which these challenges were minimized or overcome by using SAS	create table, proc sql, proc step	source	11
57	Collaboration Calibration: How SAS Data Scientists Collaborate Effectively Using SAS Viya	Weston McManus, Allison Becker	SAS Viya seems great, but how can teams collaborate with each other to analyze big data? Who owns the data? What about security? And where do the reports live? Is there a best practice? If you've found yourself asking these questions, you're not alone-here at SAS, we experienced them first! In this paper, we walk through the steps of how we are establishing a collaborative environment so that your team can get the job done	sas viya, data science, sas studio, raw data	source	11
58	Color Your Experience: Crafting Appealing Themes Using SAS Theme Designer	Lora Edwards, Lisa Everdyke, Corey Benson	SAS Theme Designer provides customers with a powerful set of options for customizing SAS application and report themes. Apply sweeping changes to your theme with ease by leveraging the structure of SAS themes, and then override key features to refine the look. Strike the right color balance by mapping your brand palette to theme variables that are intelligently applied across the software. Create complimentary application and report themes to unite your interface controls and data visualizations. Preview your theme on a set of general controls within SAS Theme Designer. This paper explores best practices for optimizing a custom look and feel that reflects your brand and that provides a great experience for your users	sample, data visualization	source	15
59	Combating Fraud and Financial Crime Effectively by Empowering Data Scientists Using SAS Viya	Steve Overton	Building a productive financial crime monitoring system to detect suspicious activity begins and ends with the data. Access to production data ensures that data scientists and strategy developers can design scenarios with actual behavior patterns and data profiles on hand - not by using theoretical or mocked activity, which is not representative of real-world actions to be analyzed and modeled. Architecting a specialized analytical SAS Viya sandbox environment, with Read-Only access to production data, provides a powerful toolbox to productively analyze and prepare data; design, build, and tune analytical models; and visualize results efficiently. This paper provides best practices for leveraging SAS Viya technologies within an analytical sandbox environment, including SAS Visual Analytics, SAS Visual Statistics, SAS Data Preparation, and SAS Visual Data Mining and Machine Learning	sas viya, financial crime, sas studio, data science, data governance, machine learning	source	10
60	Commodities Risk Easy Button: Configure Advanced Risk Analytics from Scratch in 20 Minutes or Less	Albert Hopping, Eric Carriere, Michael Busby, Christine Jackson	There is often a misconception that performing risk analysis must be a major endeavor that requires your institution to have advanced quants and technical skills in house. However, with SAS Commodity Risk Analytics, the needed expertise is built in, but the risk platform is still fully customizable. The easy-to-configure interface runs in your browser and can have you understanding your risk in minutes. Explore your VaR and other risk metrics, and drill down to the position level on the fly. Enable traders to understand the impact of their trades before they make them	correlation, sas risk engine, monte carlo, data cleaning	source	13
61	Comparing Methods for Dynamically Updating Data: The MODIFY Statement, Temporary Arrays, and Hash Objects	Richard D. Langston	This paper explains the implementation of an account voting mechanism that enables dynamic updating of data. Three implementation methods are compared: using the MODIFY statement, using temporary arrays, and using hash objects. This paper also explains how the three methods impact performance	hash table, modify statement	source	7
62	Comparing Multiple SAS Functions for Text Field Matching in Data Linkage: SOUNDEX, NYSIIS, COMPGED	Yu Sun, Cordell Golden	Presentation		source	5
63	Comparing SAS Viya and SAS 9.4: How Their Features Complement Each Other	Mark L. Schneider	SAS Viya extends the SAS Platform in a number of ways and has opened the door for new SAS software to take advantage of its capabilities. SAS 9.4 continues to be a foundational component of the SAS Platform, not only providing the backbone for a product suite that has matured over the last forty years, but also delivering direct interoperability with the next-generation analytics engine in SAS Viya. Learn about the core capabilities shared between SAS Viya 3.4 and SAS 9.4, and about how they are unique. See how the capabilities complement each other in a common environment. In addition to these core capabilities, see how the SAS software product lines stack up within each one, including analytics, visualization, and data management. Some products, like SAS Visual Analytics, have one version aligned with SAS Viya and a different version with SAS 9.4	sas viya, sas base	source	2
64	Condition-Based Monitoring Using SAS Event Stream Processing	Anya McGuirk, Yuwei Liao, Byron Biggs, Deovrat Kakde, Joseph Costin	Condition-based maintenance (CBM) of critical high-valued machines is a typical Internet of Things (IoT) scenario. Key to CBM is monitoring important machine health parameters, such that maintenance can be based on the perceived condition of the machine, rather than performing preventive maintenance at intervals, where the likelihood of failures between repairs is very small, or performing corrective maintenance by running the machine until it fails and repairing it. Vibration data analysis is a key tool for understanding the internal condition of a machine, often when it is running continuously. This paper illustrates how to use the new digital filters, time-frequency analysis tools, machine learning algorithms available in SAS Visual Data Mining and Machine Learning, and SAS Event Stream Processing to monitor the real-time condition of a "live" variable-speed rotating machine by analyzing vibration sensor measurements obtained at sampling rates higher than 10,000 Hz	stream processing, real-time processing	source	19
65	Conducting Tests in Multivariate Regression	Chii-Dean Joey Lin	Linear regression models are used to predict a response variable based on a set of independent variables (predictors). Multivariate regression is an extension of a linear regression model with more than one response variable in the model. In a linear regression model, a linear relationship between the response variable and the one or more predictors is assumed. In addition, the random errors are assumed to follow a normal distribution with a constant variance and are also assumed to be independent. In conducting a multivariate regression analysis, the assumptions are similar to the assumptions of a linear regression model but in a multivariate domain. In this paper, we first review the concepts of multivariate regression models and tests that can be performed. In correspondence with the tests under multivariate regression analyses, we provide SAS code for testing relationships among regression coefficients using the `REG` procedure	response variable, regression analysis, predictor variable, proc reg, multiple regression, proc step, correlation, normal distribution, covariance, null hypothesis, proc discrim, p-value, estimate	source	13
66	Conveying Your Brand Identity	Robby Powell	The most successful brands focus on users, not buyers. Engage with your internal and external users through digital dashboards and mobile apps that echo the value of your brand and deliver precise information to support decisions. See how you can convey your brand identity through your dashboards and custom mobile apps. In this paper, you learn how to use the capabilities in SAS Theme Designer, SAS Visual Analytics Graph Builder, reusable object templates, and the SAS SDK for iOS and SAS SDK for Android	sample, bubble plot	source	10
67	Cool SQL Tricks	Russell M. Lavery	This paper is a collection of eleven tips and tricks using SAS `PROC SQL`. It is intended for an intermediate SAS person who would like to take a peek under the hood. Code for the H.O.W. is included in Appendix A. That code has the same section organization as this paper and an interested person can read this paper while executing the code. That process will provide examples of the concepts developed below and, largely, duplicate an H.O.W. attendance. To increase your chance of attending another SAS Global Forum, you can use this code to provide a lunch-and-learn for your company. Topics covered are: 1) the SAS data engine, 2) indexing, 3) the optimizer, 4) correlated and uncorrelated sub-queries, 5) placement of sub-queries in SQL syntax, 6) views, 7) fuzzy merging, 8) coalescing, 9) finding duplicates using SQL, 10) reflexive joins, 11) Using SQL dictionary tables and a macro to document SAS tables	proc sql, proc step, where statement, create table, order by, inner join, proc print, create view, select, excel, full join, pdv, xls, missing value, proc format, dataset, if-then-else, macro variable, having, data merging	source	53
68	Cows or Chickens: How You Can Make Your Models into Containers	Hongjie Xin	Models are specific units of work that have one job to perform: scoring new data to make predictions. Containers are self-contained workers that can be easily created, destroyed, and reused as needed. They are portable and easily integrate into numerous modern cloud and on-premises execution engines. SAS users can now follow a recipe to turn advanced model functions into on-demand containers such as Docker for both on-premises and cloud deployment. SAS Model Manager can be used to organize the model content from many sources, including SAS and open source, to create containers. This presentation presents the basics and shows you how to turn your SAS analytical models into modern containers		source	20
69	Create a Combined Graph of Tumor Data	Cleester D. Heath, Sanjay N. Matange	Traditionally, tumor response and duration of treatment information have been displayed in separate plots in which the subjects can be sorted by different criteria. In such cases, the clinician must work harder to associate the subject across the plots. Recently there has been increased interest in combining this information in one visual. Displaying the data together, sorted by the tumor response with associated duration information, makes it easier for the clinician to understand this information. Three-dimensional waterfall plots, which have both pros and cons, have been proposed for such cases. This paper shows you how to build a 3-D plot using SAS that shows both tumor response and duration of treatment. This paper also presents alternative 2-D visuals that were created using the `SGPLOT` procedure. These 2-D visuals enable easier decoding of the data, which enables you to display more information in the plot	waterfall chart, swimmer plot, proc sgplot	source	15
70	Create Multi-arm CONSORT Diagrams the Easy Way with SAS	Prashant Hebbar, Sanjay N. Matange	In clinical trials, Consolidated Standards of Reporting Trials (CONSORT) flow diagrams are an important part of the randomized trials report. These diagrams present a bird's eye view of the flow of patients through the different stages of the trial. The SG procedures do not support a statement for drawing these diagrams. But by completing some data processing steps, you can draw commonly used CONSORT diagrams by using the `SGPLOT` procedure. You can generate the output in all the formats that are supported by the Output Delivery System (`ODS`)-and not just RTF! In this paper, we show you how to harness the power of the `SGPLOT` procedure to create these diagrams	proc sgplot, macro variable, proc step	source	10
71	Create True Load Sharing Facility Batch Job Submission Capability on SAS Enterprise Guide in a SAS Grid	Adam Hendricks, Derek Grittmann	Presentation	batch job, sas enterprise guide, batch processing, data science, full load, sas batch	source	14
72	Creating a comprehensive risk-aware business culture using SAS Risk Management products	Kelly Potter	The Thompson Reuters Cost of Compliance 2018 study surveyed global systemically important financial institutions on their expectations for costs associated with regulatory compliance over the next year. Seventy six percent of participants expected an increase in focus on managing regulatory risk in the upcoming year. These institutions expected that they would prioritize General Data Protection Regulation (GDPR) readiness, policy management, risk identification and control effectiveness, and the assessment of the impact that regulatory changes have on business. Since the financial crisis of 2008, this shift in focus on regulatory obligations within financial institutions has become a driving force for the creation of software that can be customized, enhanced, and easily updated by the user. These changes are often necessary to keep pace with the ever-changing regulatory efforts of governments around the world	model risk management, feature selection	source	9
73	Creating a Strong Business Case for SAS Event Stream Processing	Henrique Consolini Danc	SAS Event Stream Processing has a set of great capabilities that both IT and business areas can easily benefit from. This technology enables seizing opportunities and spotting red flags that usually are hidden in the massive torrent of fast -moving data that flows through our business, without the need for storing any data, by applying simple business rules, pattern detection, geofencing, and real-time training and scoring of analytical models. However, it is not always easy to translate these specific features into direct business benefits for business groups, which usually hold the budget for projects like these. Therefore, it is crucial to demonstrate to business users how they will benefit from this technology and how the features will help them take faster action and achieve better results	stream processing, real-time processing, customer satisfaction, data cleaning, machine learning, data validation rules, regression analysis, data governance	source	16
74	Creating an Effective Environment for Learning SAS Programming Online	Justina M. Flavin	Knowledge of SAS programming provides excellent employment prospects in a variety of industries and can lead to a challenging and rewarding career. However, learning the SAS programming language can be daunting, especially when undertaken alongside a full time job and family obligations. This paper will discuss the author's experience in developing and delivering effective online SAS programming courses and present strategies and ideas to promote successful learning and knowledge transfer when teaching a diverse student population of working professionals	sas programming, dataset, proc step, sas enterprise guide, macro definition	source	7
75	Creating and Controlling JSON Output with PROC JSON	Adam Linker	JSON is continuing to grow as the preferred data interchange format due to its simplicity and versatility. The `JSON` procedure gives SAS users the ability to export SAS data sets in JSON, as well as the ability to create custom JSON output. The procedure is simple to use and gives the user a huge amount of flexibility and control of the JSON output. This paper gives an overview of how to use the `JSON` procedure as well as detailed use cases to highlight the most important options to get the most out of the output generated by the procedure	ods json, proc json, proc step	source	13
76	Creating Great Maps in ODS Graphics Using the SGMAP Procedure	Kelly Mills	With the introduction of the `SGMAP` procedure to SAS `ODS` Graphics, geographic mapping has never been easier. This paper shows you how to unlock the geographic potential of your data in just a few statements to create rich, meaningful maps showing locations, regions, pathways, and labels. By mixing and combining statements, you can highlight important aspects of your data or reveal information hidden in the surrounding geography. With `PROC SGMAP`, the world is at your fingertips!	proc step, sas base, output delivery system, response variable, proc import	source	11
77	Creating Lists! Using the Powerful INTO Clause with PROC SQL to Store Information in Macro Lists	Julie Melzer	Presentation	proc sql, proc step, macro variable list	source	7
78	Cryptosystem for Protecting Personal Information and Data Visualization using SAS Visual Analytics	Patrick Sekgoka, Olufemi Adetunji	Data comprising private and confidential information is often stored with multiple records per subject in a variety of fields. It is important to know how to process such data sets with grouped observations while complying with information privacy laws and policies. In this paper, we demonstrate how to use temporary automatic variables such as `_N_`, First.BY - variable and L ast.BY-variable from the SAS Program Data Vector (PDV) to implement a cryptosystem for protection of personally identifiable information in a data set comprising cross-border financial flows. We also construct a bipartite graph using a network diagram in	pii, automatic variable, data protection, by group processing, transaction data	source	8
79	Customized Output with the SAS Config File: Always Useful but Seldom Used	Zeke Torres	The SAS config file is powerful and useful. In this example, we show how to customize the names of the Log and List files from the code we run, and obtain a useful new name as the	macro facility, macro definition, proc printto, proc step	source	11
80	Data Governance: Harder, Better, Faster, Stronger	Vincent Rejany	All the major trends call for advanced control and accountability toward the use of data: from the migration to cloud applications and data warehouses, to the deployment of big data environments, the democratization of analytics and artificial intelligence or the increasing requirements related to data privacy and data protection. Data Governance turned from being a nice to have to a must have, with an ever-expanding scope to address; gone are the days of marketing databases, some ERP processes, or specific regulations such as Solvency 2 or BCBS239 being the limits. Most of the organizations came through strong challenges aligning people and processes, trying to sustain the governance effort the time, and progressively this dream of "Enterprise Data Governance" is fading	data governance, data cleaning, data wrangling, privacy, reference data, artificial intelligence, data profiling, quality control, outlier, data validation rules, proc freq	source	18
81	Data Modeling for a Better Analytical Environment	Linus Hjorth	Developing and maintaining an analytical environment can be hard-especially if your area of interest is complex, involves more than one subject area, and has several source systems that needs to be integrated. There are several tools available to us when developing and maintaining such an environment, usually a data warehouse; examples are data and analytical processes, and documentation. One of the neatest parts of documentation (and communication) is the use of information and data models. In this paper, I discuss how to use information models and some of the data modeling techniques that can be used in a data warehouse. I also show how to exchange data models between data modeling tools and SAS metadata, and tools like the ETL tool in SAS Data Integration Studio	data standard, data warehouse, data validation rules, star schema	source	12
82	Data Preparation in the Analytical Life Cycle	Ivor G. Moan	The term data preparation summarizes all tasks that are done when collecting, processing, and cleansing data for use in analytics and business intelligence. These tasks include accessing, loading, structuring, purging, unifying (joining), adjusting data types, verifying that only valid values are present, and checking for duplicates and uniform data (for example, two birthdates for one person). Data preparation can be costly and complex because of increasing data quantity and the number of data sources. Data preparation is a new paradigm that is now shaping the market. Previously, data management concentrated on designing and running extract, transfer, load (ETL) and data quality processes in order to feed analytic processes. This situation was all very well when data volumes were smaller and the velocity of new data was slower. We're now seeing a trend toward more dynamic data management, with data preparation playing the role of self-service data management	data wrangling, data governance, data cleaning, data science	source	8
83	Data Quality Programming Techniques with SAS Cloud Analytic Services	Nicolas Robert	SAS Cloud Analytic Services (CAS) provides advanced and powerful analytic capabilities on big data, both in-memory and in-parallel. But what do your reports, models, or decisions look like if your data is not of good quality? How do you improve the quality of big data by using distributed processing and in-memory techniques? CAS offers not only numerous actions for analyzing big data, but it also provides big data preparation and big data quality capabilities from a user interface perspective, as well as from a programming perspective. This paper focuses on the programming techniques that are available to perform common data quality tasks in parallel on in-memory (big) data	cas action, data cleaning, sas viya, big data, proc freq, by group processing	source	10
84	Data-Driven Agent Allocation in a Process using Machine Learning and Optimization in SAS	Lokendra Kumar Devangan	Data-driven agent allocation provides immense opportunity in improving the efficiency of any process. Cost effective system can be designed using machine learning and optimization in SAS. In this paper, the application of machine learning and optimization for agent allocation in two-stage job processing is described using a case study of Business Process Outsourcing (BPO) organization. This organization handles verification and underwriting process for credit card applications of a large bank . The paper provides a brief overview of unsupervised machine learning algorithms for agent and application profiling. Clusters of application and agents are created and are used in the agent allocation optimization problem. Optimization model framework is extensively discussed to solve the problem of skill-based agent allocation for credit card application processing based on its complexities , which has two stage s of processing, application verification, and underwriting	odds ratio, cpu time, proc step, credit card, machine learning, resource allocation	source	9
85	Data4Good helping IOM forecast logistics for refugees in Africa	Colin Nugteren		machine learning, cluster analysis	source	12
86	Dealing with Exact Confidence Interval of Binary Endpoint Data	Kamleshkumar Patel, Vaishali Patel	Presentation: Programmers come across various binary data when working on data analysis. However, when working on binary endpoint, one of the challenges is getting correct confidence interval (CI) for proportion. In SAS, most of the programmers get confidence interval using `PROC FREQ` procedure. Under certain scenario, `PROC FREQ` calculates confidence interval for proportion of another group and hence, it turns out to be incorrect confidence interval for proportion of the group programmers have requested. Here, we will present a simple but accurate way to get confidence interval of proportion. We will present a macro to calculate confidence interval which is based on derivation as per statistical	confidence interval, proc freq, proc step	source	3
87	Deploying Models Using SAS and Open Source	Jared Dean	In the excitement and hype around machine learning (ML) and artificial intelligence (AI) most of the time is spend in the model building. Much less energy is expended on how to take the insights from models and deploy them efficiently to create value and improve business outcomes. This paper will show a complete example using DevOps principals for building models and deploying them using SAS in conjunction with opens source projects including Docker, Flask, Jenkins, Jupyter, and Python. The reference application is a recommendation engine on a web property with a global user base. This use case forces us to confront security, latency, scalability, repeatability. The paper will outline the final solution but also include some of the problems encountered along the way that informed the final solution	machine learning, data science	source	10
88	Designing and Sustaining a Successful Student Led Program	Bonnie Chapman, Nick Armstrong	Student employees are an extremely valuable, but often underutilized asset. An organization must be willing to invest in a student employment program that benefits the organization while staying focused on the responsibility to educate and prepare students for the modern workforce. The Institute for Veterans and Military Families (IVMF) at Syracuse University, higher education's first interdisciplinary academic institute that is focused on advancing the lives of the nation's military veterans and their families, has developed an internship program that is predominately led and managed by the students themselves. This presentation details the development of the student internship program, the types of projects students are given, and the management and cultural elements that the IVMF attributes to its success. In the IVMF program, an experienced graduate student is selected to lead and manage the other students, encouraging their own growth as well as reducing the time costs to staff	business intelligence, data visualization	source	6
89	Detecting Fraud and Other Anomalies Using Isolation Forests	Ryan Gillespie	Methods of fraud and other financial crimes are becoming increasingly sophisticated. One method of detecting fraud is to use unsupervised machine learning techniques. In this breakout session, we'll explore how isolation forests can be used to detect anomalies without requiring previously labeled data. We'll also compare this method against other well-known anomaly detection methods to evaluate its performance on selected data sets	proc hpsplit, outlier, supervised learning	source	6
90	Detecting Human Trafficking? An Anti-Money Laundering Program and SAS Visual Investigator Can Help	Dan Tamburro	According to CNN, there are approximately 20 to 30 million slaves in the world today. Trafficking in Human Beings (THB) - often simply referred to as "human trafficking" - is the third largest international crime industry, behind illegal drugs and arms trafficking. THB generates a profit of $32 billion every year. Of that amount, $15.5 billion is made in industrialized countries. The United States, possibly surprisingly, is one of the highest THB destinations in the world. (Bales, 2017). Can an established anti-money laundering program assist with combating THB? The short answer is "yes." A July 2011 Financial Action Task Force (FATF) report best summarizes this as follows: "There is no specific guidance on money laundering associated with THB/SOM [smuggling of migrants] because the channels used, the instruments and the sectors implied are the same as for other criminal activities	machine learning, aml	source	13
91	Developing a Credit Risk Model Using SAS	Amos Odeleye	A credit risk score is an analytical method of modeling the credit riskiness of individual borrowers (prospects and customers). While there are several generic, one-size-might-fit-all risk scores developed by vendors, there are numerous factors increasingly driving the development of in-house risk scores. This presentation introduces the audience to how to develop an in-house risk score using SAS, reject inference methodology, and machine learning and data science methods	insurance risk score, sample, credit risk, credit score, logistic regression, regression analysis, logistic distribution, data cleaning, predictor variable, machine learning, response variable, data science, model validation, data transformation, proc princomp	source	16
92	Disclosure Control: Project Management Issues and Solutions	Peter Timusk	With many statistics, protecting the privacy of individual response data is legally mandated. Privacy of data is not a new topic in government statistics. In fact, much of the work involved in producing tables of statistical estimates can concern making sure data from individual respondents (be they businesses or persons) is kept confidential. The consideration of confidentiality needs to occur early in productions of tables of statistical estimates. The author explains some basics of statistical confidentiality calculations and explains why early consideration of confidentiality needs to happen in projects	sample, sas base	source	4
93	Discover Seven Gems for Getting the Most Out of SAS Visual Analytics Viewers	Lavanya Mandavilli, Joe Sumpter, Houda Moudouni, Achala Kamath	Go beyond a basic report-viewing experience with SAS Visual Analytics viewers. Discover seven precious gems in the viewers that enrich your report-viewing experience. With the nifty present screen feature, conduct a live report presentation with your geographically dispersed colleagues worldwide from your Apple iOS or Android mobile devices. With the new voice assistant feature, navigate, open, and interact with reports on iOS devices. Use the report playback and screenplay feature to create a customized report presentation. Create custom summaries for report objects and use the personalized state feature to save report object selections for drilling, filtering, and other interactions. Generate web address links to reports, objects, and images that you can share or copy and paste. Refresh only the report objects that you select. This paper is suitable for users of all experience levels		source	19
94	Distribution Transformer Health Monitoring and Predictive Asst Maintenance	Prasenjit Shil, Tom Anderson	Distribution transformer is the most vital asset in any electrical distribution network. Hence, distribution transformer health monitoring and load management are critical aspects of smart grids. Transformer health monitoring becomes more challenging for smaller transf ormers where attaching expensive health monitoring devices to the transformer is not economically justified. The addition of Advanced Metering Infrastructure (AMI) in smart grids offers significant visibility to the status of distribution transformers. How ever, leveraging vast amount of AMI data can be daunting. This paper uses the hourly usage data collected from Ameren Illinois' AMI meters to determine distribution transformer outage, failure, and overload. The proposed methodology not only detects and vi sualizes outage and congested areas in near real time, but also detects transformers and distribution areas with a long history of outage and congestion	proc hpsplit, boosting, response variable, sas enterprise guide, data mining	source	11
95	Does the Cat Have Your Tongue? Using the VNAME and CATX Functions to Get SAS to Speak to You	Gayle K. Springer	In our longitudinal health study, when certain health events occur, we restrict access to biological specimens from that time period. The reason or reasons for restricted access are maintained in a data table. Our challenge is to provide the reasons for restriction at the individual and population level for reporting purposes. When biological specimens are requested for use with a project, we must review the ID/visits requested against the list of restricted specimens. Then, the restricted ID/visits, along with the reasons, are provided to an oversight committee to determine whether the restricted specimens can be used for the project. We developed an easy method for creating the reason list in words by creating variable names that are succinct and descriptive. Using an ARRAY statement and the CATX and VNAME functions, we quickly compile the reason list to give to our oversight committee to aid in their decision	array, character functions, length statement, label statement, proc print, binary variable, proc step	source	6
96	Don't Just Survive, Thrive! A Learning-Based Strategy for a Modern Organization Centered Around SAS	Jovan Marjanovic	SAS is still the undisputed market leader in the commercial data science space. A strong global academic alliance provides a quality stream of SAS-literate graduates, and the educational offering of SAS is among the best in the industry. Still, almost all organizations focused on SAS face increasing challenges and complexity due to the proliferation of new programming platforms and paradigms, both in the industry and in academia. Inexpensive online learning options that mostly use alternative languages are already an established supplement, even an alternative, to academic or corporate training. They are one of the critical drivers of the democratization of data science. Publicly available SAS online courses remain mainly those developed by SAS, although the courseware developed by members of the SAS Academic Alliance is mostly unavailable on leading MOOC platforms	data science, machine learning, sas engineer, sas viya, sas programming	source	7
97	Don't leave it to the discretion of ODS Graphics: Use Discrete Attribute Map	Shilpakala Vasudevan	When working with visualization of grouped data, SAS `ODS` defines default attributes (colors, symbols, patterns) for each group in the graph. For example, if we were to plot line plots with markers for different groups in a graph: `ODS` styles would assign by default a line color, a symbol for the marker, and a marker color for each of the groups by itself. Although this may seem convenient, however, if there is an update in the data, the values for the groups might change or there might be missing groups in the data. This might cause the attributes for the groups to differ each time the graph is run. To aid in uniformity, we can use discrete attribute maps that assign set attributes to each group value. This consistency in the patterns and attributes then makes it easier to understand and review the graphs. In this paper, we will be seeing how to implement discrete attribute map sets with examples, and how it ensures an easy way to consistency in display of outputs	proc step, sample, proc template, class variable, proc sgplot	source	10
98	Don't Let Complex Survey Data Get the Best of You! SAS Survey PROCs for Categorical Data Analysis	Charlotte Baker	Data from US federal health surveys frequently use complex survey structures, rendering traditional procedures not useful for analysis. The SAS survey procedures exist but have not yet become a regularly used asset in analysis. Instead, users frequently choose to use other programs or add-ons for even the most basic of analyses. This paper demonstrates why the survey procedures such as SURVEYFREQ, SURVEYLOGISTIC, and SURVEYREG should be in everyone's toolbox when using complex survey data in research or practice	proc step, proc surveyfreq, proc surveylogistic, proc freq, proc surveyreg, nominal variable, proc reg, odds ratio, sample, data science, interaction effect, logistic distribution, proc logistic, regression analysis	source	16
99	DOSUBL Function + SQL View + Hash Object = FedSQL + PROC DS2 Hash Package	Yutaka Morioka, Jun Hasegawa	The `DS2` procedure brought the object-oriented concept to the world of SAS programming and enabled a high level of data handling that the `DATA STEP` could not reach for years. One detailed but noteworthy feature is that the hash package, a predefined package in the `DS2` procedure, enables us to assign a FedSQL query to the argument tag of the dataset method. By specifying a FedSQL query for the argument, you can directly store the result of the query into the hash object without creating an unnecessary subset data set beforehand. Despite this and other powerful advantages in data handling, the `DATA STEP` still prevails among SAS users, and in most cases, it makes us feel content to continue using the `DATA STEP`. Given the situation, this paper introduces an alternative way to achieve what the `DS2` procedure enables us to, by the traditional `DATA STEP` combining a hash object, the `SQL` procedure, and the DOSUBL function, which enables the immediate execution of SAS code	hash table, dosubl, proc sql, conditional do loop, call execute, merge statement	source	10
100	Dynamic Call Center Simulation in SAS: Revamping the Business Resiliency Process at JPMorgan Chase	Jay P. Carini, Amy Pielow, Joel C. Weaver	The JPMorgan Chase Operations Research and Data Science Center of Excellence (ORDS CoE) has started a multi-year project to provide the internal Business Resiliency team with a simulation-based application to supplement current practices regarding resiliency testing and analysis. In the event of an outage, Business Resiliency seeks key insights about impacted locations: What will happen to service level during an outage? How will mitigation strategies impact service level (add headcount, reduce volume, and/or processing time)? The approach leverages simulation-based modeling (via `SAS/OR` software) to estimate the expected impacts to service level due to an outage. The dynamic design of the model enables users to simulate any combination of numerous call centers and/or locations, with the ability to customize mitigation scenarios to compare with the "do-nothing" scenario	sample, proc step, bootstrap, parallel processing, probability distribution, macro variable, out=, confidence interval, proc surveyselect, sample size, exponential distribution, in=, cpu time, data science	source	16
101	Dynamically Assigning Length to Transposed Variables	Ethan Ritchie	When using `PROC TRANSPOSE` to transform "narrow" data (a single subject variable stored in many rows) into "wide" data (one row with subject values stored as distinct variables), the length of the original subject variable is used as the length for each new variable. As a result, variable lengths may be much larger than necessary. This paper will demonstrate a method to automat ically assign the smallest length necessary to each variable once the "narrow" data is transposed into "wide" data. The method is currently used on large sample survey data that is updated daily and accounts for the possibility that the optimal length of a variable may change from day to day	macro variable, proc contents, proc step, character variable, proc print, raw data	source	5
102	Electric Vehicles: Impact and Opportunity for the Power Grid	Tim Fairchild	The electricity industry is waking up to the prospect of large-scale deployments of electric vehicles (EVs). As an IoT-enabled connected device, electric vehicles present both opportunities and risks to the electric power industry. They require a power source in order to recharge, but their battery can also function as a source of supply. EV fleets and buses present different opportunities than individually owned cars. There is a complex and competitive ecosystem of stakeholders, some of which will be in direct competition with incumbent energy suppliers; there is little room for monopoly market thinking, even for vertically integrated utilities. Effective use of IoT data will be key to EV market optimization. How utilities build infrastructure today, both for EV charging and for data analytics, can determine their opportunity to capitalize on this unique growth opportunity	machine learning, artificial intelligence	source	14
103	Embracing the open API Ecosystem to Give Analytics an Organizational Operational Landing Spot	Olivier Thierie, Wouter Travers, Andrew S. Pease	Organizations often struggle to make strategic insights actionable. Powerful analytics, available at key operational decision points, can make the difference. An example of such a case was to determine occupancy of key docks at a major international seaport, using object detection techniques on the camera feeds. In order to leverage such strategic insights throughout the organization, a major hurdle is to deploy analytics in the key operational systems. With Viya, SAS has embraced the open source community with open source analytics integration and the open API ecosystem. This paper will demonstrate how to leverage CAS for image recognition from within Python and how-to set-up a kappa architecture using Kafka to publish results in real-time to organizational systems like SAP and Salesforce. The focus in the paper is on the integration of open source and SAS analytics in these two systems, rather than on the specific use case	rest api, proof of concept, stream processing, real-time processing	source	18
104	Enabling SAS Forecast Server To Perform Best On Automated walmart.com Sales Forecasting	Alexander Chobanyan, Sangita Fatnani	Generating sales forecast for online retailer the size of Walmart.com is a big and complex task. Our online sales are of massive magnitude every day. Our sales pattern for Holiday season between Thanksgiving and Christmas is very different from the rest of a year. In addition, sales patterns significantly shift between different lines of goods distribution (such as shipping directly to customer's home vs. shipping to store for customer's pick-up) as well as between different departments (such as Electronics sales vs. Pharmacy sales). Our daily Statistical Forecast helps the business to with long-term financial planning as well as with evaluating efficiency of particular promotion, which can run just for one day or so. SAS Forecast Server provides a great infrastructure to automatically diagnose each of hundreds of Sales time -series at various levels of sales and time hierarchies and find the best time -series model for each of these time -series	standard deviation, sample	source	9
105	Enabling SAS Software to Communicate RESTfully with Cloud Services	Tom Caswell, Fred Burke	Representational State Transfer (REST) architecture has become the standard for cloud communication; it defines an abstraction among service providers that enables each to evolve independently from the others. Because REST architecture hides the underlying implementation, SAS software does not need to know how any required function (such as authorization or persistence) is provided and it is not affected by changes in the cloud service. For example, authorization can change from LDAP to Kerberos and persistence can change from PostgreSQL to Oracle. SAS software can access functions by invoking the `HTTP` procedure to send cloud service requests and by using the `LIBNAME` statement with the JSON (JavaScript Object Notation) engine to parse cloud service responses. REST also requires stateless communication, which allows work to be automatically load-balanced to meet increasing demands. This paper shows how SAS software can become a RESTful client of cloud services	proc http, score code, macro variable, sas studio, proc hpsplit, libname statement, rest api	source	14
106	Engineering CAS Performance Hardware, Network, and Storage Considerations for CAS Servers	Tony Brown	SAS Viya, using SAS Cloud Analytical Services (CAS), offers a highly performant and scalable computing environment. For the best performance, the CAS Server can benefit from performant hardware architecture solutions to support implementations with high software scalability and large data models. This paper offers best practices for provisioning hardware and network options to create a performant SAS Viya environment: from CPU and Memory to CAS_Disk_Cache configuration and inter-server network bandwidth requirements	sas viya, in-memory analytics, sas 9	source	15
107	Evaluating and Improving the Efficiency of Inference Rules for Segmentation in SAS	Alec Zhixiao Lin	Inference rules are a category of business rules expressed in a series of if-then statements. They are often used jointly with a modeling score for segmentation in many business decisions such as marketing, underwriting, pricing, attribution control, etc. Each inference rule identifies a sweet or sour spot in a population whose performance deviates greatly from the population average but whom a regression- or tree-based predictive model often fails to capture. A mature set of inference rules is a good compromise between maximizing business outcome and minimizing loss in business volume. Using decline rules in mortgage underwriting as an example, this paper introduces a step-wise process in SAS to evaluate and improve the efficiency of inference rules. The final product is a series of neural-network-look-like rules expressed in a white-box manner that are easily communicable and implementable	sample, regression analysis, data cleaning, macro variable	source	9
108	Everything Is Better with Friends: Executing SAS Code in Python Scripts with SASPy	Isaiah Lankham, Matthew T. Slaughter	SASPy is a module developed by SAS Institute for the Python programming language, providing an alternative interface to the SAS System. With SASPy, SAS procedures can be executed in Python scripts using Python syntax, and data can be transferred between SAS datasets and their Python DataFrame equivalent. This allows SAS programmers to take advantage of the flexibility of Python for flow control, and Python programmers can incorporate SAS analytics into their scripts. This paper provides several examples of common data-analysis tasks using both regular SAS code and SASPy within a Python script, highlighting important tradeoffs for each and emphasizing the value of being a polyglot programmer fluent in multiple languages. Instructions are also included for replicating these examples with the JupyterLab interface for SAS University Edition, which includes everything needed to try out SASPy	dataset, proc step, macro facility, macro definition, sas studio, sas programming, sas base, ods html, proc iml, proc sql	source	20
109	Exclusive Data Set Access in a Stored Process Web Application: Macros for mutexes in Windows OS	Richard A. DeVenezia	Data sets used in stored process web applications can experience access contention errors during high frequency usage. What can you do when attempts to use SAS features such as the `LIBNAME` statement option FILELOCKWAIT=wait-time and `SAS/SHARE` libnames fail? This paper will demonstrate how these failures can occur in Windows OS servers and how to prevent them using system mutexes. A discussion of creating a custom dynamic link library (DLL) to interact with the mutexes, macros to interact with the DLL and systematic macro usage is made. These techniques are both academically interesting and useful in actual code	proc step, macro variable, proc json, dataset, proc freq	source	14
110	Execution of User-Written DS2 Programs inside Apache Spark Using SAS In-Database Code Accelerator	David Ghazaleh	SAS In-Database Technologies offers a flexible, efficient way to leverage increasing amounts of data by injecting the processing power of SAS wherever the data lives. SAS In-Database Technologies can tap into the massively parallel processing (MPP) architecture of Apache Hadoop and Apache Spark for scalable performance. SAS In-Database Code Accelerator for Hadoop allows the parallel execution of user-written DS2 programs using Apache Spark. This paper explains how the SAS In-Database Code Accelerator exploits Scala and the parallel processing power of Apache Spark and prepares you to get started with SAS In- Database Technologies	by group processing, out=, spark, parallel processing, proc sql, hive, sas base, select, proc step, options statement	source	18
111	Exploring Computer Vision in Deep Learning: Object Detection and Semantic Segmentation	Xindian Long, Maggie Du, Xiangqian Hu	This paper describes the new object detection and semantic segmentation features in SAS Deep Learning, which are targeted to solve a wider variety of problems that are related to computer vision. The paper focuses on algorithms that are supported on SAS Viya, specifically Faster R-CNN and YOLO (you only look once) for object detection, and U-Net for semantic segmentation. This paper shows how to use the functionality of the Deep Learning action set in SAS Visual Data Mining and Machine Learning in addition to DLPy, an open-source, high-level Python package for deep learning. The paper demonstrates applications of object detection and semantic segmentation on different scenarios, and it shows how to prepare data, build networks, select parameters, load or train the weights, and display results. Future development and potential applications in different areas are discussed	deep learning, neural network, machine learning, cas action, sample, regression analysis	source	20
112	Exploring SAS Viya Operations Infrastructure	Bryan Ellington		sas viya, data mart	source	9
113	Exploring the Intentions of Entering Entrepreneurship for SAS Global Forum 2019	Surabhi Arya, Prashant Gour, Dhruv Sharma, Jeroen Vanheeringen	Entrepreneurship is fast emerging as a transformational megatrend of the 21st century given its capacity to reshape economies and industries throughout the world. This study shows that initially societal perception about an entrepreneur was influential in starting a business in the U.S. while in recent years other factors replaced this such as experience in owning a business and individual perception regarding entrepreneurship. This study explores the determinants explaining why people likely start a business in the U.S. across the time period 2005 - 2014	feature selection, gradient boosting, missing value, data cleaning, data mining	source	7
114	Extending the Reach of Kerberos Authentication from SAS Viya 3.4 to Apache Hadoop	Stuart J. Rogers	Strong authentication using techniques such as Kerberos is becoming an IT security requirement for many organizations. SAS Viya 3.4 supports the option to delegate Kerberos credentials throughout the environment and onto your Apache Hadoop distribution. Doing so enables you to provide strong authentication both into and out of your SAS Viya 3.4 environment. This paper describes the steps that a SAS administrator and IT security specialist need to complete in order to enable strong authentication both into and out of a SAS Viya 3.4 environment	sas viya, cas, bootstrap	source	20
115	Extracting Zipped Files and Downloading Data from External FTP Sites Using SAS	Shimels Afework	Presentation		source	12
116	Factorization Machines, Visual Analytics, and Personalized Marketing	Suneel Grover	Competition in customer experience management has never been as challenging as it is now. Customers spend more money in aggregate, but less per brand. The average size of a single purchase has decreased, partly because competitive offers are just one click away. Predicting offer relevance to potential (and existing) customers plays a key role in segmentation strategies, increasing macro- and micro-conversion rates, and the average order size. This session (and the associated white paper) covers the following topics: factorization machines and how they support personalized marketing; how SAS Visual Data Mining and Machine Learning, SAS Decision Manager, and SAS Customer Intelligence 360 support building and deploying factorization machines with digital experiences; and a step- by-step demonstration and business use case for the sas.com bookstore	machine learning, recommendation engine, data validation rules, if-then-else, customer experience, proc princomp	source	26
117	Factors Affecting Mental Health in Employees and Their Relation to Suicide Rates at a Workplace	Shambhavi Pandey	The number of suicide attempts in the world are increasing and one of the main reasons for this is mental disorders, such as depression and mood disorders. The objective of this project is to predict the likelihood of an employee at workplace to commit suicide based on their behavior. Many factors are considered to analyze the reasons behind why a person would commit suicide and how it can be prevented. The present research mainly focuses on the working population because suicide rates are the highest amongst that population. The logistic regression model created in this study predicts the suicidal tendency among employees with 96% accuracy. This model has its implications in decreasing the suicide rate amongst the young generation by helping them to prevent self-harm	logistic regression, neural network, logistic distribution, regression analysis, proc hpsplit, correlation, data cleaning, response variable	source	11
118	Factors Influencing Academic Performance	Onkar Mayekar, Miriam McGaugh	There are some patterns and aspects that make some students perform better than their peers in terms of academics. At Oklahoma State University, the administration wanted to identify patterns and factors leading to high academic performance. For this, data consisting of almost 22,000 records and 35 variables were obtained from Institutional Research and Information Management at Oklahoma State University. The dataset consists of student demographics, admission data, email interactions before admission including the interaction messages with the admissions office, athletes' data, gymnasium/recreation center check-in data, student employment data and participation data in various departmental events. This paper attempts to identify factors and predict the performance of the students based on the attributes of the students including demographics, participation in extracurricular activities and exercise routine of the student	regression analysis, proc hpsplit, logistic regression, missing value, neural network, data mining, logistic distribution, roc curve, sas enterprise guide, type i error, outlier, response variable	source	9
119	FETCH()ing Use Cases for the Data Access Functions	Chad Mukherjee	The SAS data access functions [OPEN(), FETCHOBS(), GETVARN(), GETVARC(), ATTRN(), ATTRNC(), VARNAME(), and CLOSE()] are powerful and under-used elements of the SAS programming language, with broad applications in both macro and `DATA STEP` contexts. This paper demonstrates the mechanics and several common use cases of these functions in order to accomplish programming feats that would otherwise be a bit awkward or inefficient to attempt in SAS. In order to capture the most value from this paper, you should have a solid understanding of the `DATA STEP` program data vector (PDV) and typical `DATA STEP` behavior, as well as an established comfort level with the SAS macro language and the %SYSFUNC() macro function	hash table, set statement, macro facility, lag function, macro definition, proc step, proc sql, macro variable	source	11
120	Fit Statistic Smoothing: Minimizing Bias in Time Series Forecasting	Zachary D. Blizard	Firms depend on SAS software to build, execute, and maintain forecasting models. Practitioners, whether using `SAS/ETS` or SAS Forecast Studio, can fit various models to a series and deploy the most accurate one according to a specified selection criterion. Practitioners will often combine the forecasts from different models (i.e. ensemble modeling). The ensembling process involves combining forecasts whose associated models are ranked using a single error metric. This practice is not the best option available because no single error metric is globally optimal. Instead, forecasters should combine the forecasts from champion models selected using numerous error metrics. This results in a more stable and robust forecast because the outcome is an average of many forecasts, each from the champion of a unique selection criteria. This paper describes this process and demonstrates its benefits	champion model, time series analysis, likelihood function, proc step	source	12
121	Follow My Lead: Designing Accessible Reports by Example Using SAS Visual Analytics	Jesse Sookne	Your Legal, IT, or Communications department said that your reports must be accessible to people with disabilities. They might have used terms like "Section 508" or "WCAG". Now what? This paper leads you down the path to creating accessible reports by using SAS Visual Analytics. It includes examples of what to do-and what not to do-to make your reports accessible. It provides information about which types of objects to use and how to use them in order to maximize the accessibility of your reports. You can use the information in this paper to create accessible reports, comply with your organization's accessibility requirements, and enable people with disabilities to benefit from the information that you publish	bar chart, time series analysis, line chart, series plot	source	18
122	Forecasting CO2 Emission of Electrical Generation by Using SAS Software	Kaito Kobayashi	Presentation		source	24
123	Framework for German Dossier Submissions	Kriss Harris	When submitting your drug benefit assessments to the German Authority or other (foreign) regulatory agencies, you need to provide your reports in a specific format. These reports are usually in a non - English language, so the characters are different from the English characters that you are used to. The characters contain accents and other special characters, and the numerical results contain commas where you expect decimal points to be, and vice versa. To provide this report, a medical writer typically uses Microsoft Word to copy the results from a Clinical Study Report (CSR) into the document, and includes the appropriate formatting and translations. The copying must be done very carefully, and this process is error prone and very exhaustive. Also, the German authorities might have additional follow -up questions. Therefore, following this process is inefficient. There is a way to make the process more automated, and this paper demonstrated those methods	overall survival, median survival, proc step, proc report, proc sql, adverse events	source	9
124	From Words to Actions: Using Text Analytics to Drive Business Decisions	Reid Baughman	Companies from a variety of industries collect free-form text data that can be used to identify new patterns and relationships. Because unstructured text data does not fit the standard row and column format, it can be more difficult to analyze and utilize. This growing array of data has the potential of yielding new insights for companies seeking to better understand customers and gain an edge relative to competitors. By transforming free-form text data into a structure that can be analyzed and visualized, analysts can use supervised and unsupervised data mining techniques to shed new light on old business problems or develop fresh insights. Call center transcripts, medical records, practitioner's notes, survey responses, or any free-form response fields can all hold valuable insights for any organization. This paper will outline an example of how to manage and transform free-form text data, apply advanced analytical methods to extract useful patterns, and develop actionable insights	proc hpsplit, proc hpforest, feature importance	source	7
125	Full Stream Ahead: Design and Engineering Patterns for SAS Event Stream Processing in the Cloud	Alessio Tomasino, Louis Katsouris	In recent years, there has been a fundamental shift in the requirements of IT organizations to seamlessly manage and deploy their software estates in the public and private clouds. SAS Event Stream Processing is at this forefront of this revolution, providing the benefits of a cloud-ready solution coupled with powerful analytics at scale when used alongside AI, machine learning, IoT, and real-time data streaming. This paper discusses advanced design, deployment, and engineering patterns for SAS Event Stream Processing in the cloud and its integration with key third-party technologies such as Apache Kafka	sas viya, stream processing, real-time processing	source	17
126	Generating Sales Insights from Predictive Models	Krzysztof Dzieciolowski	In a competitive business environment, the ability to predict customers' behaviors is imperative to the success of every company. The probabilities of customers' behaviors are often estimated by predictive models. By rank-ordering the model predictions (scores), we can identify who in the customer population should be targeted to maximize the sales rate. However, the predictive models do not explain why the targeted customers are more likely to buy. As such, models may be ineffective in generating expected business outcomes and could be excluded from the deployment by decision makers. In this paper, we propose a new inputs-ranking method to disentangle customers' motives to buy. The multi-step approach is based on a correlation analysis between customer attributes and model-ranked predictions. The highest-correlated model inputs can be then selected to reveal individual customers' reasons to buy	correlation, gradient boosting, predictor variable, response variable	source	10
127	Generating Synthetic Instruments for Stress Testing: Machine Learning and Econometrics in SAS Viya	Rocco Cannizzaro, Christian Macaro	In stress testing, an analysis of financial risk exposure needs to be carried out with respect to two key components: the current portfolio positions and the future portfolio positions. The risk analysis of current portfolio positions, properly projected in the future, is a well-known topic of discussion. The risk analysis of future portfolio positions, (that is, financial positions that might originate in the future) is less studied and it poses analytical challenges. In this paper, a random forest regression is used to predict the overall future portfolio volume. Then, conditionally on the future volume, a Copula model is used to generate synthetic positions. Finally, a K-Nearest Neighbor classification method is used to fill-in non-numerical attributes of the newly generated positions	proc hpforest, risk exposure, machine learning, stress testing, pd, random sampling, proc step	source	8
128	Getting Started with the SGPLOT Procedure	Joshua M. Horstman	Do you want to create highly-customizable, publication-ready graphics in just minutes using SAS? This workshop introduces the `SGPLOT` procedure, which is part of `ODS` Statistical Graphics, included in Base SAS. Starting with the basic building blocks, you can construct basic plots and charts in no time. We work through several plot types, and you learn some simple ways to customize each one	proc sgplot, bar chart, vertical bar chart, scatter plot, nominal variable, line chart, box plot, response variable, histogram, proc step, bubble plot, heatmap, series plot, sas base, density plot, group by, regression analysis	source	19
129	Getting Started with the SGPLOT Procedure	Cleiton Barbosa Silva, Douglas Avedikian, Parajara Moraes Alves Júnior	The fraud risk management methodology is used by private sector organizations very successfully to curb deviations and minimize vulnerabilities. In the publics organizations the main reference is the Fraud Risk Management Guide published in 2013 by the C ommittee of Sponsoring Organizations of the Treadway Commission (COSO). In Brazil, the Risk Management methodology in publics organizations recommended by the TCU - Brazilian Court of Audit. The TCE -MS adopted the methodology of Fraud Risk Management in 2 013 and implemented it in association with an Information Technology platform and It started at the end of 2016 the project called E -EXTRATOR that combines the extraction of data of the data base from all cities hall with the Artificial Intelligence (Analytics by SAS ), BI tool and the Risk Management of Frauds with the objective of achieving effectiveness in its actions fulfilling in this way its constitutional role. Keywords: Risk, Fraud, Effectiveness, Extraction	artificial intelligence, fraud management	source	8
130	Gimme Five: Building Better SAS Environments with the Five Pillars	Ryan Martis	A large-scale SAS Grid platform can be a complex environment to administer, and going from an out-of-the-box installation to running the service as part of business as usual (BAU) can appear daunting. Using the five pillars approach set out in this paper, your enterprise can start leveraging the power of a SAS Grid installation from day one to integrate 1) Workload Management; 2) Alerting and Monitoring; 3) Logging and Audit; 4) Resilience and Availability; and 5) Continuity and Recovery, into your BAU methodology. A toolset consisting of shell scripts, SAS programs, SAS command-line interfaces (CLIs), and third- party alerting tools, combined with automation-these guiding principles, along with proven examples, can smooth the path to enabling analytics across your organization	sas grid, sas base	source	11
131	Google Store Revenue Probability Prediction	Rohit Kaul, Meera Govindan	We design and examine an analytics solution to make probability predictions of earning revenue per customer visit. The motivation for this study is to help marketing teams make better use of their budgets. Specifically, we wish to aid a firm to use their data as a guiding- tool for decision-making. Often the 80/20 rule has been proven right for many firms- A significant portion of their revenue comes from a relatively small percentage of customers. Therefore, marketing teams are challenged to make appropriate investments in promotional strategies. We use customer data from Google's Merchandise Store (G-Store) available on Kaggle along with SAS University Edition to derive essential insights and generate models to predict the probability of earning revenue per visit	missing value, correlation, proc freq, anova, likelihood function	source	10
132	Got Results? Let Cloud Analytic Services Language (CASL) Help You Turn Them into Superior Reports	Jerry C. Pendergrass	CASL is a new programming language that simplifies running actions in SAS Cloud Analytic Services and processing the results of those actions. A core strength of CASL is the ability to receive the results of an action (tables, dictionaries, arrays, and scalar values) and transform those results into a final report. You can combine CASL's expressiveness for transforming results with the capabilities of SAS Output Delivery Service (`ODS`) to produce refined reports. We present CASL syntax to extract rows and columns using a WHERE clause and column subsetting operators. Find out how to pipeline actions using CASL to prepare arguments for an action using the results of a previous action. Come and see CASL in action producing polished report	missing value, cas, dataset, proc step	source	19
133	Great Time to Learn GTL: A Step-by-Step Approach to Creating the Impossible	Richann J. Watson	Output Delivery System (`ODS`) graphics, produced by SAS procedures, are the backbone of the Graph Template Language (GTL). Procedures such as the Statistical Graphics (SG) procedures dynamically generate GTL templates based on the plot requests made through the procedure syntax. For this paper, these templates will be referenced as procedure-driven templates. GTL generates graphs using a template definition that provides extensive control over output formats and appearance. Would you like to learn how to build your own template and make customized graphs and how to create that one highly desired, unique graph that at first glance seems impossible? Then it's a Great Time to Learn GTL! This paper guides you through the GTL fundamentals while walking you through creating a graph that at first glance appears too complex but is truly simple once you understand how to build your own template	proc template, series plot, proc step, sample, scatter plot, missing value	source	26
134	Hands-On SAS Macro Programming Essentials for New Users	Kirk Paul Lafler	The SAS Macro Language is a powerful tool for extending the capabilities of the SAS System. This hands-on workshop teaches essential macro coding concepts, techniques, tips and tricks to help beginning users learn the basics of how the Macro language works. Using a collection of proven Macro Language coding techniques, attendees learn how to write and process macro statements and parameters; replace text strings with macro (symbolic) variables; generate SAS code using macro techniques; manipulate macro variable values with macro functions; create and use global and local macro variables; construct simple arithmetic and logical expressions; interface the macro language with the `SQL` procedure; store and reuse macros; troubleshoot and debug macros; and develop efficient and portable macro language code	macro variable, macro facility, proc step, autocall macro, macro definition, macro parameter, %macro parameters, macro function, proc sql, dictionary tables, options statement, macro loop, &variable	source	10
135	HASH Beyond Lookups - Take Another Look	Elizabeth Axelrod	Using the hash object in SAS - beyond table lookup and data retrieval - enables you to do some pretty cool stuff, solving complicated problems with code that runs efficiently and flows intuitively. And… it's easy to maintain and modify. What's not to like? This is an updated and expanded version of my original paper "Hash Beyond Lookups - Your Code Will Never Be the Same". It provides a further exploration of some useful Hash techniques, and offers more situations that can be elegantly solved using Hash. The main message? Don't be afraid of Hash!	hash table, hash iterator, set statement, lookup, proc step	source	13
136	Hearing Every Voice: SAS Text Analytics for Federal Regulations Public Commentary	Emily McRae, Tom Sabo, Manuel Figallo-Monge	Regulations.gov was launched in 2003 to provide the public with access to federal regulatory content and the ability to submit comments on federal regulations. Public participation in federal rulemaking is encouraged as it supports the legitimacy of regulatory decisions, frames public acceptance or resistance to rules under development, and shapes how the public interest will be served. Manually reading thousands of comments is time - consuming and labor-intensive. It is also difficult for multiple reviewers to accurately and consistently assess content, themes, stakeholder identity, and sentiment	score code, proc freq	source	12
137	Hidden Treasures in SAS Scheduler Land	Randolph S. Williams	As a user of the Schedule Manager plug-in to SAS Management Console, you might not realize all the features available for managing flows. This paper shows you how to unlock some hidden features and learn about new features designed to help with huge flows. This paper also provides an overview of the various supported schedulers and their niche. Come learn how to get more out of SAS scheduling	dataset, sas management console	source	8
138	Honey, Where Are My Grants? Tips for Creating a SAS Viya Content-Security Model Based on SAS9	Mark Dragone	Generally speaking, a well-designed content security model for a SAS software deployment is an intentional balance between authorizations and those who require them. Maintaining this balance is an ongoing task for SAS administrators. The process for implementing a security model as part of a SAS Viya 3.4 deployment needs to be intentional as well. Fortunately, using an existing SAS9 environment that has evolved in accordance with the corporate security model can prove very valuable. This value is not limited to the initial implementation of the SAS Viya 3.4 security model. The SAS9 environment's model can also be referenced when the two environments run in parallel. This paper introduces techniques and processes to identify SAS9 metadata objects that include directly (and intentionally) applied authorizations, and it explains how to retain those authorizations in SAS Viya 3.4	sas viya, access control, proc sql, create table, sas management console, proc step, proc print, sas administrator	source	20
139	How Access to STEM can Save the World	Jennifer Sabourin, Lucy Kosturko	For every challenge we face on the planet, there is data that can help us find the solution. The next generation of problem solvers, "the digital natives", are poised to see issues such as poverty, hunger, gender equality, and climate change not as insurmountable but as solvable puzzles. With unprecedented access to technology and data, and an unquenchable thirst for digital connection, this generation holds incredible promise in solving social challenges that affect every population, every race, every gender. An interdisciplinary approach to education that incorporates science, technology, engineering, and math (STEM) and project-based learning is the best way to prepare these students for the world that awaits their contributions	data science, problem solving	source	8
140	How many Genies can you fit in one bottle? Multi Tenancy and all the wishes you want.pdf	Ross Sansom	With data becoming bigger and bigger year after year, it is important to have the right tool set and platforms to meet the need of the organization. The idea of one platform to meet all of your needs is something we all think would be the perfect solution. Somewhere to bring all your business needs and thereby make the managem ent of the platform a lot easier . Royal Bank of Scotland (RBS) is now three years down the line, and the dream has become the reality. This paper looks at how RBS implemented a multi -tenancy platform, which is internally known as Genie, and what was discovered along the three -year journey. This paper covers: building tenancies and how using one format to fit all can work; user management; data segregation, or how many SAS LASR Analytic Servers do you really need?; security and usin g the same model to meet all requirements. One SAS 9.4 platform, 10,000 users, and 14 tenancies; did the Genie help build our dream …	access control, sas enterprise guide	source	9
141	How SAS Viya Ensures that Your Data Is Safeguarded by Default with Secure Encryption Techniques	Alec S. Fernandez, Destiny Harrell	What is "data in motion"? What techniques does SAS Viya use to protect your data in motion from unwanted inspection or tampering? Why should you care? This paper introduces the concepts behind securely moving data across networks without fear of a data breach or data falsification. First, there's an overview of Transport Layer Security (TLS), x.509 c ertificates, cipher algorithms, and SAS Secret Manager. I explain how SAS Viya uses TLS to establish secure communications between SAS servers and clients. I describe how SAS generates, distributes, and renews certificates, and how SAS delivers and manages the list of trusted Certificate Authorities. I also explain how you can replac e certificates provided by SAS Viya with your own certificates for any servers that users access directly. From a security perspective, I show how to disable and enable network security	sas viya, traffic data, bootstrap	source	26
142	How to HOW: Hands-on-Workshops Made Easy	Chuck Kincaid	Have you ever attended a Hands-on-Workshop and found it useful? Many people do! Being able to actually try out the things that you're learning is a wonderful way to learn. It's also a great way to teach. You can actually see the people apply what they're learning. Have you ever thought that it would be fun to teach other people in a hands on format? Maybe you weren't sure what it takes or how to approach the course. This presentation will help you with those questions and struggles. What to teach? How much to teach? How should I teach it? How is a Hands-on- Workshop different than lecture style? How much to put into PowerPoints? What if they ask me something I don't know? What if they have a computer problem? All those questions that you have will be answered in this presentation	proc step, proc sgplot	source	8
143	How to Land Work as a SAS Professional	Charu Shankar	"By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions" (McKinsey Global Institute report, May 2011). This situation represents a 50-60% gap between the supply and demand of people with deep analytical talent. Thus, it must come as no surprise that the question the author is asked most in the SAS classroom is how to find SAS work. The author is inundated daily with resumes, requests to connect on LinkedIn, and offers of coffee, tea, or dinner. SAS is a highly valued skill, and everyone wants to know what steps to take to land work. Time magazine rated SAS as the Number 1 career skill in the data world. SAS is also the #1 skill for landing a bigger paycheck according to this study	sas programming, sample	source	16
144	How to Use Deep Learning with Your Internet of Things (IoT) Digital Twin	Bradley W. Klenz	With the Internet of Things (IoT), a digital twin is created to have a virtual representation of a remote device or system. The digital twin shows you the device's operating condition, no matter where it is physically located. IoT devices have a number of sensors installed on them, as well as sensors for the environment around them. Analytics can bring this sensor data together to create a true real-time digital twin. A previous paper showed how streaming analytics are used for device state estimation and anomaly detection. This paper explains how deep learning can be added to your digital twin for more understanding. Image and video analytics are used to capture operating conditions that are missed by regular sensors. Recurrent neural networks (RNN) add temporal data analysis and pattern detection in real-time data streams that are prevalent in digital twins. With these deep learning capabilities, your digital twin provides a new level of insight for your remote devices	deep learning, neural network, time series data, machine learning, missing value, response variable, model training, time series analysis	source	9
145	How to Use JavaScript Libraries Easily with SAS Stored Processes	Philip A. Mason	Looking at websites, you can see a wide variety of ways to display data and interact with users. Much of what you see in a web browser is done by using a combination of HTML, CSS, and JavaScript. There are libraries of CSS and JavaScript code that provide a massive amount of functionality that we can use in our applications. Many of these libraries are free or very inexpensive. I describe some of the best libraries that I know of and how to use them with SAS. SAS Stored Processes enable SAS code to be run from a web browser using the SAS Stored Process Web Application. They enable us to write SAS code that works with web technologies so that we can have SAS in the back end and HTML, CSS, or JavaScript in the front end. In this presentation, I show you how to get SAS to use these libraries to make impressive applications with SAS Stored Processes in the web browser	proc step, proc export, excel, macro variable	source	18
146	How to Win Friends and Influence Platforms: Solving Non-Technical Challenges of SAS Estates.	Johnathan Kain	Managing SAS platforms in big organisations, you face some unique non-technical challenges, especially when you are serving many different departments. Few other tools offer the connectivity SAS does, and few are so widely used by people across the business. With new data storage types, new server environments like the cloud, and new versions of SAS arriving seemingly every month, leaders and SAS administrators find themselves at the heart of a tangled knot where each department is pulling their own thread to get data and insight relevant to them with ever bigger and more varied asks. At Royal Bank of Scotland (RBS), our SAS 9.4M5 platform with SAS Visual Analytics, which we call Genie, has 16,000 users, 1500 of which manufacture data for SAS Visual Analytics reports, run complex models and provide in-the-moment analysis	sas administrator, big data	source	6
147	Human Trafficking Research: The Persistence and Movement of Phone Numbers in Escort Ads	Miriam McGaugh, James Van Scotter, Lauren Ann Agrigento, Denise J. McManus, Tom Kari	The Internet is a great tool for human traffickers. Online classified ad sites have created a "digital red-light district" that allows traffickers to reach more customers while making it easier to avoid being noticed by law enforcement. The contact phone numbers displayed in online sex ads may be the most important single clue for finding the traffickers. However, traffickers can reduce their risks by disposing of phones or changing phone numbers. In addition, previous research suggests that some of the missing phone numbers may have simply moved to other areas. In this paper, we used a sample of nearly 3 million classified ads posted on backpage.com to examine phone numbers from 570 locations in 44 states and the District of Columbia. Our goal was to use the phone number data to understand the traffickers' behavior and to identify trafficking organizations for future investigation	sample, proc freq	source	10
148	Identifying Partisanship in Media Articles	Alexander Lobman, Lohit Bhandari, Ravi Josyula, Nhan Nguyen	In the United States, political polarization has become a significant issue. News media bias and the manner in which social networks deliver news to individual users have both been implicated as playing an essential role in increasing this polarization. Ma ny have suggested that identification and disclosure of media bias would help address this divisive issue. In this paper, a method is demonstrated for data scientists to detect media bias without the bias of the data scientist influencing the results. The paper examines only partisan bias and focuses only on the two major political parties in the United States. The methods used were to build a predictive model on congressional house speeches, labeling each speech based on the speaker's party and then using the model to score news media articles	data mining, regression analysis, data cleaning, data science	source	7
149	Identifying Seasonality and Trend with a Cycle Plot in SAS	Lingxiao Li	Can you easily tell the day of the week on which the most sales for a store item occurred, and how its sales varied over time for each day? The cycle plot (also known as month plot or seasonal subseries plot) is a popular tool for answering these types of questions. It is an effective graph for analyzing seasonal patterns and long-term trends. Initially developed by Cleveland, et al. in the 1970s, it has gained great interest in the data visualization community lately. Although the Graph Template Language (GTL) does not provide a statement for this plot, you can easily produce one in SAS with a combination of the `DATA STEP`, the SQL and SUMMARY procedures, and `ODS` Graphics. This paper uses examples to show you ways to generate cycle plots. The same techniques can also be used to produce similar visuals for other types of data	macro variable, scatter plot, proc step, time series analysis, proc sql, macro function, macro definition, series plot, proc means	source	10
150	Identifying the Most Probable Principal Psychiatric Diagnosis Using Administrative Data	Qingxian Chen, Emily Leckman-Westin	Leveraging existing administrative data to better understand the course and consequences of psychiatric conditions is a potentially potent tool for medical researchers as well as those entities that manage and oversee health care for populations (states, plans, etc..). In psychiatry, some conditions are expected to remit, such that with appropriate treatment and care, the symptoms would be expected to be mitigated; while for other conditions, there is an expectation of a longer, more chronic course. In addition, people may express symptomatology from different conditions at different points in time, indicating that the primary presenting concern may fluctuate over time. Therefore, people with psychiatric conditions can present with a myriad of symptomatology and categorizing the underlying condition(s) can be difficult	create table, sample, best estimate liability	source	16
151	IFRS9: Using SAS/ETD Software and SAS Studio to Select Macro Economic Variables	Julio Alberto Campa Vega Filho	Published in 2014 by the International Accounting Standards Board (IASB), applying IFRS 9 (International Financial Reporting Standard) to financial instruments will be man datory beginning January 1, 2021 , in accordance with the Central Bank of Brazil. As the impairment model for IFRS 9 is forward -looking, banks in Brazil are required to consider future economic scenarios to calculate ECL, which brings new challenges to modeling teams. In particular, we ha ve identified a set of conditions that make monetary and fiscal policy mutually influence each other in a perverse way. One of these conditions is what we call fiscal dominance: an increase in the interest rate makes public debt dynamics unsustainable, whi ch leads to a rise in the inflation rate rather than a reduction	unit root, interest rate, null hypothesis, time series analysis, regression analysis, residual, akaike information criterion, likelihood function, degrees of freedom, moving average, sample, proc autoreg, first difference, covariance, proc step, maximum likelihood estimation, proc varmax	source	19
152	Implementing the Customer Lifecycle in Customer-Relationship Management Using SAS	Peterson Colares	Presentation		source	6
153	Important Performance Considerations When Moving SAS to a Public Cloud	Margaret A. Crevar	When choosing a hardware infrastructure to run your SAS applications, you need a solid understanding of all the layers and components of the SAS infrastructure. You also need the ability not only to successfully run the software but to optimize its performance. Finally, you need an administrator to configure and manage the infrastructure. This paper talks about important performance considerations for SAS9 (both SAS Foundation and SAS Grid Manager) and for SAS Viya when hosted in any of the available public clouds-Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform, to name a few. It also provides guidance on how to configure the cloud infrastructure to get the best performance with SAS. Disclaimer: We strongly encourage you to take the advice in this paper and work with your local public cloud teams to make sure the instances you decide to use are available in the closest region and that you understand their costs			9
154	Improve Your AML Monitoring While Reducing Costs with SAS Viya	Calvin Crase, Chris St. Jeor	Businesses required to comply with Bank Secrecy Act and Anti-Money Laundering (BSA- AML) regulations are very familiar with the burden of false positive alert generation. Of the thousands of potential money-laundering alerts generated each month, only a small handful are positively identified as requiring additional investigation. According to industry statistics, over 95% of system-generated alerts are false positives, and nearly 98% never result in a suspicious activity report (SAR). These false alerts cost billions of dollars annually in wasted investigation time. The conventional rules-based detection approach cannot keep pace with today's constantly evolving money laundering topologies. By leveraging the predictive power of SAS Viya Visual Data Mining and Machine Learning (VDMML), financial institutions can reduce risk by reassessing and enhancing their detection strategies with a data-driven, risk-based approach that is recommended by regulators	sas visual data mining and machine learning, gradient boosting, logistic regression, proc hpsplit, logistic distribution, regression analysis, predictor variable, type i error, machine learning, numeric variable, transaction monitoring, champion model, sample, bank transaction	source	11
155	Improving Security Through Metadata Objects: Permission-Driven Macros	Andrew Gannon	Users can wreak havoc on systems in many different ways - from excessive memory and cpu usage to updating tables with bad data missing data. There are several ways to prevent individuals from doing this - namely by limiting users' access to servers and data. There is a better way to manage permissions for users that is not server or environment specific. By creating a framework of metadata groups and roles, macro specific permissions can be applied at the individual and group levels. This paper seeks to explain and demonstrate how to apply macro level permissions to users via roles and groups in Metadata. Process Flow This presentation will describe how system administrators can apply SAS Metadata-based permissions to individual macros utilizing users, roles, and groups in SAS Metadata and apply them with simple SAS code. Role 1 Role 2 Role 3 Role 4 Macro 1 Macro 2 Macro 3 Macro 4 Metadata Groups and Roles	macro variable, macro definition	source presentation	8
156	Improving Survey Data Quality and Use with SAS Data Management Studio and SAS Visual Analytics	Ankita Kalita, Bonnie Chapman	The Institute for Veterans and Military Families (IVMF) offers many nationally run programs, and the survey data we collect across our entrepreneurship programming portfolio captures business outcomes of participants. The cleaning methods in SAS Data Management Studio, which include a number of sequel executes and data jobs with expression, standardization, concatenation, data validation, clustering, and surviving record nodes, are discussed. With the cleaned data, dashboards were built in SAS Visual Analytics to communicate program outcomes. The presentation walks through the rationale behind the evaluation and analysis, and how to conduct each step from cleaning the raw data through to its presentation in SAS Visual Analytics. It also details the way in which IVMF uses graduate student talent, the hallmark of our success at IVMF, higher education's first interdisciplinary academic institute that is focused on advancing the lives of the nation's military veterans and their families	proc sql, odbc connection, sql server	source	7
157	Increase your Productivity with SAS Enterprise Guide	Marje Fecht	Whether you have been programming in SAS for years, are new to SAS, or have dabbled with SAS Enterprise Guide before, this hands-on workshop sheds some light on the depth, breadth, and power of the SAS Enterprise Guide environment. With all the demands on your time, you need powerful tools that are easy to learn and that deliver end-to-end support for your data exploration, reporting, and analytics needs. This workshop explores increasing your productivity by using SAS Enterprise Guide data exploration tools workspace layout enhanced programming environment `DATA STEP` debugger * and many more features. You will also learn how to easily create reports and graphics, and produce the output formats that you need (XLS, PDF, RTF, HTML, and PPT)	sas enterprise guide, bar chart, scatter plot, vertical bar chart, macro variable, proc freq, proc step	source	18
158	Increasing Operational Efficiency by Using SAS in an Automated Process for Credit Scoring Models	Roberta Pimenta Moreira, Leonardo de Lima Aguirre, Alfredo Luiz Venzel de Oliveira	Sicoob is the largest Credit Union System in Brazil. It has more than 4.2 million customers and almost US$300 million in total assets. This case shows how we used SAS Enterprise Miner, SAS Enterprise Guide, SAS Management Console, and SAS Stored Process to completely change the credit scoring process of the institution, exploiting integration with products databases, automation, and reporting. Previous models were based on customer registration data and qualitative information, captured from self-filled questionnaires. The customers probability of default had to be manually updated by employees, which took, on average, 15 minutes for each update. This process involved high operational risk. The automatic reclassification project is based on a more robust methodology and intensive use of data for decision-making	credit risk, operational risk, pd, risk classification, sas enterprise guide, sas management console, data mining	source	7
159	Independent Component Analysis Using the ICA Procedure	Ning Kang	Independent component analysis (ICA) attempts to extract from observed multivariate data independent components (also called factors or latent variables) that are as statistically independent from each other as possible. Y ou can use ICA to reveal the hidden structure of the data in applications in many different fields, such as audio processing, biomedical signal processing, and image processing. This paper briefly covers the underlying principles of ICA and then discusses how to use the `ICA` procedure, available in SAS Visual Statistics 8.3 in SAS Viya, to perform independent component analysis	dimensionality reduction, proc step, proc princomp	source	15
160	Influencer Marketing Analytics Using SAS Viya	Tamas Bosznay	Marketing nowadays is more and more done via influencers. Influencers are content creators on social media who have a lot of followers and who really influence what people think, but also, what people consider buying. As influencer marketing is costly just as the more traditional marketing channels, it can be crucial for a company to select the best influencers to work with. In our paper we aim to analyse on examples, who are the real influencers in Social Media, and what is the extent of their influence. The case study focuses on influencing opinions and sentiment about several brands. We analyse the posts and activities of a set of social media users with high number of followers. This requires first downloading posts and their metadata from some social media channels (we will focus on Instagram). Python libraries and code will be used to complete these steps	sas viya, parsing	source	19
161	Integrating Case Studies in a Health Analytics Curriculum	Besa Smith, Tyler C. Smith	Advanced informatics layered with customized analytics has driven major change in every discipline in the past decade. In the healthcare environment, individual level determinants of health are being leveraged to reveal personalized patient management that considers disease patterns, high-risk attributes, hospital acquired conditions, and performance measures for specialized treatment approaches. In light of these health informatics advancements during the last decade, the growing need for health analytics expertise has created a critical void in higher education. An abundance of new data-based opportunities that have made large public-use data sets accessible for easy download and use in the classroom have allowed for a much more applied classroom experience. More hands-on applications are positioning students for greater impact in the real world upon graduation and entry into the job market	propensity score, sample, proc step, regression analysis, logistic regression, logistic distribution, proc logistic	source	21
162	Integrating SAS and Microsoft Excel: Exploring the Many Options Available to You	Vincent DelGobbo	This paper explains different techniques available to you when importing and exporting SAS and Microsoft `EXCEL` data. You learn how to import `EXCEL` data into SAS using the `IMPORT` procedure, the SAS `DATA STEP`, SAS Enterprise Guide, and other methods. Exporting data and analytical results from SAS to `EXCEL` is performed using the `EXPORT` procedure, the SAS `DATA STEP`, SAS Enterprise Guide, the SAS Output Delivery System (`ODS`), and other tools. The material is appropriate for all skill levels, and the techniques work with various versions of SAS software running on the Windows, UNIX (including Linux), and z/OS operating systems. Some techniques require only Base SAS and others require the `SAS/ACCESS` Interface to PC Files	excel, sas enterprise guide, proc step, variable, dataset, proc import, sas base, proc export, datetime, delimited file, xls, libname engine, proc print, label statement, sample, datetime variable, format statement, proc contents, proc sql	source	23
163	Integrating SAS, Apache Hadoop and an Enterprise Data Warehouse in a Single Solution	Bob Matsey	Presentation	proc step, proc sql, proc freq, proc princomp, proc reg, proc varclus, sas log	source	17
164	Integration of Google BigQuery with SAS Analytics Pro in Docker Container on Cloud Environment	Sanket Mitra, Srivalli Avadhanula, Fahad Ali	SAS Analytics for Containers provides the option to deploy SAS Analytics within container-enabled infrastructures, including Docker and Kubernetes, which are often run in the cloud. Aiming to analyze massively large data from Google BigQuery through SAS in containerized environment, we have integrated Google BigQuery with SAS 9.4 Analytics Pro in Docker Container on Google Cloud Environment. This paper guides you through the process of configuring SAS Access to BigQuery in containerized SAS Application and validation steps for the same	google bigquery, data cleaning	source	16
165	Integration of the SAS Platform in a Business Analytic Curriculum	Michael Gibbs, Ronald Freeze	The objective of this breakout session is to show strategies used while implementing the SAS Platform into a Business Analytics curriculum at the University of Arkansas. The University of Arkansas Walton College of Business, located in Fayetteville, Arkansas, is accredited by the Association to Advance Collegiate Schools of Business (AACSB). The AACSB has participated in workshops with the Information Systems Department, outlining best practices for business analytics programs. SAS has been instrumental in helping to design the business analytics programs and has recognized the value of our program by presenting graduate and undergraduate students upon completion with a certificate endorsed by SAS. The initial portion of this session outlines the j ourney of program design and growth. The SAS solutions used in the Analytics program include SAS Enterprise Guide, SAS Enterprise Miner, SAS Viya, and SAS Visual Analytics	data mining, sas enterprise guide	source	4
166	Interaction between SAS and Python for Data Handling and Visualization	Yohei Takanami	For drug development, SAS is the most powerful tool for analyzing data and producing tables, figures, and listings (TLF) that are incorporated into a statistical analysis report as a part of Clinical Study Report (CSR) in clinical trials. On the other hand, in recent years, programming tools such as Python and R have been growing up and are used in the data science industry, especially for academic research. For this reason, improvement in productivity and efficiency gain can be realized with the combination and interaction among these tools. In this paper, basic data handling and visualization techniques in clinical trials with SAS and Python, including pandas and SASPy modules that enable Python users to access SAS datasets and use other SAS functionalities, are introduced	dataset, histogram, clinical trial, missing value, scatter plot, merge statement, proc sgplot, regression analysis, bar chart, proc step, numeric variable, proc means, sas library, data science	source	22
167	Interactive Graphs	Kriss Harris, Richann J. Watson	This paper demonstrates how you can use interactive graphics in SAS 9.4 to assess and report your safety data. The interactive visualizations that you will be shown include the adverse event and laboratory results. In addition, you will be shown how to display "details-on-demand" when you hover over a point. Adding interactivity to your graphs will bring your data to life and help improve lives!	bar chart, change from baseline, series plot, box plot, adverse events, sample, macro variable, scatter plot, macro loop	source	23
168	International Financial Reporting Standard IFRS 17: Practical Experiences from Implementation	Sebiha Sahin	This paper provides an overview of the SAS solution offering for the new International Financial Reporting Standard IFRS 17, which includes a product bundle of SAS Infrastructure for Risk Management, SAS Risk and Finance Workbench, and SAS Visual Analytics. We cover both the main features of the solution and specific project-related details that relate to items that we have already observed in project implementations	ifrs 17, sas risk, insurer, financial reporting, sample, excel	source	9
169	Introducing Pattern Matching for Graph Queries in SAS Viya 3.4	Matthew V. Galati, Steve Harenberg, Robert C. Pratt	SAS Visual Data Mining and Machine Learning 8.3 in SAS Viya 3.4 includes a new patternMatch action, which you can use to execute graph queries that search for copies of a query graph within a larger graph, with the option of respecting node or link attributes (or both). This feature is also available via the PATTERNMATCH statement in the `NETWORK` procedure. This paper presents examples of pattern matching in social network and anti-money laundering applications. It also provides a functional comparison to Neo4j's query language, Cypher, and computational comparisons to both iGraph and Neo4j	proc network, proc step, machine learning	source	26
170	Introducing SAS Workload Orchestrator, the New SAS GRID Manager Workload Manager from SAS	Doug Haigh	Prior to SAS 9.4M6, there were two options for managing your workload on a SAS Grid - Platform Load Sharing Facility (LSF) from IBM and Apache Hadoop YARN. Starting with SAS 9.4M6, SAS will provide its own workload manager, SAS Workload Orchestrator. This paper provides information about the new SAS Workload Orchestrator in the areas of design and configuration along with a comparison to the other workload managers that are supported for SAS Grid Computing	sas grid, rest api, web service, job scheduling, sas viya	source	10
171	Introducing the BGLIMM Procedure for Bayesian Generalized Linear Mixed Models	Amy Shi, Fang K. Chen	`SAS/STAT` 15.1 includes `PROC BGLIMM`, a new, high-performance, sampling-based procedure that provides full Bayesian inference for generalized linear mixed models. `PROC BGLIMM` models data from exponential family distributions that have correlations or nonconstant variability; uses syntax similar to that of the MIXED and GLIMMIX procedures (the CLASS, MODEL, RANDOM, REPEATED, and ESTIMATE statements); deploys optimal sampling algorithms that are parallelized for performance; handles multilevel nested and non-nested random-effects models; and fits models to multivariate or longitudinal data that contain repeated measurements. `PROC BGLIMM` provides convenient access, with improved performance, to Bayesian analysis of complex mixed models that you could previously perform with the `MCMC` procedure. This paper describes how to use the `BGLIMM` procedure for estimation, inference, and prediction	covariance, proc mixed, binomial distribution, proc step, covariance structure, random intercept, poisson distribution, correlation	source	20
172	Introducing the MBC Procedure for Model-Based Clustering	David Kessler	Clustering has long been used to learn the structure of data and to classify individuals into distinct groups. For example, cluster analysis can be used for marketing segmentation that distinguishes potential buyers from nonbuyers. Classical clustering methods use heuristics or simple Euclidean distance to form clusters, but in some cases, the clustering solution is more parsimonious and more useful if it is based on the formal likelihood of a model. The model and its associated likelihood allow the clustering to accommodate more complex covariance structure that is exhibited in the data. The `MBC` procedure, available in SAS Visual Statistics 8.3 in SAS Viya, enables you to fit mixtures of multivariate Gaussian (normal) distributions to your data to learn a cluster structure in an unsupervised manner. In addition to clustering the observations in a data set, you can use the fitted model to classify new observations	proc step, covariance, covariance structure, feature selection	source	21
173	Introduction to Data-driven Programming Using SAS	Kirk Paul Lafler	Data-driven programming, or data oriented programming (DOP), is a specific programming paradigm where the data, and/or data structures, control the flow of a program and not the program logic. Often, data-driven programming approaches are applied in organizations with structured and unstructured data for filtering, aggregating, transforming and calling other programs. This paper and presentation explores several data-driven programming techniques that are available to SAS users	dictionary tables, proc step, proc sql, excel, proc contents, call execute, ods excel, sas library, sas log, output delivery system, macro facility, macro variable, dataset, select, character variable, proc format, proc print, options statement, user-defined format, proc freq	source	23
174	Introduction to Esri Integration in SAS Visual Analytics	Jeffrey Phillips, Scott Hicks, Tony Graham	SAS has partnered with Esri, the world's leading mapping technology company, to provide access to geospatial features throughout SAS Visual Analytics. This paper shows you how to find trends and make decisions by adding location information to your data using geocoding, enriching your data by adding demographics, and analyzing your data using routing and drive-time calculations. We also show you how to incorporate your Esri shapefiles and feature services, and we give a preview of future integration	sample, bar chart	source	25
175	Introduction to Nonlinear Mixed-Effects Models of Longitudinal Data	Shelley A. Blozis	Nonlinear mixed-effects models are models in which one or more coefficients of the model enter in a nonlinear manner, such as appearing in the exponent of the growth function. This talk is intended for users already familiar with linear mixed-effects models who are interested in extending their modeling options to include more complex functions. Unlike linear mixed-effects models for longitudinal data, nonlinear mixed-effects models enable researchers to apply a wide range of nonlinear growth functions to data, including multi- phase functions. This talk reviews the syntax for the `NLMIXED` procedure for fitting a variety of nonlinear mixed-effects models	proc step, repeated measures, longitudinal data	source	6
176	It's All About the Base-Procedures	Jane Eslinger	As a Base SAS programmer, you spend your day manipulating data and creating reports. You know there is a procedure that can give you what you want. As a matter of fact, there is probably more than one procedure to accomplish the task. Which one should you use? How do you remember which procedure is best for which task? This paper is all about the Base procedures. It explores the strengths of the commonly used, nongraphing procedures. It discusses the challenges of using each procedure and compares it to other procedures that accomplish similar tasks. The first section of the paper looks at utility procedures that gather and structure data: APPEND, COMPARE, CONTENTS, DATASETS, FORMAT, SORT, SQL, and TRANSPOSE. The next section discusses the Base SAS procedures that work with statistics: FREQ, MEANS/SUMMARY, and UNIVARIATE. The final section provides information about reporting procedures: PRINT, REPORT, and	proc step, proc sql, proc contents, proc freq, proc report, proc tabulate, proc compare, proc transpose, proc append, proc print, proc format, proc sort, sas base, proc datasets, proc means, proc univariate, create table, dictionary tables, macro variable	source	21
177	Joint Analysis of Failure Times and Time-Varying Covariates	Joseph C. Gardiner	Statistical models for analyses of failure times include the proportional hazards model and the accelerated failure time model. These models can be extended to assess the influence of a longitudinally assessed biomarker (a time-varying covariate) on the survival distribution by modeling the hazard function or the scale parameter of a parametric survival distribution. If the biomarker is updated intermittently at a few time points, a straight-forward approach applies the most recent values preceding the failure times. SAS procedures PHREQ, LIFEREG and SEVERITY can be used for analyses. Joint models for the failure time and biomarker parse their joint distribution into conditionally independent components given random effects. Using the `GLIMMIX` procedure, the biomarker trajectory is constructed as a linear function of random effects and polynomials or splines of time	likelihood function, hazard function, proc mixed, piecewise exponential model, proc step, sample, hazard ratio, logistic distribution, cumulative hazard, macro definition, time-dependent covariate, covariance, proc fcmp, proc phreg, proc glimmix	source	22
178	Kustomizing Your Viya Engine Using SAS Studio Custom Tasks and D3.js	Elliot Inman, Ryan West, Olivia Wright	In the 1950s, people like George Barris and Von Dutch took standard American sports cars and turned them into custom cars with rebuilt bodies and pinstripe paint - giving birth to the "kustom car" industry. SAS Viya provides a highly integrated analytics environment. Data scientists can use SAS Studio to run point-and-click machine learning models and automatically access the scored data in SAS Visual Analytics interactive reporting. That comes standard with the SAS Viya platform. SAS Viya also provides many opportunities to create a custom workflow for analytics projects - to kustomize the SAS Viya engine with additional features and a stunning new paint job. By making your own point -and-click tasks in SAS Studio and using open-source data visualization software like D3.js to develop unique graphs within SAS Visual Analytics, you can supercharge your data science platform	sas studio, cluster analysis, sas viya, data visualization, proc hpsplit, machine learning, data science, feature importance, proc step	source	16
179	Large Scale Reporting Solution using SAS and Excel	Balraj Pitlola, Malleswara Sastry Kanduri, Lokendra Kumar Devangan	Are you looking for large scale reporting from your data? Do you want to empower end-users with the information that data provides and help in the decision making? Do you want to serve all the reporting needs of the customers without the need for a BI or Reporting tool? Do you want the reports to be generated on a single click using a user -friendly UI? Do you have a large audience who are looking for the reporting services? If your answer is affirmative, then this paper is for you. Here we discuss a use-case wherein every quarter the business wants to generate more than 1500 customized customer reports, so we have developed a tool which enables the business to generate the customer specific reports on single-click and share the report(s) with the customer(s) as an email attachment. To achieve this, we use SAS Programming, SAS Add -In for Microsoft Office (M S-Office), VB Script and Think-Cell plugin for MS-Office	excel, data lake, proc step, proc print, sas programming, proc sql	source	8
180	Latin Hypersquare Sampling and Genetic Algorithm in SAS/IML for Hyperparameter Optimization	Andrea Magatti	A Hyperparameters Optimization approach for SAS 9.4 users that do not have access to the "magic" AUTOTUNE option of `SAS/VIYA`, using `SAS/IML`, some `SAS/STAT` and	sample, neural network, proc step, machine learning, stratified sampling, nominal variable	source	16
181	Learning Data Science with SAS University Edition and JupyterLab	Brian R. Gaines	One of the interfaces that SAS University Edition includes is the popular JupyterLab interface. Y ou can use this open-source interface to generate dynamic notebooks that easily incorporate SAS code and results into documents such as course materials and analytical reports. The ability to seamlessly interweave code, results, narrative text, and mathematical formulas all into one document provides students with practical experience in creating reports and effectively communicating results. In addition, the use of an executable document facilitates collaboration and promotes reproducible research and analyses. After a brief overview of SAS University Edition, this paper describes JupyterLab, discusses examples of using it to learn data science with SAS, and provides tips. SAS University Edition, which is available at no charge to educators and learners for academic, noncommercial use, includes SAS Studio, Base SAS, `SAS/STAT`, and `SAS/IML` software and some other analytical capabilities	data science, sample	source	17
182	Lessons Learned Architecting a Modern Data Analytics Platform in the Coud Using MapR and SAS Viya	Shane Gibson	During 2018, I was the Technical Lead on an Analytics 2.0 project for a large New Zealand Government organization that was deploying a MapR converged data platform in Microsoft Azure and a SAS Viya and SAS 9.4 Platform via SAS Global Hosting in Amazon Web Services (AWS). This presentation covers the technical architecture that was defined for the integrated platforms and the lessons learned during the architecture of the platforms	sas viya, data lake	source	7
183	Life Expectancy Tables - Getting SAS to run the hard math	Anna Vincent	Life expectancy (LE) tables are statistical tools typically used to portray the expectation of life at various ages and are a fundamental tool in survival analysis. They also provide information on the number of individuals who survive to various ages, median age at death, age-specific death rates, and the probability of dying at certain ages. LE at birth is the most frequently cited LE table statistic. The Texas Department of State Health Services (DSHS) creates LE for publication in the Vital Statistics Annual Report every year. Previously, these tables were constructed in a multi-step process using detailed Python and Java script. In this paper we provide a general framework for building LE tables using SAS	life table, sas syntax	source	8
184	Machine Learning and Predictive Analytics in SAS Enterprise Miner and SAS/STAT Software	David Richard Cutler	`SAS/STAT` software and SAS Enterprise Miner are two excellent environments for applying machine learning and other analytical procedures to a wide range of problems, from small data sets to the very large and very wide. In SAS Ent erprise Miner, one can move seamlessly from data cleaning and processing, through preliminary analyses and modeling, to comparisons of predictive accuracy of several predictive methods and the scoring of new data sets. Many powerful machine learning and st atistical learning tools including gradient boosting machines, artificial neural networks, and decision trees are nodes in SAS Enterprise Miner, and other SAS procedures that are not nodes can be accessed through the SAS Code Node. In this talk, I work th rough some examples from business and other areas to illustrate some of the many capabilities of `SAS/STAT` and SAS Enterprise Miner	data mining, logistic regression, regression analysis, logistic distribution, proc hpforest, feature selection, proc step, proc hpsplit, insurance claims, roc curve, imputation, sample, machine learning, missing value, proc logistic, histogram, sas programming, response variable, training set, support vector machine, gradient boosting, backward selection, data science, data cleaning	source	21
185	Machine Learning for Effective Surveillance with SAS Adaptive Learning and Intelligent Agent System	Rory MacKenzie	In SAS Visual Investigator, scenario administrators can author surveillance rules to detect threats and generate highly visual alerts, which can then be investigated by analysts. Analysts then use the application to perform investigations before taking an action on the alert. Wouldn't it be great if the system could learn from the investigation that an analyst has performed in order to make the alert generation process more accurate and more up- to-date as trends change? This paper describes how SAS Adaptive Learning and Intelligent Agent System automates the surveillance authoring process and uses machine learning techniques to adapt as new threat patterns emerge. For organizations without training data sets, we also examine how unsupervised and semi-supervised learning can be used with SAS Adaptive Learning and Intelligent Agent System to provide effective surveillance solutions	machine learning, sample, type i error, model validation, stage 1, stage 2, data science	source	14
186	Macro-mize Your Time: Using SAS to Email Hundreds of Individualized Reports in Minutes	Salma Ibrahim	Sending out individualized reports can take days if you have hundreds (or thousands) of recipients. SAS can help you get that time back by generating and sending these emails. In this paper, we use SAS macros to send hundreds of individualized reports in a matter of minutes. This process can be fully automated with a single input table that maps recipients to their respective reports. We cover options that can be applied to the emails, including personalizing the content of the email, sending from a designated email address, and marking the emails as high priority. In addition, we discuss creating summary reports that provide you with useful information, such as invalid email addresses and a snapshot of the emails sent out in a given run	error message, sample, macro definition, output delivery system, call execute, excel	source	12
187	Macros I Use Every Day (And You Can, Too!)	Joe DeShon	SAS macros are a powerful tool that can be used in all stages of SAS program development. Like most programmers, I have collected a set of macros that are so vital to my program development that I find that I use them just about every day. In this paper, I demonstrate the SAS macros that I developed and use myself. I hope they will serve as an inspiration for SAS programmers to develop their own set of must-have macros that they can use every day	missing value, macro definition, macro variable, macro facility, sas base, macro loop, sas function, excel	source	11
188	Make SAS Viya Realize Strategic Data Science - Data Governance, AI Platform and AI SAS Programmer	Ryo Kiguchi, Katsunari Hirano, Yoshitake Kitanishi	Data scientist is always necessary to construct various models and to acquire the latest analysis method for various kinds of data in order to make use of maximizing "Data Science". Fashionable AI is also the same. The Artificial Intelligence (AI) that we define is the system with the series of processes of "Recognition", "Learning" and "Action", which assists people's activities. There are various types of data used in AI, and so that, the models or methods used recognition, learning, and action are different depending on the data format. However, in talking about data science, data governance is very important regardless of the data format. We have made the strategy about data governance using Python and SAS via SAS Viya, and have been maximizing data science based on effective matching such as machine learning and deep learning (CNN, RNN etc.)	data science, sas viya, mock shell, data governance, heatmap, sas programmer, machine learning, clinical trial, neural network, deep learning, clinical research	source	8
189	Making Life Easier on the SAS End: Best Practices for Collecting Survey Data	Julie Plano, Keli Sorrentino	An analysis-ready SAS dataset is the deliverable, the one and only thing the analysts are waiting for. It is the ultimate goal of any research project. Much effort goes into the collecting and cleaning of data before it is ready to be analyzed. Our research team has spent decades learning to design good data collection instruments, resulting in purposeful survey questions with practical response values. Whether the questionnaire is administered in person or collected via online software, it is important to begin with a clear and concise plan. Here we share our expertise and best practices to help research teams design data collection instruments effectively and efficiently from the ground up. Our example will use Qualtrics Research Suite to design and gather data, and SAS 9.4 to create a dataset for further investigation and analysis	dataset, sample, unique key, data validation rules, missing value	source	11
190	Mastering Parameters in SAS Visual Analytics	Stu Sztukowski	SAS Visual Analytics provides simple, straight-forward filtering mechanisms that work right out of the box with almost no configuration needed: drop a control (filter) into a report and assign a variable role to it. It's easy, quick, and satisfies most UI requirements for users. When used on their own, report controls only filter other objects and do not provide any additional information to you, the designer. For example, if a user selects a filter, it might be helpful to know: What filter did they select? What value or values does the filter hold? * Can I use the value they selected in calculated variables? Visual Analytics allows you to answer these questions through special variables called parameters. Parameters are data set independent dynamic variables in Visual Analytics that can be added to report controls to store user-selected value(s) of lists, buttons, sliders, and other controls into a variable or group of variables	bar chart, macro variable	source	18
191	Matchy-Matchy: Role of Strategy, Case to Control Ratio, and Number of Variables in PROC PSMATCH	Brittany Hand	Presentation	propensity score, proc step, confidence interval, propensity score matching	source	17
192	Maxims of Maximally Efficient SAS Programmers	Kurt Bremser	The Maxims are a set of simple rules intended to improve the life of a SAS programmer. They provide best practices, hints, cautions, stuff for thinking, and even some humor	maxims, maximally efficient, sas programmer	source maxims	11
193	Measuring Analytic Performance in Financial Terms for Executives	Kenneth Pikulik	Data Scientists are by their very nature good at understanding the details of information and finding the connections between often unique and unrelated data points. The value of what they do is in high demand and often goes unfulfilled because requests overwhelm their ability to fulfill everyone's desires. New analytic tools that support greater speed and performance by eliminating data movement, simplifying testing, and streamlining the move to production can provide relief for this problem but building the business case for new analytic tools is not as easy as building the business case for a fraud or churn solution . As a result, Data Scientists need to be able to communicate why an investment in new analytics tools is necessary in financial terms that executives will understand and approve. This paper focuses on enabling you to do this. 2	data science, in-database processing	source	5
194	Measuring Model Stability	Daymond Ling	If the stability or variation of a model's performance is important, how would you measure it before deploying the model into production? This paper discusses the use of randomization test or bootstrap sampling as a post model build technique to understand the variations in a model's performance. This situation may arise if you are vetting a model built by someone else for instance. Awareness of the variance in model performance can influence deployment decision and/or manage performance expectation when deployed. During model build, this method may also be of use in choosing amongst candidate models	data cleaning, sample, proc logistic, proc step, bootstrap, key performance indicator, logistic distribution	source	15
195	Medical Image Analytics in SAS Viya for the Treatment of Colorectal Cancer Spread to the Liver	Fijoy Vadakkumpadan, Joost Huiskens	The powerful analytics in SAS Viya have been recently extended to include the ability to process medical images, the largest driver of health-care data growth. This new extension, released in SAS Visual Data Mining and Machine Learning 8.3 in SAS Viya 3.4 enables customers to load, visualize, analyze, and save health-care image data and associated metadata at scale. As SAS continues to build on this foundation, several substantially new medical image processing capabilities are planned for future versions of SAS Visual Data Mining and Machine Learning	sas viya, machine learning, deep learning, neural network, cas action, cas, data wrangling, proc princomp	source	11
196	Millennium BCP Increase business analytics skills with a new educational approach, what; how and value	Tarcísio Pontes	This paper goal is to share how SAS after and Millennium Bank partnered to bring success to their Millennium Banking Academy (MBA) program. The MBA program was developed to address two main topics: Risk Management and Marketing. These topics are challenging the bank to move to a strategic vision using advanced analytics for predictive risk, reporting to central bank and data-driven marketing. The holistic and main questions to answer were: what; how and the value. On what, both companies identified the needs for better training. SAS was a strategic advisor in "inGenious Program" to develop skills and talent in internship and traditional workers. To answer how (process) Millennium BCP and SAS work together to create a personalized solution by creating the Millennium Banking Academy (MBA) program for interns	business intelligence, machine learning	source	5
197	Model Risk Management: How to Be Prepared in a Data-Driven World	David Asermely	In our increasingly analytical world, models are high-value assets that drive important business decisions and critical regulatory activities. Models have allowed organizations to replace subjective opinion with objective and repeatable logic. However, models require attention and care; most professionals would agree that bad models are costly! Yet many companies struggle to answer basic questions about their models: How well are your models performing? What models require attention? Are you properly allocating your resources to address your material models? SAS is the largest and most successful model risk management software vendor in the world. Software like SAS Model Risk Management enables organizations to use a data-driven approach to assess their model risks, identify gaps, review and update their policies and procedures, and reassess their risks. This iterative approach continually improves their model quality and efficiency	model risk management, risk exposure	source	6
198	Modeling Sequential Adverse Events of HIV/AIDS? Transition-Specific Parametric Multi-state Model	Zelalem Dessie, Temesgen Zewotir	Presentation		source	6
199	Modeling with Deep Recurrent Architectures: A Case Study of Using SAS and Python for Deep Learning	Linh H. Le, Ying Xie	Deep learning is attracting more and more researchers and analysts with its numerous breakthrough successes in different areas of analytics and artificial intelligence. Although being well-known for its power in data processing and manipulation, SAS is still relatively new as a deep learning toolbox. On the other hand, Python has become "the language for deep learning" due to its flexibilities and great supports from deep learning researchers. In this paper, we present a case study of combining SAS and Python for the task of predicting next-day stock price direction with deep recurrent architectures, including vanilla Recurrent Neural Network, Long Short-Term Memory, Gated Recurrent Unit, and a novel model we proposed named Recurrent Embedding Kernel. To use the power of each framework, we preprocess data in SAS and build our deep models in Python. The whole process is unified in one framework using the SASPy module, which allows access to SAS codes in Python environment	deep learning, neural network, proc print, data cleaning	source	12
200	Modern, Fast, Automated and Secure Trigger-Based SAS 9.4 Application Configuration	Timo Blomqvist, Tapio Kalmi	Traditional SAS application configuration and administration has many detailed tasks for which you need deep knowledge of the SAS Platform. When these tasks are done manually, they take a lot of time and mistakes are possible. That is the reason why we wanted to modernize the way to do it. SAS Platform administration configuration tools have been used as a quick start to automate configuration tasks. Some of the tools have been automated and integrated with the enterprise-level identity and access management process. SAS Platform user IDs, rights, and authorizations are now administered with as much automation as possible. When user groups have been added into Active Directory for a new application, the application configuration is automatically done in the development environment in the next morning. The automation includes metadata folders, groups, ACTs, libraries and SAS LASR Servers, and filesystem folders	sas administrator, sample, sas base	source	9
201	Modernize Your Business Intelligence Content from SAS 9 to SAS Viya in Three Steps	Gregor Herrmann	In recent releases of SAS Visual Analytics on SAS Viya, many reporting and dashboarding capabilities have been introduced that make the move to the current version of SAS Visual Analytics very attractive. Because the underlying architecture has changed, you must carefully prepare for this move. This paper describes how you can best prepare and execute the modernization and what you should take into consideration to avoid any setbacks. The presentation also includes a live demonstration showcasing how to move content from SAS Visual Analytics 7.4 to SAS Visual Analytics 8	sas viya, sas 9, user-defined format, spk file, sample	source	10
202	Monitoring the Relevance of Predictors for a Model Over Time	Ming-Long Lam	This paper presents a novel approach to monitor model performance over time. Instead of monitoring accuracy of prediction or conformity of predictors' marginal distributions, this approach watches for changes in the joint distribution of the predictors. Mathematically, the model predicted outcome is a function of the predictors' values. Therefore, the predicted outcomes contain intricate information about the joint distribution of the predictors. This paper proposes a simple metric that is coined the Feature Contribution Index. Computing this index requires only the predicted target values and the predictors' observed values. Thus, we can assess the health of a model as soon as the scores are available and raise our readiness for preemptive actions long before the target values are eventually observed. This index is model neutral because it works for any types of models that contain categorical or continuous predictors, and models that generate predicted values or probabilities	covariance, training set, covariance structure, response variable, standard deviation, correlation, regression analysis, interquartile range, poisson regression, proc reg, poisson distribution, key performance indicator, proc glm, confidence interval, probability distribution, nominal variable	source	16
203	Months to Weeks: SAS Credit Scoring Express Implementation	Abhijit S. Joshi	SAS Credit Scoring is a flagship product from SAS that helps you reduce credit losses and boost your overall business performance by making better, data-driven credit decisions on both the origination and servicing sides of your business. One of the key benefits of the solution is the reduction in time to market, which delivers significant Return on Investment (ROI). Recently, the solution has undergone significant enhancements that reduce its implementation time from several months to several weeks-enabling financial institutions to realize their time-to-market ROI even sooner. Based on real-life project experiences, this paper outlines how implementation can be fast-tracked through an improved assessment process and a phased implementation approach. Moreover, the use of an automation toolkit optimizes implementation efforts, which helps further reduce time frames	credit scoring, sample, time variable	source	12
204	Multi-tenancy in SAS Viya : Considerations and Implementation	Eric Davis	This paper introduces SAS Viya 3.4 multi-tenancy and considerations for its deployment. It delves into the steps required to deploy and configure multi-tenancy successfully. It highlights the reasons to use multi-tenancy, changes to make during deployment, how to onboard tenants, and pitfalls to avoid during the configuration	sas viya, cas, sas studio, connection pool, access control, sample	source	23
205	Multiple Imputation for Skewed Multivariate Data: A Marriage of the MI and COPULA Procedures	Zhixin Lun, Ravindra Khattree	Missing data is a common phenomenon in various data analyses. Imputation is a flexible method for handling missing-data problems since it efficiently uses all the available information in the data. Apart from regression imputation approach, the `MI` procedure in SAS also provides the multiple imputation options which create multiple data set s based on Markov chain Monte Carlo (MCMC) and fully conditional specification (FCS) methods. However, these methods may not work very effectively for skewed multivariate data since they require the assumption of multivariate normal distribution. To deal with such data, we introduce an approach based on copula transformation. We combine imputation using `PROC MI` and copula theory using `PROC COPULA` to arrive at an approach to solve the missing data problem for skewed multivariate data. We implement and demonstrate the use of this method through simulated examples under the assumption that data are missing completely at random (MCAR)	imputation, multiple imputation, cumulative frequency, missing value, probability distribution, proc step, normal distribution, regression analysis, proc mi, correlation, sample	source	9
206	Nestl Brazil Case: Demand Planning using SAS Forecast Server and SAS Enterprise Guide	Marcos Frossard Borges	Demand Forecasting is a critical process to drive several business decisions in industries. Marketing & Sales, Finance, Supply Chain and Factories are guided by an agreed sales forecast number. Additional to sales history, there are several factors and events that shape the demand, and robust statistic is a key competitive advantage to predict the future demand. There is a huge amount of data to be processed and hundreds of forecasting models needed. The objective of this paper is to describe how Demand Planning at Nestlé Brazil is supported by statistics using SAS Forecast Server and SAS Enterprise Guide	sas enterprise guide, demand forecasting	source	7
207	New to SAS? Helpful hints for developing your own professional development plan	Kelly D. Smith	SAS runs on multiple platforms and has multiple procedures for pulling in data, multiple methods to create and modify data, multiple procedures for data analysis, multiple methods to output data, multiple ways to present the data, and to top it all off, SAS offers multiple learning options. As someone new to SAS, how do you decide the best route for your own learning experience? Three years ago, I began my own journey as a SAS rookie when I started transitioning from a career as a secondary science instructor to a SAS programmer and institutional researcher in higher education. In my own learning journey, I have utilized both online and face-to-face classes and successfully achieved certification as a SAS programmer. I have a doctorate in Adult and Community College Education, and I provide an overview of my own SAS journey from the perspective of both an adult learner and a professional adult educator	sas base, sas enterprise guide, sas programmer, data science, sas programming, proc step	source	12
208	No Time to Create an Enterprise Data Warehouse? No Problem. Blockchain to the Rescue!	Angela M. Hall	A majority of analytic work cannot start until an enterprise data warehouse (EDW) exists or significant database development work occurs. This time-consuming and usually cost- prohibitive prerequisite gets in an organization's way of generating real analytical value quickly from data. Other issues with EDWs include security (single-point-of-failure), scalability, data transfer and resource constraints, and accessibility. What if blockchain technology could help an organization skip the EDW process altogether? And how can SAS be leveraged to generate immediate value in ensuring valid blockchain creation as well as timely analytic results? In this paper, we explore several potential cross-industry use cases for blockchain and how SAS can assist in creating a block and generate value from the block	stream processing, real-time processing, data warehouse	source	6
209	Nothing is More Powerful than the APACHE Helicopter, Except the APACHE II Macro	Margaret Kline, Jessica Parker	Presentation		source	20
210	Open Visualization with SAS Viya and Python	Joseph Indelicato	As SAS continues to push boundaries with its cloud-based analytics ecosystem, SAS Viya, SAS also continues to break new ground as well! With a new initiative to become more open to developers via a robust API, and current integration with the Python package known as SWAT (Scripting Wrapper for Analytics Transfer), there are opportunities to take your in-house data science initiatives to a higher level. This session looks at incorporating open-source graphing techniques, specifically Python's matplotlib integrated with the popular D3 visualization framework, to generate interactive plots that can spur discovery of the story your data is trying to tell you. We work through some traditional statistical programming examples via calls in Jupyter Notebook to SAS Viya. Finally, within Jupyter, we convert our static graphs into dynamic graphs using mpld3, an open-source Python library that marries D3 to Python	proc hpsplit, sas viya, dataset	source	12
211	Optimization of Biopsies Using Tumor Modeling and Clustering Methods	Richa Sehgal		proc cluster, proc step	source	13
212	Optimizing Product Assortment with Total Unduplicated Reach and Frequency Analysis in SAS/OR	Jay Laramore	Within the market research industry, Total Unduplicated Reach and Frequency (TURF) analysis has become an increasingly popular technique used to determine which combination of products will appeal to the greatest number of consumers. For companies that rely on optimal product assortment to help drive profitability, the output from a well-designed TURF analysis is critical for understanding product cannibalization and for evaluating the tradeoffs associated with adding or removing specific products from a product line. Conventional approaches for TURF analyses have involved calculating all possible product combinations, only to then recommend the one optimal, or the few near-optimal, solutions. This exhaustive approach is computationally inefficient and does not scale to commercial sized problems, which can often involve dozens of products, thousands of consum ers, and tens of millions of product combinations	linear programming, scenario analysis, sample, correlation, sample size, proc freq	source	16
213	Orchestration of SAS Data Integration Processes on AWS	Rohit Shetty, Paul Daley, Prasanth Reddy	This paper talks about the orchestration of SAS data integration processes based on the arrival of the SAS data integration input files in Amazon Web Services (AWS) S3. Our client runs a daily process where they generate credit statements for their customers. Each customer receives their statement once in a month. Every day, around 200K customers are processed, eventually reaching out to their entire customer base of roughly 6 million in a month. The process starts in their on-premises datacenter, followed by the APR calculations in SAS data integration in AWS, and finally culminates with the generation of the statements in the on-premises datacenter. The entire architecture is built using microservices; small independent and highly decoupled components such as AWS Lambda, Simple Notification Service (SNS), Amazon Simple Storage Service (Amazon S3), and Amazon Elastic Compute Cloud (EC2). This makes it easier to troubleshoot any issues in the data pipeline	sas data integration studio, amazon s3, data integration	source	5
214	Our Journey: Getting on SAS Grid	David Wilson, Annette M. Green, Karen Terminiello	RTI International made the move from PC and server-based SAS to SAS Grid this past year. The decision to move to the Grid was based on cost over time, scalability - allowing room for growth, centralization of SAS administration, improved performance, and a highly available environment. This paper will focus on how the overall transition occurred from the decision to move to SAS Grid to getting 400+ SAS users successfully transitioned within a 12-month period on both the Federal Information Processing Standard (FIPS) Low (limited adverse effect) and FIPS Moderate (serious adverse effect) environments. The process included getting sponsorship from the business units and IT, identifying an implementation partner, forming stakeholder teams, gathering requirements and designing the system, establish a plan and timeline for communication, implementation, training, and rollout	sas grid, excel, sas studio, sas base, sas programmer, sas enterprise guide	source	15
215	Overcoming the Blockers: Analytics Adoption Strategies Used by Successful Organizations	Patricia S. Aanderud, Reid Baughman, Ken Matz, Chris St. Jeor	Every month, it seems there is a modern technology introduced that can transform your organization in unimaginable ways. Despite the rapid growth of analytic solutions, a recent Gartner survey revealed that almost 75% of organizations thought their analytics maturity had not reached a level that optimized business outcomes. The hype is not matching reality. Just like with any endeavor, your organization must have a planned strategy to achieve its analytical goals. Even the best-intended projects die on the vine if not given the proper support. In this paper, you learn how to determine your organization's current analytic maturity level, ways for overcoming common blockers, and elements used in successful analytics adoption strategies. Our team has assisted multiple organizations as they transform from simple reporting to data optimization. Using this information, you can lead your organization in creating a successful roadmap	data cleaning, business intelligence, data science, predictive analytics	source	16
216	Parents in Prison: Correspondence Analysis (CA) for Criminal Justice Phenomena	Wendy B. Dickinson	Presentation	proc corresp	source	9
217	Planning for Migration from SAS 9.4 to SAS Viya	Don B. Hayes, Spencer Hayes, Michael Shealy, Rebecca Hayes, Carlos Phillips	SAS Viya is the new Analytics architecture based on the Cloud Analytic Services (CAS) In-Memory Engine delivered by the SAS Institute Inc. SAS Viya brings a change in the fundamental methodology of installing SAS by moving away from SAS Software Depots and towards industry-standard software deployment methods. This paper will compare and contrast SAS 9.4 with SAS Viya in several key areas for Administrators including pre-install requirements, installation processes, administration tools and methods, and data source connectivity including library definitions. The paper will also discuss upgrade and migration planning	sas viya, cas, sas studio, sas 9, parallel processing, sas administrator, proc step	source	9
218	Playing Favorites: Our Top 10 Model Studio Features in SAS Visual Data Mining and Machine Learning	Wendy Czika, Peter Gerakios	Model Studio in SAS Visual Data Mining and Machine Learning provides a pipeline-centric, collaborative modeling environment that enables you to chain together steps (into a pipeline) for preprocessing your data, making predictions by using supervised learning algorithms, and then using the assessment measure of your choice to compare models. After the pipeline has determined the champion model, you can deploy that model to score new data. Since the first release of SAS Visual Data Mining and Machine Learning, many enhancements have been made to Model Studio, from nodes for running code (open-source code, batch code from SAS Enterprise Miner, or score code that is generated outside Model Studio) to more integration with other visual environments to allow for seamless exploration and visualization of your data as you build models	supervised learning, score code, machine learning, data mining, outlier, proc hpsplit, gradient boosting, feature engineering, proc hpforest, feature selection, sample, champion model, sas viya, proc varclus, proc step, regression analysis, hyperparameter tuning	source	16
219	Power Up Your Reporting Using the SAS Output Delivery System	Chevell T. Parker	Making sense of a large amount of data is one of the most important aspects of a reporting system. Reporting helps you and others in your organization discover important insights into trends, business strengths and weaknesses, and the overall health of a company. Therefore, report output should be in a format that anyone can understand easily. To create such output, you need to use the correct reporting tools. This paper, written for data analysts, discusses techniques to power up (amplify) the effectiveness of your reporting. These techniques use SAS Output Deliver System (`ODS`) destinations (especially the `ODS` `EXCEL` destination) to generate functional, presentation-ready Microsoft `EXCEL` worksheets. The discussion also explains how to use the `ODS` destinations to enhance web pages and other types of documents. Finally, the paper explains how you can use Python open-source software with the SAS System and `ODS` destinations to further enhance your reporting	excel, proc step, format statement, output delivery system, ods excel, filename statement, proc export, user-defined format, ods html, e., numeric variable, proc print, proc report, macro variable	source	21
220	Powering Business Users with one-click insights and custom reports using SAS	Venkata Sivarami Reddy Karimiddela, Lokendra Devangan, Malleswara Sastry Kanduri	One of the challenges which business users have is handling data of variety and size and then processing it to extract information to make business decision(s). SAS Enterprise Guide (EG) can handle complex data files easily with different file formats comi ng from the various data sources. SAS Visual Analytics (VA) with its rich visuals can be used to develop business - friendly dashboards. Did you ever felt the need to publish/update the comments/insights while analyzing the results in the dashboard so that it's visible to others in real time? SAS VA has the unique capability to solve this use case using SAS stored procedure(s) by interacting with external URL(s). This paper explains how a user can enter their comments/insights to publish them real -time in the SAS VA dashboard	sas visual analytics, excel, sas enterprise guide, stored procedure, proc step	source	9
221	Predicting Inside the Dead Zone of Complete Separation in Logistic Regression	Robert Derr	Logistic regression modeling can result in perfect prediction of the responses in your training data, which unfortunately can correspond in practice to very imperfect prediction of the responses in a new data set. This imperfect prediction is often due to complete separation of your data, which means that there is a way to fit the model so that all the events have high predicted probabilities and all the nonevents have low predicted probabilities. In particular, there is a region that separates your event data from your nonevent data and that contains no observations. This "dead zone" has no information about your model, and hence you cannot evaluate the correctness of any predictions inside this region. This paper illustrates the dead zone and provides methods to find and measure it	proc step, sample, logistic distribution, logistic regression, regression analysis, proc logistic, likelihood function, main effect, proc discrim	source	15
222	Predicting Patients at High Risk of Hospital-Acquired Conditions Using the LOGISTIC Procedure	Jennifer Hargrove, Pamela Hipp, Glenn Sidle	Hospital-acquired conditions (HACs) are health-care complications that occur during inpatient hospital stays. Given that HACs can potentially be avoided if hospitals follow standard clinical evidence-based guidelines, Medicaid and Medicare reduce reimbursement for these conditions. This paper demonstrates how the `LOGISTIC` procedure can identify patients at high risk of developing HACs during an inpatient stay. `PROC LOGISTIC` predicts the probability of an outcome given a set of covariates by using logistic regression models and maximum likelihood estimation. Features in the procedure enable users to select covariates associated with the highest probability of having an event. This paper demonstrates how to use `PROC LOGISTIC` to identify patients at an elevated risk of HACs based on patients' prior medical history	logistic distribution, proc logistic, proc step, logistic regression, insurance risk score, sample, binary variable, confidence interval, regression analysis, odds ratio, stepwise selection, likelihood function, medical history, maximum likelihood estimation, missing value, insurance claims	source	12
223	Predicting the success of a startup company	Vrushank Shah, Miriam McGaugh	More than 50% of startup companies fail in the initial four years. Further, three out of every four venture-backed firms fail. The algorithm proposed in this paper will help to predict the success of a startup company based on financial and managerial variables. This prediction will help investors to get an idea whether the investing in a startup will be successful or not? Apart from implementing a model consisting of all the factors mentioned below and predicting the success of a startup company, various other models will be created representing various milestones achieved by the company. This paper will help startup companies to know which factors are essential for getting an investment. The algorithm will be based on more than 15,000 companies' data collected from crunchbase.com	survival analysis, logistic distribution, logistic regression, neural network, response variable, regression analysis, data mining	source	11
224	Predictive Warranty Analytics using SAS Field Quality Analytics to generate Proactive Insights	Prateek Kumar Singh, Saket Shubham, Shiv Kumar, Paresh Peter Rodrigues	In today's data driven world, unprecedented information related to product reliability and field quality can be furnished through warranty claims data. The tools and techniques used in Warranty analytics assist the quality management teams in decision making and extraction of meaningful information by analyzing structured as well as non -structured warranty d ata with statistical techniques. Predictive warranty analytics helps in unearthing potential problems early on and identifying emerging issues before they become huge, costly problems, and enables to initiate the problem-solving process months in advance. SAS Field Quality Analytics (FQA) , a suite for quality analytics, assesses data from warranty, customer service, product and other relevant sources to detect emerging issues sooner and provide actionable insights	weibull distribution, variable	source	20
225	Prevent Culture Shock with SAS National Language Support Formats	Yingying Zhao	Working in different cultures results in a wide variety of data presentation among different regions and languages. If you have trouble showing data that complies with local culture s or customer habits, SAS National Language Support (NLS) formats are here for you! This paper shows you the power of NLS formats to translate SAS output into meaningful results for users anywhere in the world. This paper demonstrates how using NLS formats in typical user scenarios gives you great usability and flexibility for your data presentation	sample, format statement	source	14
226	PROC DS2, An Intro	Dari Mazloom	`PROC DS2` is a new programming feature of SAS programming language which introduces concepts of methods, modularity, and encapsulation. A DS2 program encapsulates variables and code. DS2 is included with BASE SAS language 9.4. based on the `DATA STEP` language. DS2 provides new data types which make possible to have better precision for data and better interaction with external databases. DS2 provides three separate but related system-defined methods which run in order. The methods are: INIT(), RUN(), and TERM(). In addition, DS2 provides a capability for multi-threading. It also allows the SAS practitioners to define their own custom methods called user- defined methods. This paper concentrates on how SAS programmers can use `PROC DS2` to write modularized programs which make for easier testing and maintenance. It also introduces the basic programming instructions(syntax) associated with the construction and use of `PROC DS2`	proc step, integer	source	12
227	Procedure Code Explosion: Applying SAS Macro Language, HASH Tables, and PROC FCMP to Manage Large Volumes of Procedure Codes in Administrative Data Research	Matthew Keller, Dustin Stwalley, Katelin B. Nickel	Managing a large volume of diagnosis and procedure codes is a common task in health services research that uses administrative data. One such collection of procedure codes is provided by the 39 unique categories of surgical procedures for the purposes of healthcare-associated infection surveillance.1 To identify these surgical categories in a dataset, a simple programming method would be to write 39 If-Else If statements referencing a single variable containing procedure codes. The issue with this approach is that there are over 1,100 Current Procedural Terminology (CPT) codes in the most recent NHSN release. Our approach to reducing the written text in a program is to use a macro to write If statements for the 39 variables using a user-defined function. The function searches the inputted CPT code in a hash table holding the NHSN-specific CPT codes	hash table, if-then-else, macro facility, composite key, proc fcmp, conditional do loop, insurance claims	source	3
228	Process Manager Batch Job Monitoring	Pradeep Ponnusamy	The paper explains how one can best use the information stored in a history.log file that contains information about the jobs running in process manager. A SAS program can be written to read the history.log file to identify failed jobs, long-running jobs, CPU consumption of the jobs, execution time of jobs, and so on	dataset, proc sql, proc step, input statement, sample, create table, macro definition	source	9
229	Proper Planning Prevents Possible Problems: SAS Viya High Availability Considerations	Edoardo Riva	SAS Viya is used for enterprise-class systems, and customers expect a reliable system. Highly available deployments are a key goal for SAS Viya. This paper addresses SAS Viya high-availability considerations through different phases of the SAS software life cycle. After an introduction to SAS Viya, design principles, and intra-service communication mechanisms, we present how to plan and design your SAS Viya environment for high availability. We also describe how to install and administer a highly available environment. Finally, we examine what happens when services fail and how to recover	sas viya, sas studio, cas	source	21
230	Pseudo-Random Numbers in SAS Model Implementation Platform	Joel Dood	Monte Carlo simulation can be used in SAS Model Implementation Platform to estimate the average cash flow from a loan under one or more set of economic assumptions. Some users want an independent simulation for each scenario, while others might want to use the same set of random numbers for each scenario. If you do not set up the random numbers in a specific way, then you might not get the numbers that you are expecting. SAS 9.4M5 introduced new pseudo-random number generators (PRNGs) and new subroutines that enable you to manipulate multiple pseudo-random number streams. These enhancements enable SAS Model Implementation Platform to generate PRNGs that are well-suited for a wide variety of needs. This paper describes techniques for using these features correctly, according to your specific purpose	monte carlo, sample, call streaminit, correlation	source	11
231	Puzzle Me, Puzzle You: How a Thought Experiment Became a Rubik's Cube Among a Set of Fun Puzzles	Amit Patel, Lewis Mitchell	How do you help a community of SAS users become more involved in the software, and learn new ideas without providing direct training? We decided to answer this question by designing a series of puzzles, which require the solutions to be run in SAS. This allowed us to showcase SAS features that were unused by many of our colleagues and at the same time promote a critical thinking mind set when solving the puzzles. Whether its X marks the spot using office locations and geolocational data, solving a Rubik's cube that utilises the `SGPLOT` procedure, or a dataset based crossword using SAS Procedure names, each puzzle requires a different technique to solve. Our aim is to help users expand their SAS knowledge and develop a different way of thinking that they can apply to their day-to-day work	macro variable, macro call, proc sgplot	source	23
232	Quick Tips and Tricks: Perl Regular Expressions in SAS	Pratap Singh Kunwar	Programming with text strings or patterns in SAS can be complicated without the knowledge of Perl regular expressions. Just knowing the basics of regular expressions (PRX functions) will sharpen anyone's programming skills. Having attended a few SAS conferences lately, we have noticed that there are few presentations on this topic and many programmers tend to avoid learning and applying the regular expressions. Also, many of them are not aware of the capabilities of these functions in SAS. In this presentation, we present quick tips on these expressions with various applications which will enable anyone learn this topic with ease	proc sql, prxmatch	source	9
233	Railways Scheduling and Rolling Stock Optimization using SAS	Ayush Tiwari, Lokendra Devangan, Malleswara Sastry Kanduri, Faiz Hamid	Passenger comfort, optimal utilization of rolling stock and proper allocation of operational resources are the essential aspects for efficient management for any rail infrastructure. In this paper we address a scheduling problem for railways with the aim t o minimize travel time of all the trains of a route by reducing the number of stoppages and optimal allocation of passenger quota/seats between every pair of stations in each train using modern SAS technologies and `SAS/OR` software . An attempt is also ma de to investigate whether the given demand can be met by lesser number of trains . Mixed integer linear programming model is formulated to meet the two simultaneous decisions in scheduling objective- to minimize the number of running trains as well as their stoppages . The computational results with real life study on Indian Rail Network show a significant reduction in the number of stoppages and trains while satisfying the given demand	odds ratio, sas enterprise guide, proc step, binary variable, linear programming	source	10
234	Re-Mapping a Bitmap	Paul M. Dorfman, Lessia S. Shajenko	Bitmap lookup was introduced into SAS programming in 2000. Yet, its virtues started to gradually fade from the mind of SAS programmers after the advent of the SAS hash object a few years later. One reason is that the hash object requires no custom coding. The other is that its table lookup functionality seems to supplant the utility of bitmapping. However, this impression is false: In its niche of searching against a massive number of limited -range integer keys, a properly constructed and accessed bitmap is both much faster and incurs much smaller memory footprint. In this paper, we will illustrate programming methods of allocating, mapping, and searching bitmaps based on both character and numeric arrays and discuss their relative pros and cons in terms of lookup speed and memory usage	numeric variable, memory usage, temporary array, macro variable, lookup, character variable, hash table, bitmap	source	33
235	Real-World Evidence and Population Health Analytics: Intersection and Application	David Olaleye	Regulatory agencies and pharmaceutical companies use real-world evidence (RWE) to generate clinical evidence derived from real-world data (RWD) for routine regulatory drug review and to monitor the usage and potential benefits or risks associated with a medical product in real-world settings. SAS Real World Evidence is a visual RWE and visual analytics platform that enables quick discovery and creation of patient cohorts for population health analytics. We used SAS Real World Evidence to create index event cohorts to perform unsupervised and supervised signal detection analyses involving stroke events and atypical/typical antipsychotic medications. Use cases that show how SAS Real World Evidence enables intersection and application of RWE with population health analytics are provided	adverse events, exposure, propensity score, proc freq, propensity score matching, potential outcomes, confidence interval, bootstrap, response variable, trend analysis, adverse drug reaction, average treatment effect, neural network, analytics platform, robust estimator, treatment effect, proc step, clinical trial	source	16
236	Reduce Table Size and Always Normalize! (Right? Maybe Not!)	Darryl Prebble	For years it's been common knowledge that you should normalize your data to maximize your storage space. Of course, this involves creating and maintaining reference tables. What if the tables become out of date? Am I just causing more problems by introducing complexity like this? In this paper I illustrate a method of automating the normalization of tables to take the work and maintenance out of the equation. I also introduce an alternative to normalization available in Teradata and how it stacks up	lookup, normalization, macro call	source	8
237	Reducing the Space Requirements of SAS Data Sets without Sacrificing any Variables or Observations	Stephen B. Sloan	Presentation		source	15
238	Regulation Techniques for Multicollinearity: Lasso, Ridge, and Elastic Nets	Deanna Naomi Schreiber-Gregory	Multicollinearity can be briefly described as the phenomenon in which two or more identified predictor variables are linearly related, or codependent. The presence of this phenomenon can have a negative impact on an analysis as a whole and can severely limit the conclusions of a research study. In this paper, we will briefly review how to detect multicollinearity, and once it is detected, which regularization techniques would be the most appropriate to combat it. The nuances and assumptions of R1 (Lasso), R2 (Ridge Regression), and Elastic Nets will be covered in order to provide adequate background for appropriate analytic implementation. This paper is intended for any level of SAS user. This paper is also written to an audience with a background in theoretical and applied statistics, though the information within will be presented in such a way that any level of statistics/mathematical knowledge will be able to understand the content	regression analysis, ridge regression, elastic net, lasso regression, proc step, predictor variable, proc reg, response variable, feature selection, estimate, proc glmselect, correlation, bootstrap, proc calis, sample, quadratic programming, proc univariate, logistic distribution, condition index, proc corr	source	23
239	Review and Comparing (Analyzing) Relationship between Teacher Judgment and Student Performance in North Carolina	Sareh Meshkinfam, Julie S. Ivy, Amy C. Reamer	A teacher's assessment or judgment of an individual student's performance can impact other educators' expectations of that student's ability as well as the student's future academic placement. Exploring the relationship between teacher judgment and student performance in primary education is critical, as early barriers can evolve into significant academic hurdles for individual students at the middle school and high school levels. Correlation analysis and regression models have been used to analyze the longitudinal and cross-sectional relationship between students' achievements and teacher judgment in reading and mathematics across grades and years, considering students' demographics. SAS procedures (such as `PROC CORR` and `PROC MIXED`) were used to explore a large data set from the North Carolina Education Research Data Center (NCERDC), which includes 6,511,741 students in 3rd to 8th grades from 2006 to 2013	multiple regression, correlation, regression analysis, proc step, sample, predictor variable, proc mixed, proc glmselect, data science, sample size, likelihood function	source	15
240	Sample Size Determination with SAS Studio	William Coar	Experiments are designed to answer specific questions. In order for the results to have a reasonable amount of certainty and statistical validity, a sufficient number of observations are required. To achieve this, the concepts of type 1 error, power, and sample size are introduced in to the experimental design. Real world constraints such as budget and feasibility are equally as important. Thus, statisticians often re-evaluate sample size under varying sets of assumptions, sometimes on the fly. SAS Studio provides a variety of tasks associated with determining sample sizes using a point-and-click approach to enter assumptions, yet it also provides the ability to save the underlying SAS code so that it can be either refined or used at a later time. The purpose of this Hands-on-Workshop is to introduce some of the features of SAS Studio for sample size determination. A number of examples will be introduced, including tests associated with proportions, means, and survival analysis	sample size, sample, sas studio, survival analysis, clinical trial, alternative hypothesis, hazard function, null hypothesis, hazard ratio, binomial distribution	source	13
241	SAS and Containers: The State of the Union 2019	Paul Kent	Containers are a popular new idea for building and deploying applications. At SAS Global Forum 2018, I introduced a recipe for building a Docker container from a SAS Viya order. The container enables you to stand up an editing environment that allows you to perform SAS programming and experiment with SAS Viya and related SAS Cloud Analytic Services (CAS) actions from SAS code or from a Jupyter (Python) notebook. This talk presents a progress report-pre-built containers from SAS, examples for different `SAS/ACCESS` engines, and more complicated deployment patterns onto Kubernetes	sas viya, data lake	source	8
242	SAS and Teradata: SEe the Advances	Heather Burnette, Greg Otto, Salman Maher	If you haven't heard about SAS with Teradata Vantage, you will soon. Teradata's offering has evolved beyond a leading enterprise data warehouse into Teradata Vantage, a modern architecture that integrates analytic engines, analytic tools & languages with seamless data integration. Vantage extends support for data types, formats and sources beyond traditional structured data. SAS is an integral and essential component in a Vantage deployment, as SAS delivers analytic tools and languages that extend the new Vantage platform to create a complete analytic ecosystem. In this session, we will focus on answering these questions: How does SAS fit into the Teradata Vantage analytics architecture? What are the benefits of using SAS with Teradata Vantage? Can I put my analytic solution in the cloud? Do I need to change my SAS code? Or my SQL? * Can you show me some sample use cases?	proc step, proc sql, machine learning, dataset, create table, sql pass-through, teradata, proc sort, sample, in-database processing	source	15
243	SAS Edge to core/Cloud - Customer Presentation	Kannan Mani	Presentation		source	5
244	SAS Enterprise Miner and SAS Visual Data Mining and Machine Learning Hand Shake	Jagruti Kanjia, Dominique Latour, Holly Sweeney	SAS Enterprise Miner and Model Studio are two solutions that you can use to create predictive and classification models. In this paper, we show that although these applications have different architectures and run in different environments, we can integrate models generated in one environment and compare them with models produced in the other. In SAS Enterprise Miner, we show how the SAS Viya Code node can be used to create models based on SAS Visual Data Mining and Machine Learning and integrate them into a SAS Enterprise Miner project. For Model Studio, we describe how models generated in SAS Enterprise Miner can be integrated into a Model Studio pipeline for the purpose of comparison. We also discuss how you can use the SAS code node in Model Studio to produce user-defined models. We hope that a better understanding of these capabilities can help users to fully use the rich functionality and flexibility of these products	data mining, score code, sas viya, proc step, machine learning, training set, data cleaning, sample, macro variable, feature importance, cas, supervised learning, dataset, batch processing, logistic regression, imputation, bar chart, estimate, gradient boosting, feature selection, logistic distribution, regression analysis, response variable	source	35
245	SAS Functions to Drive Source Control with Git	Danny Zimmerman	Whether you work with Java, C#, or web development technologies, source control plays an important role in software development. SAS is no different. These days, the front runner in the source control world is Git. Git is a widely used distributed source control system with one remote repository hosted on a website such as GitHub and local repositories on your computer. This paper provides a look at the Git functions in SAS 9.4M6. Highlights include an introduction to the functions and why they were developed; a quick overview of what Git is; a series of workflow scenarios that cover the functions; a functions section that describes each function; and an overview of the Git interface in SAS Studio 3.8	sas studio, data step, sas log	source	26
246	SAS Grid Quick Start 2.0 on Amazon Web Services	Siddharth Prasad, Srikanth Mergu	SAS Grid is a shared, centrally managed analytics computing environment that features workload balancing and management, high availability, and fast processing. A SAS Grid environment helps you incrementally scale your computing infrastructure over time as the number of users and the size of data grow. It also provides rolling maintenance and upgrades without any disruption to your users. This Quick Start bootstraps the infrastructure for a SAS Grid cluster and installs the SAS Grid software, which includes SAS Grid Control Server, SAS Grid nodes, SAS Metadata Server, and SAS mid-tier components. For storage, you can choose Cloud Edition for Lustre or IBM Spectrum Scale	sas base, bootstrap	source	5
247	SAS Macros Beyond the Basics	Ronald Coleman	Basic macros rely on symbolic substitution to place values in particular locations in SAS code-they simply create code. Beyond that, the sky is the limit! We cover the following advanced techniques: create and populate macro variables; read a list of file names from a folder and use the list for processing code; utility macros that calculate new values without the need for a `DATA STEP`; and advanced macro functions. We also include a discussion about macro quoting. Some pointers for passing values when you are using `SAS/CONNECT` and SAS Grid are also discussed	macro variable, symbol table, ods pdf, macro put, sas function, call symputx, macro definition, macro quoting function, output delivery system, macro function, &variable, proc step, macro loop, proc sql, %if-%then-%else	source	12
248	SAS ODS Destination for Microsoft Excel: Use the STYLE Option to Spruce Up an Excel Output Workbook	William E. Benjamin Jr.	The SAS environment maintains many different output styles to use to enhance the visual display of your output data. The `ODS` destination for `EXCEL` can take advantage of these styles maintained by SAS to apply formatting and color schemes to your `EXCEL` output workbooks. I show you how to use the STYLE option in the `ODS` destination for `EXCEL` to enhance your output workbooks	excel, xls, ods excel	source	8
249	SAS on Kubernetes: Container Orchestration of Analytic Work Loads	Sumanth Yamala	With vast amounts of data, the infrastructure demands for advanced analytics is ever increasing. There are a class of analytic problems like optimization, forecasting, machine learning in domains of retail, finance, and other verticals, where the problem could be broken down into smaller sub-problems. Example, in retail; assortment by store, or price optimization by category, in finance; security portfolio optimization by industry, the entire problem is broken down into subset of smaller jobs. In this paper we present orchestrating these jobs on Kubernetes, GKE powered by Google Cloud Platform (GCP). We marry the best in analytics; SAS, with the best in container orchestration, GKE, to build a robust solution to manage analytic workloads. The solution will have the following properties: scalable, performant, elastic, cost efficient and simple to deploy	sas viya, proc hpsplit, cloud storage	source	6
250	SAS Studio or SAS Enterprise Guide: What's the Best SAS Programming Interface for Me?	Amy Peters, Samantha DuPont	In some people, loyalty to an interface can run as deep as loyalty to a football team, and it's been no different for SAS users. The good news is that a concerted effort is underway to remove the barriers between interfaces and the differences between them so that you can just choose what you use based on where you are. Are you on a desktop? A mobile device? In a conference room? This paper talks you through the integrated future that's under development. In the meantime, this paper helps you to understand what the strengths and weaknesses are of the programming interfaces today so that you can choose wisely. You also learn tips and tricks for working efficiently. The paper focuses on SAS Studio and SAS Enterprise Guide, but it also touches on several of the other SAS programming interfaces that are available, including Jupyter	sas enterprise guide, sas studio, sas viya, sas programming	source	8
251	SAS Technical Support Top-Five Lists for SAS Environment Manager on SAS Viya	Ursula Polo, Allison Mahaffey	SAS Environment Manager is a powerful web application that enables administrators to monitor and administer their entire SAS Viya platform. There are many tips and tricks to using this application to its fullest potential. Because SAS Technical Support Engineers support a wide variety of customer deployments, they have unique insight. This paper shares some of this insight into SAS Environment Manager through the following top-five lists: administration tips, lesser-known features, and documentation resources	sas viya, memory usage, audit dataset, bar chart	source	19
252	SAS Viya and Kerberos: From the Ground Up	Spencer Hayes, Michael Shealy	Kerberos is a network authentication protocol commonly found in medium and large enterprises. It uses strong cryptography to prove the identities of users, hosts, and services. SAS has long been able to integrate with Kerberos to not only support its authentication strengths, but to enable user -friendly features such as Integrated Windows Authentication (IWA). However, SAS delegates some key configuration and operational tasks to the Kerberos realm and operating system layers, and their respective administrators. These include Kerberos principal configuratio ns, ticket requests, ticket cache management, OS-layer authentication, and more. For this reason, it can be difficult for SAS administrators to fully see and understand the entire SAS and Kerberos integration picture	sas viya, sas administrator, sas studio, cas	source	38
253	SAS Viya report Images Service: The Report Optimization Speedometer	Michael Drutar	SAS Viya offers several techniques that can maximize the speed of SAS Visual Analytics reporting: data partitioning, user-defined formats, and the use of aggregated data. However, every SAS Visual Analytics report can be different: different data, different graphs, and other differences in terms of filters, interactive widgets, and more. Testing how changes to individual reports affect speed can be laborious and might involve manually opening reports in the SAS Report Viewer several times and meticulously reviewing each report's diagnostics or microservice logs. Even with this information, external factors such as network performance can confound the diagnostics. This paper presents a programmatic way to call a SAS Visual Analytics report to quickly determine how long it takes the report to render using the reportImages service, available via the SAS Viya REST API	sas viya, macro variable, rest api, proc step, libname engine, proc http, conditional do loop, macro definition, dataset	source	12
254	SAS Viya: Experience Gained through Multiple Installations and Implementations	John Schmitz	SAS Viya offers new and exciting capabilities for the SAS Platform, but the SAS Viya capabilities and designs might be unfamiliar to many SAS users. This paper highlights experiences gained from multiple SAS Viya installation and implementation projects involving SAS Viya 3.2, 3.3, and 3.4, from small to very large hardware resource allocations. Key topics include hardware and architectural concepts, leveraging of external data sources, recovery considerations, integration with SAS 9, and daily operation overviews. This paper provides valuable insights to organizations considering or in the early stages of a SAS Viya implementation	sas viya, sas 9, sas studio, machine learning, sas programming, data science, sas enterprise guide, regression analysis	source	14
255	SAS-Coin: Understanding Blockchains Through Simple Data Step Examples	Seth W. Hoffman	Have you ever wanted to start your own small economy, but had no idea how to handle the recordkeeping? In that case, it is time to learn about what "blockchain" technology can do. Using Base SAS code examples, this paper will illustrate the principles behind creating a blockchain system to provide an unalterable record of validated transactions for your project	hash table, sas base	source	7
256	SAS/Access Interface to Hadoop: experiences and Best Practices at a Large Financial Institution	Thomas N. Keefer Jr.	`SAS/ACCESS` Interface to Hadoop is a critical component of integrating SAS environments to the increasing amounts of data available in Apache Hadoop. Although SAS Viya has enhanced features for data lift to memory, there are many day-to-day tasks that will continue to leverage SAS9 technologies for analytics. Therefore, the reliance on an optimal integration to Hadoop with `SAS/ACCESS` is critical, especially for larger data and user volumes. This paper highlights best practices and experiences over and above the standard SAS documentation by sharing real-life experiences from a large financial institution and close work with SAS Research and Development. This paper focuses on using Apache Hive and the Hadoop Distributed File System (HDFS). Other SAS and open source technologies are mentioned when and if they are better choices to meet performance or business requirements	proc sql, sas viya, proc step, in-memory analytics, sas 9	source	8
257	SCORE! Techniques for Scoring Predictive Regression Models Using SAS/STAT Software	Phil Gibbs, Randy Tobias	Scoring new data to compute predictions for an existing model is a fundamental stage in the analytics life cycle. This task has never been easier, given recent additions to `SAS/STAT` syntax. This paper shows how to use these new features in `SAS/STAT` software to make it simple to predict the response for new data. The paper discusses practical applications for scoring with the SCORE statement within a modeling procedure, with the `PLM` procedure using a stored model fit, and with the CODE statement in the `DATA STEP`. The paper also discusses tried-and-true methods of scoring using the `SCORE` procedure and using the missing-value-for-the-response trick	proc step, proc plm, regression analysis, logistic distribution, proc reg, estimate, response variable, sas viya, proc logistic, macro variable, logistic regression, missing value, proc calis, class variable, multiple imputation, proc glmselect, imputation, sample, proc sql, predictor variable, score code, proc genmod	source	18
258	Scouting in Soccer with Applied Machine Learning	Vishal Gaurav, Goutam Chakraborty	After netting 65 goals in 102 league appearances between 2007 & 2011, Fernando Torres became the most sought after player in the 2011 January transfer window. Chelsea spent a whopping £50m to procure the services of the most lethal striker of his era. Torres' move set a British record for most expensive transfer at that time. In the seasons to follow with the Blues, the Spaniard failed to live up to his high price tag. He scored only 19 league goals in 103 matches for Chelsea. Was it the injuries, or, the pressure of the huge expectations? Or, was he just not a right fit for the Blues? Every year we see major soccer clubs spend hundreds of millions of dollars to sign contracts with the players of their interest. More often than not, these deals are based on a player's skills, potential for growth, performance in prior seasons, and brand value. While often these transfer deals pay off, there is an abundance of failed transfers every season	proc princomp, machine learning, group by, feature importance, data mining, box plot	source	19
259	Screen Scraping Using SAS for Healthcare Fraud Prevention and Detection	Hitesh Kharbanda	Fraud in U.S. Healthcare System is rampant. It is estimated that 3-10% of U.S. Healthcare annual expenditure is lost to fraud and abuse. One of the most common types of fraud is phantom provider billing. Phantom provider billing occurs when providers either do not exist or do not hold a valid license and bill false claims for services that were not rendered. Phantom providers frequently change billing patterns on submitted claims to avoid detection from payer edits. One method used to avoid detection is to submit a new Tax Identification Number (TIN) into the payment system whenever their current TIN has been identified as fraudulent. In addition, phantom providers will often use a TIN belonging to an existing legitimate provider in order to disguise fraudulent claims, in a similar manner to identity theft with personal credit `CARDS`	proc step, proc sql, create table, proc http	source	11
260	Screening, Transforming, and Fitting Predictors for the Cumulative Logit Model	Bruce S. Lund	The cumulative logit model is a logistic regression model where the target (or dependent variable) has 2 or more ordered levels. If only 2 levels, then the cumulative logit is the binary logistic model. Predictors for the cumulative logit model might be "NOD" (nominal, ordinal, discrete) where typically the number of levels is under 20. Alternatively, predictors might be "continuous" where the predictor is numeric and has many levels. This paper discusses methods that screen and transform both NOD and continuous predictors before the stage of model fitting. Once a collection of predictors has been screened and transformed, the paper discusses predictor variable selection methods for model fitting. One focus of this paper is determining when a predictor should be allowed to have unequal slopes. If unequal slopes are allowed, then the predictor has J-1 distinct slopes corresponding to the J values of the target variable	logistic regression, logistic distribution, proc logistic, proc step, likelihood function, significance level, degrees of freedom, model training, regression analysis, ordinal logistic regression, predictor variable, feature selection, macro call, macro definition, response variable, proc genmod, sample, macro parameter, proc hplogistic, hypothesis testing, proc freq, binary variable	source	21
261	Seriously Serial or Perfectly Parallel Data Transfer with SAS Viya	Robert Collum	SAS Cloud Analytic Services (CAS) is the engine of our in-memory analytics offerings. It promises to deliver massively scalable performance of huge volumes of data. But then getting all that data loaded into CAS (or saved back out later) is another scalability challenge to tackle. CAS addresses that challenge by providing support for a wide range of data sources starting with native SAS formats and extending to include direct loading from many third-party data providers. Along the way, we also need to carefully plan and evaluate the architecture and infrastructure to ensure that everything is moving along as fast and efficiently as intended. As of SAS Viya 3.4, the language we use to direct CAS in its efforts to transfer data has evolved considerably. We can explicitly configure CAS to use a serial data transfer method, but the objective we're shooting for is effectively parallel	sas log, sas viya, proc step, sample, amazon s3, sas programming, sas syntax, cpu time, dataset	source	35
262	SF12v2 Health Scores for African Americans Using SAS in a Cluster-Randomized Community Trial	Thomas Joshua, Lucy Marion, Lovoria Williams, Jane Garvin	Presentation	standard deviation, data science	source	10
263	Simple Techniques to Improve the Performance of your SAS Programs When Processing Large Data Sets	Steve Cavill	Programs that process large data sets can consume lots of a programmer's time, while waiting for jobs to complete. This paper presents simple techniques that all programmers can use to speed up their programs, leaving time to get more work done (or maybe to play more golf :). Techniques include subsetting, indexes, data set compression and in-memory data. Windows output: 5 options nofullstimer; 6 data `_NULL_`; 7 do i = 1 to 10000000; 8 end; 9 run; NOTE: `DATA` statement used (Total process time): real time 0.10 seconds cpu time 0.11 seconds 11 options fullstimer; 12 data `_NULL_`; 13 do i = 1 to 10000000; 14 end; 15 run; NOTE: `DATA` statement used (Total process time): real time 0.10 seconds user cpu time 0.10 seconds system cpu time 0.00 seconds Memory 135k	cpu time, data step, hash table, lookup, proc step, proc sql, where statement, data merging, sample, proc surveyselect, proc format, proc sort, options statement, sysrc, dataset, index	source	20
264	Smart SAS Environment Manager Agent for SAS Grid Manager with a Shared Configuration Directory	Zhiyong Li	A question frequently asked by SAS customers is how to run SAS Environment Manager in a grid (or a shared configuration directory) environment. As of today, SAS has over seventy customer tickets that are related to this issue. You request, we listen. In SAS 9.4M6, we re-designed the SAS Environment Manager agent to make it "smart": it detects whether the request to run the SAS Environment Manager agent is from a new host or a new grid node. If the request is from a new host, it automatically creates a new SAS Environment Manager agent instance for that host. With this design, we also introduced ma ny capabilities to manage the smart agent to make it easier for customers. The new capabilities include support for update in place, hot fixes, other platforms, and all SAS Deployment Manager tasks. This paper details the design, the implementation, and the process for how you can take advantage of this new smart agent feature	sas grid, customer experience	source	17
265	Solving SAS Performance Problems: Our Methodology	Jim Kuell	Diagnosing performance issues can be a lengthy and complicated process. For many, the most difficult step is figuring out where to begin. This typically leads to a track being opened with SAS Technical Support. The SAS Performance Lab has developed a standard methodology for diagnosing performance issues based on years of experience doing so both internally and at customer sites. This process is regularly applied when assisting with performance issues in SAS Technical Support tracks. This presentation goes through the methodology used by the SAS Performance Lab to diagnose performance issues and discusses resolutions to the most common problems	sas log, cpu time, dataset, options statement, key performance indicator, sas 9, excel	source	8
266	Sometimes SQL Really Is Better: A Beginner's Guide to SQL Coding for DATA Step Users	Brett Jepson	Structured Query Language (SQL) in SAS provides not only a powerful way to manipulate your data, it enables users to perform programming tasks in a clean and concise way that would otherwise require multiple DATA steps, SORT procedures, and other summary statistical procedures. Often, SAS users use SQL for only specific tasks with which they are comfortable. They do not explore its full capabilities due to their unfamiliarity with SQL. This presentation introduces SQL to the SQL novice in a way that attempts to overcome this barrier by comparing SQL with more familiar `DATA STEP` and `PROC SORT` methods, including a discussion of tasks that are done more efficiently and accurately using SQL and tasks that are best left to DATA steps	proc sql, out=, order by, proc sort, where statement, proc means, group by, in=, create table, proc step, if-then-else	source	17
267	Sparking Your Data Innovation: SAS Integration with Apache Spark	Kumar Thangamuthu	Apache Hadoop is a fascinating landscape of distributed storage and processing. However, the environment can be a challenge for managing data. With so many robust applications available, users are treated to a virtual buffet of procedural and SQL-like languages to work with their data. Whether the data is schema-on-read or schema-on-write, Hadoop is purpose-built to handle the task. In this introductory session, learn best practices for accessing data and deploying analytics to Apache Spark from SAS, as well as for integrating Spark and SAS Cloud Analytic Services for powerful, distributed, in-memory optimization	libname statement, proc step, cas action, sas log, spark, hadoop, proc sql, sas viya, cas, data mining, macro variable, by group processing, score code, options statement	source	15
268	Spatial Analysis of Humanitarian OpenStreetMap Team Data Using SAS ODS Graphics Procedures	Michael Matthews	The Humanitarian OpenStreetMap Team (HOT) community consists of volunteers from around the globe. HOT tasks involve developing maps that identify communities and infrastructures based on satellite imagery. These maps are then used to assist aid organizations such as the Red Cross during humanitarian crises and for general community development in areas that are often not covered by the mapping products that most of us take for granted. This presentation examines OpenStreetMap data and introduces HOT and some of the associated mapping tasks, including assisting the aid efforts during the May 2018 Ebola outbreak in the Democratic Republic of the Congo. Analysis is performed on the data using the SAS `ODS` Graphics procedures (including `PROC SGMAP`) to visualize the contributions to OpenStreetMap both spatially and over time	proc step, dataset, sas base, scatter plot, proc surveyreg, proc http	source	19
269	Standardizing Text in your Data	Guihong Chen	SAS offers a variety of useful tools to manipulate character data. These are very handy when data received may not be particularly clean. For example, a character string may contain inconsistent or unwanted special character or formats that cause reconciliation or merging job to fail. This paper provides an example using customer data to discuss several functions to efficiently standardize text variables. Undesirable information can be removed by leveraging the three arguments of the COMPRESS function. Undesirable blanks can be removed by the TRIM, TRIMN or STRIP functions. Inconsistent formats or special characters can be standardized with a consistent character using the TRANWRD, UPCASE, LOWCASE, and PROPCASE functions. However, there may a need to add or reorder a substring. The concatenation operator (\|\|) or functions CAT, CATT, CATS, CATX, SUBSTR, or SCAN can also be used to clean and modify a dataset	compress function, index, character functions, character constant, character variable	source	8
270	Statistical Data Checks to Identify Questionable and Suspicious Data	Kaitie Lawson	Although Electronic Data Capture (EDC) has improved efficiency and timeliness in data entry and analysis in clinical trials, it has reduced the safeguards inherent in double data entry performed by dedicated professionals. EDC is vulnerable to inadequate training, transcription errors, fat-finger errors, negligence, and even fraud. Moreover, recent initiatives in risk-based monitoring are moving away from 100% on-site source data verification. Thus, supplemental data monitoring strategies are essential to ensure data accuracy for statistical analysis and reporting. We have developed a suite of statistical procedures to identify suspicious data values for individual subjects and across clinical sites. They include rounding errors and digit preference checks, univariate and bivariate outlier checks, longitudinal outlier checks within subjects, and variance checks. Generally, regression models are applied to account for demographic characteristics and other important covariates	data cleaning, heatmap, correlation, outlier, box plot, edc, class variable, proc mixed, proc freq, proc step	source	11
271	Stepping Up Your SAS Game with Jupyter Notebooks	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 SAS kernel for Jupyter, you have the power to create these computational narratives and much more!	data science, sas log, sas studio, txt	source	12
272	Stories from the Trenches: Tips and Techniques for Career Advancement from a SAS Industry Recruiter	Molly Hall	Emotional intelligence is having its moment. Research now confirms what we have long suspected. Likability matters. People hire people who they like, and people who they feel they can trust. It's the X factor. Years ago, having worked for one company for a long time was a sort of protection against getting laid off. That's not the case anymore. Jobs are getting outsourced overseas, and employers are hiring contract labor instead of employees. Whether you are a SAS programmer, a statistical analyst, a data scientist, or a manager of SAS programmers, the advice covered in this presentation applies to everyone. We talk about the hidden job market, explore networking channels that work, and discuss trends in recruiting (including artificial intelligence and equal pay legislation). LinkedIn, social media, resumes, cover letters, interview tips and techniques, and all things related to job upward mobility are covered	artificial intelligence, data science	source	15
273	Stuck in Traffick: A Contextual Analysis of Human Trafficking Advertisements	Lauren Ann Agrigento, William Paul Taylor Jr.	The primary objective of this study is to create a set of detailed profiles of web advertisements known to recruit victims into human trafficking and use those profiles to classify other advertisements into the same category. The data used in this study are advertisements involving the sex trade scraped from Backpage.com. These advertisements provide evidence of certain key factors that are indicative of human trafficking. Some factors associated with human trafficking are phone numbers, locations and geographical range, frequency of ads, and specific syntax usage		source	6
274	SuperFreq: using the REPORT Procedure to Create Multi-Level Frequency Tables	Jedediah J. Teres	`PROC FREQ` is a powerful tool for summarizing categorical data. Cross-tabulating variables is a common application of `PROC FREQ`. Cumulative frequency and percents are calculated across the entire data `PROC FREQ` is summarizing. `PROC FREQ` supports BY group processing as well. Using BY group processing, cumulative frequencies and percents are calculated within each BY group. One limitation of `PROC FREQ` is that this is an either/or proposition-- cumulative frequencies and percents are calculated across the entire data set or within each BY group, but not both at once. Displaying multi-level cumulative frequencies in the same output table using `PROC FREQ` requires merging output data from multiple runs. An alternative approach that allows for displaying cumulative frequencies and percents from the full sample alongside cumulative frequencies and percents within each BY group uses `PROC REPORT` rather than `PROC FREQ`	proc step, proc report, proc freq, cumulative frequency, sample, by group processing, class variable	source	17
275	Survey Data, Administrative Data, and Big Data: An Exploration of Quality Indicators	Peter Timusk	Presentation		source	8
276	Surveys: Finding the message in the tables	Alice Feldesman	The Executive As an executive you want to manage your organization using data. It gives you the bottom line and tells you if you are heading in the right direction. Sometimes you call in your research team and you ask them questions that are on your mind---and they say "You'll need to do a survey to answer that one". The Research Team We've lined up people who know how to write the questions and we have someone to write the code to analyze the survey. So for each question with a typical 5 option rating and a no basis to judge and some people left the question blank. So all together for each question there would be 7 percentages. But let's make it more informative by showing for each question the demographics. In the case study described in this article there would be 22 rows for each of 100 questions. Let's see 100 question times 22 rows times 7 response options. That would mean we'd be delivering 15,400 numbers. My experience This happened to me exactly like it was described above	proc step, proc transpose, proc freq	source	12
277	Survival Estimates Prediction with Survey Data	Bocheng Jing, L. Grisell Diaz-Ramirez, W. John Boscardin, Alexandra K. Lee, Sei J. Lee	The SAS `PHREG` procedure includes a BASELINE statement that allows users to easily obtain the survival predictions, standard error, and confidence interval from a survival model. In the setting of complex survey design, such as stratification and multistage sampling from clusters, SAS `SURVEYPHREG` procedure is needed to appropriately incorporate survey weights. However, SURVEYPHREG currently does not support the BASELINE statement. Hence, it is difficult to obtain survival predictions, standard errors, and confidence intervals from survival models derived from complex survey data. In this paper, we explore the threshold behind survival prediction using survey data and provide possible solutions to bypass this obstacle	proc calis, sample, proc phreg, covariance, likelihood function, hazard function, sample weight, confidence interval, hypothesis testing, maximum likelihood estimation, regression analysis	source	12
278	SWAT's it all about? SAS Viya for Python Users	Carrie Foreman	Python is described within the community as the "second-best language for everything" (CALLAHAN, 2018). The strength of Python as a language stems from its huge range of uses including as a tool for data science. Python is taught on many university courses and as a result there are a vast number of coders with Python skills in the data science industry. Last year at Amadeus we explored the use of SASpy as an initial tool to bridge the gap between Python and SAS 9.4 (FOREMAN, 2018), this year we move on to look at integration with SAS Viya. In July 2016, SAS released Python SWAT (Scripting Wrapper for Analytics Transfer), a Python library. It allows connections to CAS (Cloud Analytic Services) and therefore opens up SAS Viya's functionality to Python users. SWAT allows users who do not have a SAS background to perform their data manipulation and analytics using more familiar Pythonic syntax whilst still harnessing the power of CAS and SAS Viya	sas viya, missing value, cas action, box plot, scatter plot, sas studio, numeric variable, histogram, outlier, data science, proc freq	source	18
279	Table Look-Up Techniques: Is the FORMAT Procedure the Best Tool for the Job?	Andrew T. Kuligowski	"For every action, there is an equal and opposite reaction." Sir Isaac Newton was talking about physics, but the concept also applies to other areas of life - including quotation marks in SAS code. Unfortunately, SAS coders do not always properly provide balanced quotation marks while coding. SAS will detect possible occurrences of this problem, signaling its concern with a `SASLOG` message: WARNING: The quoted string currently being processed has become more than 262 characters long. You might have unbalanced quotation marks. This presentation contains a few coding suggestions to identify unbalanced quotation marks in SAS code	macro definition, sas base	source	6
280	Take REST into Account: Account Level Profit with SAS Viya APIs	Richard Carey	The bottom line of a business is directly influenced by a great many factors, none more so than the profit generated or money lost from each customer. Accessing financial performance metrics at the account level is a crucial first step in optimising the p erformance of a company; from improving efficiency of marketing spend to identifying the most desirable customers to retain. SAS Viya's REST APIs provide the ideal route for a bespoke web application to access the power of CAS. In this paper, we explore the options available for manipulating, analysing and reporting on Account Level Profit using Viya REST API calls	rest api, sas viya, ledger	source	8
281	Tell Me a Data Story	Kat Greenbrook	In search of insight, we usually focus on data - access, preparation, modelling, analysis, and visualization - but the value of data isn't always apparent to the rest of the business. Data alone is not motivation enough for someone to invest their time in a project unless the value is clearly obvious. An effective way to engage internal business users with analytics is with a data story. Data stories paint a bigger picture and evoke an empathy for what the numbers actually represent. Contrary to popular belief, a data story is not a data visualization. Data visualization is a great way to tell a data story, but it relies on the story having been written first. A well-written data story, combined with a good visualization, will improve insight communication. Principles of graphic design are rarely taught to analysts, but an understanding of design will lift any data visualization to the next level. Kat shows you how to write a data story and effectively tell it using data visualization	data visualization, data storytelling	source	8
282	Telling Your Data Story	Atrin Assa	The most powerful person on the planet is the storyteller. From Winston Churchill in world politics to J.K. Rowling on the pages of a book-the people who move the world tell compelling stories. This paper explores the most effective tools and techniques in SAS Visual Analytics to tell your data story and move your organization	bar chart	source	7
283	Ten Underused Features That Improve Your SAS Forecast Server Workflow	Evan Anderson, Michael J. Leonard	SAS Forecast Server is one of the most feature-rich forecasting products on the market. This paper describes 10 underused features to improve your workflow. First, start with the data: (1) Use SAS Time Series Studio to become familiar with your data and define a hierarchy, (2) catch data problems through warnings about the time ID variable, (3) look under the covers by using the SAS log, and (4) use adjustment variables and start-up code to fix data issues. Next, improve the forecasts: (5) Define recurring events that influence the time series, (6) add models by importing from an external list, (7) use rolling simulations to evaluate forecast accuracy over the number of periods you need to forecast, (8) evaluate the effects of independent variables by using scenario analysis, and (9) gain insight into results by comparing models. Finally, put your workflow into production: (10) Run the code in batch	time series analysis, time, time series data, scenario analysis, sample, sas log, feature selection, missing value, histogram	source	18
284	Test Analytics and Linguistics: Finding a Linguistic Pattern for the Best Writing Practice Using SAS	Rohit Banerjee	Presentation		source	11
285	Text Analytics at British Airways	Simon Cumming	This paper descibes how British Airways, a leading British airline company, has been using SAS Text Miner and SAS Contextual Analysis to derive insight from textual data sources. We shall describe the 'Topic Discovery Tool', which is an application of SAS Enterprise Guide developed at British Airways to provide a customised user interface to Text Miner. Some of the challenges of text analytics, as well as some tips and tricks will be discussed	sas enterprise guide, time series analysis, proc step	source	18
286	Text Mining of Open-Ended Survey Data	Brandon Hosek	Presentation		source	8
287	The "Why" and "How" of Teaching SAS to High School Students	Jennifer Richards	Learning SAS for the first time as a budding epidemiologist in graduate school, I remember being amazed at how powerful and customizable SAS was in making sense of huge amounts of data. I also remember being very intimidated by all the moving parts involved in learning such a program! My study and use of SAS has continued since that time, specifically now as a Doctor of Public Health student. I have often wondered how much farther along my research would be at this point had I learned SAS in high school. When one of my professors recently told me that resources are available for teaching SAS at that level, it prompted me to ask a local high school teacher what she thought about teaching SAS to her students. When she replied that she thought it was a great idea but did not know how she could do this, I realized this was a gap that needed to be filled	data science, big data	source	5
288	The ABCs of the HTTP Procedure	Joseph Henry	Hypertext Transfer Protocol (HTTP) is the foundation of data communication for the World Wide Web, which has grown tremendously over the past generation. Many applications now exist entirely on the web, using web services that use HTTP for communication. HTTP is not just for browsers since most web services provide an HTTP REST API as a means for the client to access data. Analysts frequently find themselves in a situation where they need to communicate with a web service or application from within a SAS environment, which is where the `HTTP` procedure comes in. `PROC HTTP` makes it possible to communicate with most services, coupling your SAS system with the web. Like the web, `PROC HTTP` continues to evolve, gaining features and functionality with every new release of SAS. This paper will dive into the capabilities found in `PROC HTTP` allowing you to get the most out of this magnificent procedure	proc http, proc step, macro variable, web service	source	16
289	The Aftermath What Happens after you Deploy Your Models and Decisions	David R. Duling	We're making it easy to deploy your models and decisions to numerous run-time environments. However, the model life cycle doesn't end once the model is created. Rather, it is just the beginning of the important phases of model monitoring and analysis. This need extends to SAS models and open-source and Predictive Model Markup Language (PMML) models. In this demonstration, you learn techniques for analyzing model performance, integration with business metrics, and root cause analysis	score code, model deployment, data step, model monitoring, feature selection, model training, network optimization, sample, machine learning, neural network, root cause analysis, web service, gradient boosting, model testing, response variable, training set, missing value, logistic distribution, regression analysis, data cleaning	source	18
290	The Anatomy of an Artificial Intelligence Solution for Automation of Underwriting Decision-Making	Tanya Kolosova	Experience and good judgment are essential attributes of underwriters. Systematic analysis of the underwriting decision-making process was expected to increase the efficiency in developing these attributes in underwriters. However, this approach fell short of expectations. The industry still struggles with the pace at which knowledge and experience are delivered to the next generation of underwriters. The solution may lie in the development and deployment of artificial intelligence (AI) methods and algorithms in the automation of the underwriting decision-making process. This paper outlines the current state of the performance measurement of underwriting decision-making process through performance metrics (including a novel one). Further, this paper provides an in-depth description of artificial intelligence methods and algorithms that can be successfully used to automate underwriting decision-making process	logistic regression, logistic distribution, regression analysis, artificial intelligence, training set, sample, bias-variance tradeoff, key performance indicator, workers compensation, bootstrap	source	17
291	The Bayesians are Coming! The Bayesians are Coming! The Bayesians Are Coming to Time Series!	Aric LaBarr	With the computational advances over the past few decades, Bayesian analysis approaches are starting to be fully appreciated. Forecasting and time series also have Bayesian approaches and techniques, but most people are unfamiliar with them due to the immense popularity of Exponential Smoothing and ARIMA classes of models. However, Bayesian modeling and time series analysis have a lot in common! Both are based on using historical information to help inform future modeling and decisions. This talk will compare the classical Exponential Smoothing and ARIMA class models to Bayesian models with autoregressive components. It will compare results from each of the classes of models on the same data set as well as discuss how to approach Bayesian time series models in SAS	time series analysis, proc esm, lag function, response variable, sample, autoregressive model, proc step, posterior probability	source	13
292	The Benefits of Keeping It Casual: Investigating Stress-Mediated Health Outcomes with PROC CAUSALMED	Daniel Muzyka, Matthew Lypka	In epidemiological science, researchers aim to associate exposures to specific health outcomes. In psychological sciences, researchers want to investigate how an intervention improves quality of life. In the social sciences, policy makers want to quantify how effective work release programs are in reducing re-incarceration rates. When investigating these relationships, it is tempting to conclude that an independent X causes a dependent Y. However, though significant associations may exist, the mechanisms behind those relationships are not always apparent. Often, additional variables influence the impact of your independent X variable. To accurately model these additional variables in inferential statistics, you must classify them based on their hypothesized role in the relationship. In some situations, it may be hypothesized that X acts on, and by proxy, exerts effect on Y through a mediating variable, M	proc step, odds ratio, proc freq, nominal variable, mediation analysis, response variable, randomization	source	19
293	The Best of Both Worlds: Forecasting Using Time Series with Inputs	David A. Dickey	This paper shows how to use regression with autocorrelated errors. It features examples using the SAS procedures AUTOREG and ARIMA. Issues arising in the use of these procedures and a comparison of features of each to those of the other are presented. The emphasis is on when to use each procedure, how to understand the results, and how to use diagnostics to improve the model	proc calis, regression analysis, proc step, moving average, proc autoreg, estimate, outlier, correlation, binary variable, time series analysis, proc arima, proc reg, proc genmod, autoregressive model, predictor variable, unit root, covariance, response variable, residual, numeric functions, proc glm, maximum likelihood estimation, likelihood function	source	19
294	The Categorical Might of PROC FREQ	Jinson J. Erinjeri, Saritha Bathi	`PROC FREQ` is essential to anybody using Base SAS for analyzing categorical data. This paper presents the various utilities of the `FREQ` procedure that enable in effective data analysis. The cases presented include a range of utilities such as finding counts, percentages, unique levels or records, Pearson chi -square test, Fisher's test, McNemar test, Cochran-Armitage trend test, binomial proportions test, relative risk, and odds ratio. In addition, this paper will show the `ODS` features available to effectively display your results. All the cases presented in this paper will prove the cate- gorical might of `PROC FREQ` beyond doubt	proc freq, proc step, binomial distribution, monte carlo, data science	source	18
295	The Essentials of SAS Dates and Times	Derek P. Morgan	The first thing you need to know is that SAS software stores dates and times as numbers. However, this is not the only thing that you need to know. This presentation gives you a solid base for working with dates and times in SAS. It introduces you to functions and features that enable you to manipulate your dates and times with surprising flexibility. This paper shows you some of the possible pitfalls with dates (and times and datetimes) in your SAS code and how to avoid them. We show you how SAS handles dates and times through examples, including the ISO 8601 formats and informats and how to use dates and times in TITLE and FOOTNOTE statements. The presentation closes with a brief discussion of `EXCEL` conversions	sysdate9, date variable, sample, excel, macro variable, proc format, missing value, proc step, error message, user-defined format, character variable, input statement, numeric variable, time, flat file, time., macro function	source	25
296	The Incredible, Accessible Report: Creating PDF Reports that Meet Compliance Standards in SAS 9.4M6	Greg Kraus	SAS 9.4M6 enables SAS programmers to create PDF reports that fully meet the Web Content Accessibility Guidelines 2.0 (WCAG 2.0) conformance requirements. These are the guidelines that government and industry use to determine whether electronically produced output is usable by people with disabilities. With the accessibility features in SAS 9.4M6, it is possible to create reports that require zero post-processing remediation work, thus saving you time and money. By using the PRINT, TABULATE, and REPORT procedures, and the `ODS` Report Writing Interface (RWI), SAS 9.4M6 can prompt you to address certain accessibility problems detected in your code, create tables of data that are structured to be fully accessible to users, and add alternative text for images inserted into your reports. This paper demonstrates how to use these reporting procedures to create accessible PDF reports	proc step, proc report, proc tabulate, proc print, output delivery system, options statement, ods pdf, proc univariate, sas enterprise guide	source	20
297	The Knight's Tour in 3-Dimensional Chess	John R. Gerlach, Scott M. Gerlach	Three dimensional chess typically uses three chess boards such that a chess piece can traverse the several boards according to the rules for that piece. For example, the knight can remain on the board where it resides or move to another successive board, then move in a perpendicular fashion. In three-dimensional chess, the Knight's Tour is a sequence of moves on multiple 8x8 chess boards such that the knight visits each square only once. Thus, for three boards, there would be 192 squares visited only once. The paper, The Knight's Tour in Chess - Implementing a Heuristic Solution (Gerlach 2015), explains a SAS solution for finding such tours on a single chess board, starting from any square. This paper discusses several scenarios and SAS solutions for generating the Knight's Tour using multiple chess boards	macro definition, macro variable	source	18
298	The Mother Of All Metadata Files: Using SAS Macros To Manage Metadata	Hugh G. McCabe	Excellus Blue Cross Blue Shield is a regional health plan in Central and Western New York. We run SAS 9.4 in a grid environment. Our enterprise provisioning and identity management processes are not optimally integrated, so acquiring and managing data on users' access can be challenging. Given our highly sensitive health care data, regulatory and compliance requirements, and the current security climate, effective management and reporting on user access is critical to our success. This paper outlines how we used SAS Institute provided macros, SAS Metadata, SAS Schedule Manager, Unix scripting, human resources information system data, LDAP data, and some crafty programming to manage accounts and generate consistent, comprehensive, and timely reporting on user access to corporate data resources	call missing, missing value, out=, dataset	source	14
299	The Power of a Good SUIT: SASUnit and Integration Testing	Cameron Lawson	Test automation is an integral component of modern software development. Many programming languages and the communities built around them either enjoy integrated test libraries as part of the language's standard library or have adopted a de facto standard such as Python's unit test module or Java JUnit libraries. Many libraries that have been developed or promoted in the SAS community have been adopted to varying degrees. This includes notable projects such as SASUnit and the SAS Operational Quality testing tool. All of these libraries are focused on the programmer and geared toward unit testing approaches. SUIT is a new testing tool offered to the community that not only provides familiar JUnit-style test assertions for the SAS programmer, but also keyword-driven test-case development more suitable for business-focused SAS users and testers to adopt automated testing. With a wide range of out-of-the-box test cases for SAS 9 and Viya, users can quickly develop robust test cases	record count, sas log	source	21
300	The Power of Know-How: Pump Up Your Professional Value by Refining Your SAS Skills	Gina Huff	If you have been programming in SAS for any length of time, you are quite familiar with the slogan, "Power to Know." After more than 15 years of programming in this robust solution, it goes without saying that I am a firm believer in the truth of this statement. Undoubtedly though, there is a second unspoken and critical truth tied to the success of SAS within an organization-the power of SAS is nothing without the users' "Power of Know-How." Developing a strong foundation in programming best practices; writing stable, efficient code; and building automated solutions are all ways in which you can begin to fully appreciate the power of SAS (and make yourself more appealing to employers to boot). There is a difference between having a working knowledge of SAS and being able to really program in SAS	macro definition, sas programmer	source	10
301	The Power of REST API and CLI with SAS Visual Analytics 8.3: Automate Your Tasks with a Batch Job	Rain Kang	Many customers expect batch and programming approaches to manipulate SAS Visual Analytics reports in the areas of report creation, customization, localization, and distribution. SAS provides the power to meet these requirements. In this Paper, we demonstrate how to use the REST API and Command-Line Interface (CLI) with SAS Viya 3.4 to accomplish business tasks with typical scenarios. You will learn how to create or edit Visual Analytics (VA) reports programmatically, how to localize a VA report based on the standard report, how to customize the email content before distributing the reports in batch, how to change the report distribution recipients automatically with a batch job, and finally how to kick off the distribution right after the data is refreshed automatically. This paper will be helpful for both SAS consultant and customers who work in area of SAS Visual Analytics	sas viya, rest api, sample, batch job	source	16
302	The Relationship Between Social Determinants of Health and Healthcare Utilization	Ryan C. Butterfield, Melissa Gottschalk, Laura Soloway, Paul A. LaBrec	Decades of research has documented the effect of various social factors on health status and health outcomes. This research project examines the relationship between three measures of social determinants of health (SDoH) and two measures of avoidable outcomes of care. The SDoH measures include a validated, self-reported survey called AssessMyHealth (AMH), a subset of ICD-10 supplemental Z-codes related to patient social/psychosocial factors from claims data, and 3M's Neighborhood SDoH Scoring tool using publicly -available small area data. Avoidable outcome measures used are 3M's Potentially Preventable Readmissions (PPR) and Potentially Preventable Emergency Room Visits (PPV). The study population is enrollees in a Midwest Medicaid plan. Patients will be stratified according to clinical risk for outcomes using 3M's Clinical Risk Grouper, with covariates including age and sex	sample, insurance claims, scatter plot, logistic regression, regression analysis, predictive probability, feature importance, proc glm, box plot, logistic distribution, fixed effects, proc hpsplit, feature selection, histogram, output delivery system	source	16
303	The SAS Hash Object: Well Beyond Table Lookup	Paul M. Dorfman, Donald J. Henderson	Most applications of the SAS hash object focus on its table lookup facility, mainly to combine data. While it is indeed a great lookup tool, its functionality is much broader: As a data structure, the hash object comes equipped with all common table operations, which means that it can be also used for data aggregation, splitting, unduplication, and so on. And, since these operations are executed in memory in constant time, they tend to outperform other SAS alternatives. Furthermore, the run -time nature of the hash object, its autonomous I/O facility, and its ability to point to other objects make it possible to write dynamic and easily parameterized programs without hard coding. In this paper, we outline the functionality of the hash object well beyond table lookup as a compendium of the book "Data Management Solutions Using SAS Hash Table Operations. A Business Intelligence Case Study " recently published by SAS Press	hash table, sample, data wrangling, hash iterator, composite key, order by, do loop, key, lookup, proc sql, sas log, sorted data, dataset	source	27
304	The Science of Workflow Management: Modeling Business Processes in SAS Simulation Studio	Dave Manet	As managers, we are often asked to ensure that our business processes are optimized. There are many software solutions for simply tracking work. But true process optimization can be attained only by carefully examining inputs and studying how varying those factors influences the outcome. Without specialized tools such as SAS Simulation Studio, obtaining accurate results is very challenging due to the complexity of modern business. In this paper, we use SAS Simulation Studio to conduct experiments of how changes in work volume and resource availability impact process efficiency and capacity of an essential business process. Modeling a process in a simulated environment allows for true A-B testing. The point-and-click interface is easy to use and empowers even novice users to translate real-world scenarios into reliable estimates	sample, group by, data cleaning	source	38
305	The Shape of SAS Code	Charu Shankar	There are many languages that co-exist in the ecosystem of your SAS toolbox. This Hands- On Workshop teaches you how to use four SAS languages - Base SAS, `PROC SQL`, Perl language elements, and the SAS Macro Language - to help you manipulate and investigate your data. Learn to leverage these powerful languages to check your data with simple, yet elegant techniques such as Boolean logic in `PROC SQL`, operators such as the SOUNDS-LIKE operator in the `DATA STEP` and `PROC STEP`, functions such as the SCAN function in the `DATA STEP`, efficient checking of your data with Perl regular expressions, and last but not least, the amazing marriage between `PROC SQL` and the SAS Macro Language to hold data you just found in a variable that you can use over and over again. This workshop focuses on coding techniques for data investigation and manipulation using Base	proc sql, proc step, macro variable, data step	source	14
306	The Transformation of Educational Data Using SAS : Introducing Intervention Analytics!	Sean W. Mulvenon	A goal of improved student outcomes, requires intervention analytics. The last few decades have seen a tremendous growth in the depth and clarity of the role of big data in education, including a greater emphasis on using analytics to improve student outcomes. Yet despite the genesis and transition from data-driven decision-making to actionable data to visual analytics, has there really been a systemic transformation in the effective use of analytics in education? This paper introduces the use of intervention analytics to improve the educational outcomes for all students. Key to the process is reliance on the single- source SAS model that transforms data, integrates an innovative analytics model, identifies unique educational interventions, and transitions information to web-based platforms	likelihood function, raw data, proc step	source	10
307	The Wondrous New tmCooccur SAS Cloud Analytic Services (CAS) Action and Some of Its Many Uses	James A. Cox, Russell Albright	The new tmCooccur action in SAS Viya 3.4 detects significant co-occurrences of terms in sentences. It can identify patterns and nuances such as the following: 1) the same word used with different meanings (for example, "They met along the bank of a river" versus "They made a deposit in a bank"); 2) verbs used as regular verbs versus verb -particle combinations (for example, " shut the heck up" versus "shut the door please"); and 3) less common uses of negation, such as statements like "He liked the show very much" versus "He liked the show not at all". This paper descr ibes how the results of the tmCooccur action can be used to generate word embeddings on the basis of local information (in a manner similar to GloVe and Word2Vec), and also how it can be used to create virtual terms that represent the most significant term co-occurrences. These new composite terms can then be used to create better topic, concept, and category definitions	proc freq, correlation, proc princomp	source	13
308	There's a Map for That! What's New and Coming Soon in SAS Mapping Technologies	Jeffrey Phillips, Tony Graham, Scott Hicks	The last few years have been an exciting time for the map geeks at SAS. We've added a lot of geospatial functionality throughout SAS, and we can't wait to show it off. This paper goes over all the additions, including GeoEnrichment, geocoding, the `SGMAP` procedure, contours, WebMap support, routing, location pins, clustering, and custom polygon support. We also show you the enhancements in progress and detail the roadmap for the future	sas studio, proc step, sample, sas base	source	31
309	Thirteen Statistics Every Biostatistician Should Know	AnnMaria De Mars	No one knows all of SAS or all of statistics. There will always be some technique that you don't know. However, there are a few techniques that anyone in biostatistics should know. Make your life easier by learning to calculate those with SAS. In this session you will learn how to compute and interpret 12 of these techniques, including several statistics that are frequently confused. The following statistics are covered: prevalence, incidence, sensitivity, specificity, attributable fraction, population attributable fraction, risk difference, relative risk, odds ratio, Fisher's exact test, number needed to treat, and McNemar's test. The 13th, extra bonus tool, is SAS statistical graphics. With these 13 tools in their tool chest, even non-statisticians or statisticians who are not specialists will be able to answer many common questions in biostatistics. You're in luck because each of these can be computed with a few statements in SAS	proc freq, proc step, relative risk, odds ratio, fisher exact test, exact test, sample, proc sgplot, confidence interval, proc transpose	source	20
310	Three Challenges using SAS Customer Intelligence Studio for Effective Campaigning	Al Cordoba, Pratyush Gupta	In this paper, we describe how to use SAS Customer Intelligence (SAS CI) for campaign execution, post-campaign reporting and how to avoid three most commonly found campaign execution hurdles. SAS CI is an integrated Marketing Management Suite. It combines Multichannel Campaign Management, Marketing Resource Management and Marketing Performance Management with strong Data Management and a wide range of analytic capabilities. Using an example of a monthly summary campaign, this paper helps understand SAS CI, what it is, what its possibilities are, and where it can be used. We present three challenges to address using SAS CI in the hospitality industry: 1. Generating an output file with one row per guest with more than one offer, 2. Creation of unique Marketing History ID with respect to the offer and the language of the email at the time of execution of the diagram and 3. Creation of customer output file containing the order information for a specific order		source	10
311	Three ways to add texts to graphics in Proc Sgplot	Yaqi Jia	The procedure `PROC SGPLOT` in Base SAS is a powerful procedure to create various graphics. We can customize graphics adding features such as color, pattern, and size, within each plot statement. However, sometimes graphics are not enough. We may wish to add texts to graphics. For example, adding correlation coefficient 'R = 'in a scatter plot helps demonstrate how the y axis variable is correlated with the x axis variable. Or, we want to add true odds ratio (95% CL) results beside their forest plots. Base SAS version 9.4 provides multiple ways to accomplish this goal. This paper will discuss three powerful and easy-to-learn ways to add texts to the graphics using special statements and options in the procedure `PROC SGPLOT`: INSET statement, TEXT statement, and the optional SGANNO=anno dataset in `PROC SGPLOT` statement	proc sgplot, proc step, scatter plot, sg annotation, forest plot, sas base, dataset, correlation	source	9
312	Time Travel into the Future of Clinical Trial Enrollment Design	Bahar Biller, Anup Mokashi, Ivan Oliveira, Sharmin Pathan, Jinxin Yi, Jim Box	How might the clinical trial that you design today perform in the future? Will your design enable you to reach the target patient enrollment fast enough while staying within budget? This paper shows how SAS Clinical Trial Enrollment Simulator, built on SAS Optimization software and integrated with machine learning, gives pharmaceutical companies and clinical research organizations the power to predict the future performance of their clinical trial enrollment designs in real time. Furthermore, combined use of simulation, machine learning, and optimization creates the option to deploy enrollment simulations in real-time analytical portals. A numerical example is used to describe the development of a clinical trial enrollment process simulation and to outline the steps of using output data generated from thi s simulation to instantaneously predict patient enrollment for any given scenario and to recommend site activations to deliver target enrollment with high confidence	clinical trial	source	17
313	Tips and Tricks for Building Business Applications with SAS Visual Investigator	Gordon Robinson	SAS Visual Investigator provides users with the flexibility to build applications that are customized to their business needs. This flexibility includes the ability to define how data is analyzed to generate alerts, how data is indexed to enable users to search, how data is presented to the users, what data users can enter into the system, and what workflows are in place with respect to data entry. As a result, SAS Visual Investigator can be used for applications in many different markets. This paper covers some of the best practices for building applications on top of SAS Visual Investigator to ensure maximum performance and utilization of available resources	foreign key, sas viya	source	11
314	Tips to Ensure Success in Your New SAS Project	Flora Fang Liu	Presentation		source	8
315	Tips, Traps, and Techniques in BASE SAS for vertically combining SAS data sets	Jayanth Iyengar	Although not as frequent as merging, a data manipulation task which SAS programmers are required to perform is vertically combining SAS data sets. The SAS system provides multiple techniques for appending SAS data sets, which is otherwise known as concatenating, or stacking. There are pitfalls and adverse data quality consequences for using traditional approaches to appending data sets. There are also efficiency implications with using different methods to append SAS data files. In this paper, with practical examples, I examine the technical procedures that are necessary to follow to prepare data to be appended. I also compare different methods that are available in BASE SAS to append SAS data sets, based on efficiency criteria	proc step, proc append, proc sql, set statement, proc compare, proc contents, sas base, cpu time, dataset, sas log, proc freq, sas programmer, data cleaning, proc datasets, length statement	source	18
316	To Buy or to Create? Determining the Best Option for Student Success Models	Stephanie R. Thompson	The number of vendors providing solutions for predicting student success keeps increasing. Each vendor claims to be able to use your data and to predict the likelihood of retention or graduation for you. This all comes with a financial cost and required time and effort to prepare and update data to feed their systems. Is creating an in-house system a better solution? This paper walks through one way to make that determination. If your institution has gone through changes impacting the continuity of data or has made other significant structural changes, finding an off-the-shelf solution can be more challenging. Should you include additional variables that the commercial products do not consider? A side benefit of creating the model in-house is validating multiple models on past students as well as working with those who will be using the model output to generate buy-in. Rolling out a commercial solution is of no benefit if those using it do not trust the results	data cleaning, data wrangling, missing value, data mining, score code	source	6
317	Top SAS Architecture Pitfalls: Lessons Learned the Hard Way	Margaret A. Crevar	Are you a SAS infrastructure architect? Do you want to know what has worked for other SAS customers and what things should be avoided? Attend this discussion to learn from others' experiences with SAS. We also encourage you to share your experiences with us	sas viya, raw data	source	3
318	Tracking and Consolidating Chinese Vaccine Source Information	Yizhe Ge, Tracy Song-Brink	Presentation		source	4
319	Transforming Ordinal Predictors to Numeric For Binary Logistic Models	Bruce S. Lund	In binary logistic regressions for credit risk and marketing applications as well as for many social science studies there are ordinal predictors that are considered for use in the model. The modeler faces the question of how to utilize these ordinal predictors. The choices include conversion to dummy variable s, recoding as weight of evidence, or assuming an interval scale for the ordinal levels and entering the effect as linear (or possibly as some transformation). The use of dummies or weight of evidence add parameters to the model (for weight of evidence the added parameters are implicit in the recoding). This paper provides a procedure to aid in the decision making regarding the handling of ordinal predictors. First, a measure is computed of the monotonic tendency of the ordinal versus the target (the dependent variable). If the ordinal is nearly monotonic, then a simple numeric recoding of the ordinal is made. This imposes an interval scale	logistic distribution, proc logistic, logistic regression, spearman correlation, proc step, correlation, degrees of freedom, regression analysis, sample, binary variable, credit risk, likelihood function	source	10
320	Twenty Ways to Run Your SAS Program Faster and Use Less Space	Stephen B. Sloan	When running SAS programs that use large amounts of data or have complicated algorithms, we often are frustrated by the amount of time it takes for the programs to run and by the large amount of space required for the program to run to completion. Even experienced SAS programmers sometimes run into this situation, perhaps through the need to produce results quickly, through a change in the data source, through inheriting someone else's programs, or for some other reason. This paper outlines twenty techniques that can reduce the time and space required for a program without requiring an extended period of time for the modifications	proc step, dataset, proc sort, numeric variable, by group processing, proc means, character variable	source	6
321	Under One Umbrella: Single Sign-On with SAS 9.4 and SAS Viya	Mike Roda	Although organizations are increasingly leveraging SAS Viya, many have substantial investment in SAS 9.4 applications and will for some time. This paper looks at a new feature introduced in SAS Viya 3.4 that provides single sign-on with SAS 9.4, enabling users to m ove seamlessly between web applications in both environments without having to log on again, and making it possible to use them collaboratively (for example, displaying a SAS Visual Analytics report from SAS Viya on the SAS Information Delivery Portal)	sas viya, sas management console	source	12
322	Understanding, Tracking, and Visualizing Decisions with SAS Viya	Carl Sommer	SAS Decision Manager on SAS Viya provides more than just decision results; it also provides insight into how a decision was made. Macros are available for use with SAS Decision Manager 5.2, which enable the SAS Studio user to work with information about which decision paths were taken as well as what rules were fired, thus providing organizations with analytical insights to revise their business' decision-making processes with a precision not available with many other tools. This paper shows how to use these macros along with SAS Cloud Analytic Services (CAS) to understand, track, and visualize the behavior of their organization's decisions	order by, macro loop, sas studio, proc freq, sas viya	source	13
323	Unleashing SAS Visual Data Mining and Machine Learning Models	Shawn Pecze, Prasanth Kanakadandi, Byron Biggs, Xin Chi	This paper shows how you can use SAS Visual Data Mining and Machine Learning and other SAS products to build and compare various predictive models. First, you use SAS Visual Data Mining and Machine Learning to create several models and you choose one of them as your champion model. You can publish all these models to different destination types such as Hadoop, Teradata, SAS Cloud Analytic Services (CAS), and SAS Micro Analytic Service. You use SAS Embedded Process to score the data against these published models where the data reside. You can also register the models to SAS Model Manager and compare them against other models for final champion model selection. You can then test these models to validate them for scoring. If you notice a degradation in the model, you can retrain the model	machine learning	source	19
324	Unlocking Your Data with SAS/ACCESS Interface to Salesforce	Kevin Kantesaria, Mason Morris	Are you a SAS programmer searching for insights within Salesforce data? Accessing and analyzing your information is now easier than ever before with the all-new `SAS/ACCESS` Interface to Salesforce. This paper provides you with an overview of how to explore Salesforce objects as if they were native SAS data sets. Learn how to directly execute the SOQL language used by Salesforce by using the `SQL` procedure, and take advantage of the advanced features of implicit SQL to let SAS do some of the work for you. With the help of `SAS/ACCESS` Interface to Salesforce, you'll begin to understand your data in no time! Figure 1: Administrative Permisssions for a User Profile If your administrator has enabled two-factor authentication, you will also need a "security token" to connect. When making a `LIBNAME` connection in SAS, you will need to append this string of characters directly to the end of your password. You cannot view your security token from the dashboard	proc sql, proc step	source	13
325	Unsupervised Learning for Anomaly Detection in Securities Data	Laura Rudolphi, Chris A. Robinson	An all-too-common practice in data analytics is the use of pre-defined business groupings to drive analyses and subsequent decision-making. Product categories drive assortment layouts in retail, but are they really indicative of what shoppers buy at the same time? Geographies define divisions for sales-oriented companies, but do customers in neighboring states really behave the same? In the Compliance Technology & Analytics group at Wells Fargo, account types from an existing vendor solution were driving the peer-to-peer comparisons for the anti-money laundering (AML) transaction program that covers our securities business. Accounts of the same type, or peer group, are compared to one another to identify anomalies and "out-of-bounds" behavior for AML alerting. However, not all accounts in the same peer group are created equal, resulting in sub-optimal comparisons and less valuable alerts for our investigative team	feature selection, outlier, sas base, data mining, transaction monitoring	source	7
326	Use advantage of INDEXes even if WHERE clause contains OR condition	Bartosz Jabłoński	The advantage of an indexed Base SAS engine's dataset is that when the dataset is queried with a WHERE clause the process may be optimized and index-subsetting will work faster than a sequential read. Unfortunately the Base SAS engine is unable to use more than one index at a time to op- timize a WHERE clause. Especially if a WHERE clause contains an OR condition between two different (indexed) variables the Base SAS engine won't optimize it and will execute a se- quential read. The main idea of this paper is to offer a solution to the following question: How to handle a situation in which we have a WHERE clause with an OR condition between two dif- ferent indexed variables and want to use the advantage of indexing to subset the data faster? The solution is datastep based and uses hash-tables. But foremost it is both simple to implement and efficient	where statement, cpu time, data step, proc step, sas log, sas base, proc sql, temporary array	source	12
327	Using a File Data Source Name to Centralize Data Connection Management in DataFlux Data Management Studio	André Harper, Marc Price, Tom Gaughan, Matt Hall, Michael Bretz	The United States Census Bureau (USCB) Demographic Systems Division (DSD, collectively USCB-DSD) utilizes Dataflux Data Management Studio and DataFlux Data Management Server for data management and cleansing. USCB-DSD accesses DataFlux Data Management Studio via a Microsoft Windows virtual desktop (vDESK) where users can perform data management tasks unique to their individual program area. Due to USCB security restrictions, users do not have access to the local Windows program files where the DataFlux Data Management Studio configuration is stored by default. Users also do not have the capability to establish a System Data Source Name (DSN) as required to make the connection to the repository. To solve these problems, USCB-DSD implemented a shared repository consisting of an Oracle database management system (DBMS) with the DataFlux data connection configuration saved on a shared file system and user credentials saved in the SAS metadata	odbc connection, sas administrator	source	11
328	Using Cost-Sensitive Predictive Modeling in Digital Marketing	Romana Sipoldova	In the present fast -moving times, when real -time decisions are needed, data quality is highly appreciated as high -quality predictive models are created based on it. Nowadays, analytics is turning into a mature, value-adding technology. A special benefit is considering future costs and revenues. In my final thesis work, which was f inalized and submitted in March 2019, I pointed out the issue around increasingly evolving digital marketing to optimize the example online campaign. The aim of my project is to describe the procedure of the application of a strategy that is sensitive to p rofit/loss, and to compare the models of standard logistic regression and weighted logistic regression with the application of such a strategy. When perceiving the goal of a selected digital campaign from the advertiser's point of view, achieving the best click-through rate (CTR), the media agency must also consider the potential costs of cost per click (CPC) or the most effective profit	logistic regression, regression analysis, logistic distribution, response variable, proc freq	source	7
329	Using Custom, User-Defined SAS Formats with SAS Visual Analytics on SAS Viya	Andrew T. Fagan	Are formats associated with your data? Formats that you created in SAS using the `FORMAT` procedure? If you want to use SAS Visual Analytics on SAS Viya with your data, you need to load your formats so that they can be found. In SAS9, this meant adding the catalog that contained your formats to the format search path. On SAS Viya, there is an analogous task but the process is different. First, SAS Viya cannot read a SAS catalog, so you need to convert your catalog to a structure that SAS Viya understands. Next, you need to place the formats in a location where SAS Viya can find them. And finally, you need to tell SAS Viya to load the formats whenever a new session is started. This paper explores how you, as a user of SAS Visual Analytics on SAS Viya, can accomplish these steps and make your formats available. You can do this by using SAS Environment Manager, SAS Studio, or shell scripts	sas viya, sas studio, proc format, sas 9, proc step, catalog	source	24
330	Using ETS Software for Intervention Models for Sales of Cars	Anders Milhøj	Cars in Denmark are heavily taxed, and the level of taxation is changed from time to time. In recent years, first an extra tax was introduced in order to reduce fuel usage, and two years later a new tax was introduced in order to make more secure cars affordable. The first intervention was in favor of small cars, while the second was in favor of large cars. Whether such changes in the taxation rate were introduced immediately or were announced some months before makes a difference in their impact. Also, changes in taxation are expected by everyone interested in the negotiations in parliament. Statistics Denmark publishes the number of cars sold in many segments. In this paper, the car segments small, medium, and large are used in order to illustrate the effect of these changes in taxation	time series analysis, standard deviation, outlier, binary variable	source	8
331	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`) `EXCEL` destination, 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
332	Using Parallel Analytics to Determine the Dimensionality of the Computer-Based Prostate Cancer Screen	Abbas S. Tavakoli, Otis L. Owens, Nikki R. Wooten	Prostate cancer (PrCA) incidence and mortality rates are higher among African-American men than any other racial group. Informed decision making about prostate cancer screening could result in early detection and potentially reduce cancer health disparities. Currently, there are some, but few, computer-based decision aids to facilitate PrCA decisions of African-American men, but no scale has been validated to assess the extent to which African-American men will accept and use a computer-based PrCA screening decision aid. Using parallel analysis, this study determined the dimensionality of the Computer-Based Prostate Cancer Screening Decision Aid and Acceptance Scale using data from a purposive sample of 352 African-American men aged 40 years and older who resided in South Carolina. Exploratory factor analysis was conducted using maximum likelihood, squared multiple correlations, and Promax rotation. Internal consistency reliability was assessed using Cronbach's alpha	correlation, standard deviation, proc freq, pearson correlation, factor loading, scree plot, factor analysis, sample, proc step	source	10
333	Using Proc Optigraph to Implement the Prize Collecting Traveling Salesman Problem in SAS	Bryan Yockey, Joe DeMaio	The classic Traveling Salesman Problem (TSP) establishes a list of cities to visit and costs associated with travel to each location. The goal is to produce a cycle of minimum cost that visits each city and returns the salesperson to her home location. What happens if an imposed time limit on the journey makes visiting all locations impossible? Assuming each location is of equal value then the goal transforms into visiting as many locations as possible within the imposed time limit. This variation is known as the Prize Collecting Traveling Salesman Problem (PCTSP). We use `PROC OPTGRAPH` to implement the TSP, develop an approach to implement the PCTSP and utilize SAS VIYA to map results. Set in a suburb of Metropolitan Atlanta, our motivation stems from the need to acquire as many virtual Pokémon as possible for Alice, the nine-year old daughter of one of the authors. Analysis of results from executing SAS generated routes is included	proc step, data science	source	14
334	Using PROC SQL to Generate Shift Tables More Efficiently	Jenna Cody	Shift tables display the change in the frequency of subjects across specified categories from baseline to post-baseline time points. They are commonly used in clinical data to display the shift in the values of laboratory parameters, ECG interpretations, or other ordinal variables of interest across visits. The "into:" statement in `PROC SQL` can be used to create macro variables for the denominators used in these tables. These macro variables can be accessed throughout the program, allowing for easy computation of percentages and the ability to call the same macro variable to display the subject count value in the header. This paper outlines the steps for creating a shift table using an example with dummy data	shift table, proc step, proc sql, macro variable, proc freq, by group processing, call symput, proc transpose, clinical data, select	source	9
335	Using SAS at SAS: Content Targeting Segmentation Strategy with SAS Customer Intelligence 360	Mark Korey	Website tracking generates a mountain of data loaded with gems waiting to be discovered. Analyzing this data uncovers untold stories and insights - when, why, how do people visit your website? But that's only the beginning of the story. By combining front-end data with back-end data, beautiful nuggets of knowledge emerge. Uniting the unique visitor identification capabilities of SAS Customer Intelligence 360 with customer relationship management (CRM) data empowers you to know not only when, why, and how people use your website, but also who is using it. You can see and predict how different segments of individuals behave. In turn, CRM data fed back into SAS Customer Intelligence 360 enables you to target each segment with increasingly relevant content. This paper delves into how marketing technologists behind sas.com integrate abilities of SAS Customer Intelligence 360, SAS Enterprise Guide, and SAS Visual Analytics to generate golden insights	sas enterprise guide, bubble plot, web analytics, sample	source	20
336	Using SAS Enterprise Guide and SAS Visual Analytics to Model a Large Institutional Data Set	Pratik Patel, Maham Khan, Varsha Sharma, Shreyas Dalvi, Shabnam Mehra	Insights and understanding of factors associated with student success is of great significance among higher education universities and the prospective student population including their parents. Prospective students are interested in learning about completion rates and their financial well-being after graduation in the form of expected earnings across universities. Various SAS procedures in Enterprise Guide software were used to import, merge, clean, and analyze a big dataset of universities and colleges collected by over a period of 10 years. The study uses publicly available College Scorecard data to study the factors associated with student success and financial well-being. SAS Visual Analytics software was used in this study to visualize and present the findings of the study. In this study we find a few institutional characteristics to be strongly related to both student success and financial well-being	proc step, proc genmod, sas enterprise guide, excel	source	12
337	Using SAS Forecast Server and the SASEFRED Engine to Enhance Your Forecast	Catherine LaChapelle	The SASEFRED interface engine is the best-kept secret in `SAS/ETS` software. It dramatically reduces the amount of time and effort required to include economic indicator variables in your time series analysis. Using the SASEFRED engine in SAS Enterprise Guide, I can directly query the economic database of the Federal Reserve Bank of St. Louis. This public database contains over 529,000 economic time series aggregated from 86 sources. In this paper, I forecast wine demand and enrich my predictions via the inclusion of economic variables such as "Retail Sales: Beer, Wine, and Liquor Stores" and "Producer Price Index by Industry: Beer, Wine, and Liquor Stores." Although I use a retail example, this technology is relevant to all industries. The diversity of economic variables provided in this database ensures that it is useful to virtually every time series analysis and industry. This specific example leverages SAS Enterprise Guide and SAS Forecast Server as interfaces	time series analysis, predictor variable, sas enterprise guide, proc freq, sample, feature selection	source	7
338	Using SAS Graph Template Language to Demonstrate Foundational Statistical Concepts	Aaron Myers	Presentation	graph template language	source	6
339	Using SAS Job Monitor to Analyze SAS Data Integration Studio Job Performance	Jeff Dyson	Obtaining run-time statistics and performance information from SAS Data Integration Studio batch processes has historically been a challenge. And with growing source populations, tight batch windows, and the demand for SAS analytics and predictive model results, being aware of negative performance trends in the essential data management layer is as important as ever. With SAS Job Monitor, the ETL (extract, transform, and load) administrator can take advantage of an integrated set of components that provides a more complete view of job load status, historical run-time trends, and other audit related information	sas log, sas data integration studio, sas management console	source	17
340	Using SAS MACRO to Demonstrate Data Presentation Standards for Proportions	Mary Ann Bush, Nazik Elgaddal	This paper describes a method to apply the National Center for Health Statistics' (NCHS) data presentation standards for reporting proportions in NCHS reports and data products. This method was developed by the Data Suppression Workgroup at NCHS. Standards are particularly important for large, cross-cutting reports where estimates cannot be individually evaluated and indicators of precision cannot be included alongside the estimates. A SAS MACRO, with code for direct computation based on mathematical formulae, is used to app ly the multistep data presentation standards based on minimum denominator sample size and on the absolute and relative widths of a Confidence Interval (CI) calculated using the Clopper-Pearson method, or the Korn and Graubard method for complex surveys. The MACRO provides a clear, efficient, and transferrable method to implement the SAS custom code	sample, sample size, degrees of freedom, confidence interval, macro definition, proc calis, macro variable	source	12
341	Using SAS Macros to Analyze Lifetime Data with Left Truncation	Zhen-Huan Hu	Like right censoring, left truncation is commonly observed in lifetime data, which requires proper technique to analyze in order to achieve unbiased results. This paper introduces how to analyze such left-truncated lifetime data using the native `PHREG` procedure, including generating survival estimates, testing homogeneity across samples, as well as generating survival plots. Alternatively, the paper demonstrates two new macro functions, namely %LT_LIFETEST and %LT_ LOGRANKTEST, to conduct survival analysis with more flexibility for data with left truncation, such as enabling users to choose specific estimation time points and to perform weighted log-rank tests. The paper also provides updates on the previously presented %LIFETEST and %LIFETESTEXPORT macro functions for generating formatted survival analysis reports with added support for left-truncated data	proc phreg, overall survival, log-rank test, survival analysis, kaplan-meier plot, rank test, macro function, right censoring, univariate analysis, null hypothesis, proc step, regression analysis	source	11
342	Using SAS Procedures to Fit Rasch Models	Tianshu Pan, Yumin Chen	This article goes over the SAS procedures which can fit the Rasch model, such as the IRT, LOGISTIC, GENMOD, GLIMMIX, NLMIXED, NLIN and MCMC procedures, and describes how to use them to fit the standard Rasch model and the Rasch model with all person or item parameters fixed	estimate, logistic distribution, logistic regression, proc logistic, regression analysis, proc glimmix, binary variable, proc genmod, proc nlin, likelihood function, maximum likelihood estimation, proc mixed, proc step, response variable	source	10
343	Using SAS to Analyze ICD9 and ICD10 Codes	Kathy Hardis Fraeman	Administrative health-care data - including insurance claims data, electronic medical records (EMR) data, and hospitalization data - contains standardized diagnosis codes to identify diseases and other medical conditions. These codes use the short-form name of ICD, which stands for International Classification of Diseases. Much of the currently available health-care data contains the ninth version of these codes, referred to as ICD-9. Although, the more recent 10th version, ICD-10, is becoming more common in health-care data. These diagnosis codes are typically saved as character variables, are often stored in arrays of multiple codes representing primary and secondary diagnoses and can be associated with either outpatient medical visits or inpatient hospitalizations. SAS text processing functions, array processing, and the SAS colon modifier can be used to analy ze the text of these codes and to identify similar codes or ranges of ICD codes	colon modifier, dataset, array, macro put, insurance claims, macro variable, excel, character variable, sas programming	source	11
344	Using SAS to Validate Prediction Models	Xiaoting Wu, Chang He, Donald S. Likosky	Model validation is an important step in establishing a prediction model. A model validation process quantifies how well the model predicts future outcomes. However, there are very few SAS programming examples showing the validation process. We previously developed a generalized mixed effect model that predicts peri-operative blood transfusion from patients' characteristics. In this paper, we demonstrate a number of SAS techniques that we used to validate such a model. This prediction model was developed using the `GLIMMIX` Procedure. The validation methods include calibration using `SGPLOT`, discrimination using the ROC statement in the `LOGISTIC` Procedure, and sensitivity analysis with a bootstrapping	bootstrap, model validation, data cleaning, sample, sensitivity analysis, proc glimmix, roc curve, proc step, proc logistic, logistic distribution, macro definition	source	9
345	Using SAS Visual Analytics for Displaying Local Currency Symbol (Rupee ₹) for Indian Locale	Kalind Patel, Avinesh Selvaraj	We are frequently asked if we can have local currency symbol in SAS Visual Analytic s Reports, Customers want the locale currency symbol displayed behind their data so they can see where the exact values are located. Unfortunately, by default SAS has limi tations on the displaying currency symbols based on Locales' which is currently available on SAS. This paper will be discussing the problems and limitations with current features an example of using the new feature. In addition to that, this paper provides an information how we can overcome the limitations on the displaying the India currency symbol in SAS Visual Analytics' Report Viewer		source	9
346	Using SAS Viya Textual Analytics to Identify and Predict Human Trafficking Attractors and Movement	Lauren Ann Agrigento, Nicholas Lacoste	Presentation	sas viya, machine learning	source	4
347	Using SAS Viya to Implement Custom SAS Analytics in Python; A Cybersecurity Example	Damian Herrick	This session is designed for data scientists looking to develop custom production analytics solutions backed by SAS analytic capabilities. First, we will describe an interactive exploration and discovery environment based on the SAS Viya/ SAS Cloud Analytic Services (CAS) architectural foundation. Next, using cybersecurity as an example, we will demonstrate how SAS analytics can be implemented in Python in a production implementation of the environment	cas, sample, data science, sas viya	source	12
348	Using Simulation Games to Teach Analytics	Jean-François Plante, Pierre-Majorique Léger, Jean-François Michon, Forough Karimi-Alaghehband, Marc Fredette, Michael Bliemel	Literature shows that gamification and experiential learning improve the attitude, retention and engagement of students. We describe and showcase a simulation game developed in the field of analytics that uses SAS Enterprise Miner. The game scenario puts the students in charge of targeted marketing actions for the fundraising campaign of a charitable foundation. Students have to decide which members to contact for a possible donation based on data from one million members. The game is designed to help participants apply and consolidate different data analytics concepts in a realistic setting. It provides pedagogical flexibility so that it could be used in classes of different levels (e.g., UG, MBA) and with students with varying levels of statistical knowledge and competency	data mining, proc hpsplit	source	7
349	Using the FORMAT Procedure to Automate the Data Correction Process	Shreyas Dalvi	Presentation	proc format	source	7
350	Using the R interface in SAS to Call R Functions and Transfer Data	Bruce F. Gilsen	Starting in SAS 9.3, the R interface enables SAS users on Windows and Linux who license `SAS/IML` software to call R functions and transfer data between SAS and R from within SAS. Potential users include `SAS/IML` users and other SAS users who can use `PROC IML` just as a wrapper to transfer data between SAS and R and call R functions. This paper provides a basic introduction and some simple examples. The focus is on SAS users who are not `PROC IML` users, but who want to take advantage of the R interface	proc step, dataset, proc iml, sysdate9, proc options, missing value, estimate, variable, date variable, options statement, work library, regression analysis, proc reg	source	17
351	Using the SAS Object Spawner Log to Identify Issues More Quickly and Avoid Downtime	Jessica Franklin	Have you ever wondered why the log file is one of the first items that SAS Technical Support requests? The reason is that the log file contains a wealth of information about what actually occurs in the application or platform. So, what exactly is Technical Support looking for? How can the SAS Object Spawner log help pinpoint the underlying issue? By reading this paper, SAS administrators will gain insight into how to use the object-spawner log to identify issues more quickly and avoid downtime. The focus of this paper is on the object-spawner log, because the object spawner is the gateway to other application servers. The paper provides an overview of the object-spawner log, describes useful loggers that you should be aware of, details how to identify common issues by looking in the logs, and offers solutions for those issues. With this knowledge, you can be proactive in solving problems that arise	sas management console, error message, sas enterprise guide, note	source	21
352	Utilization of Python in Clinical Study by SASPy	Yuichi Nakajima	Python is the one of the most popular programming languages in recent years. It is now getting used in machine learning and AI. Big advantage of Python is that plenty of Python libraries to implement various analysis and it can be a "one stop shop" for programming. Although SAS is and will be the most powerful analytical tool in clinical study, Python will expand reporting activity such as data aggregation and visualization in addition to SAS, and also it can be potential advancement of SAS programmer's career. SASPy is the new ticket to Python for SAS programmers. It is known as Python Package library to the SAS system and enables to start SAS session in Python. This means Python can support the activities from SAS data handling to reporting. This paper describes basic usage of SASPy and introduction of tips for handling SA S dataset. Then, discuss possible reporting activities using Python in clinical study	dataset, clinical trial, sas programmer, data cleaning, excel, proc means, data review	source	15
353	Visualizing Hospital Readmission rates: How Well Does your Hospital Perform	Andrea Barbo	Presentation: Hospital readmission is an adverse but often preventable event that has been shown to be related to a hospital's quality of care (Frankl et al., 1991). To reduce unplanned 30-day readmission rates among Medicare beneficiaries, the Centers for Medicare & Medicaid Services introduced measures of risk-standardized readmission rates (RSRRs) in both its quality reporting and pay for performance programs. RSRRs are calculated for five medical conditions, two procedures, and one hospital-wide measure for participating hospitals. These results are publicly reported on Hospital Compare, an online tool that patients can use to guide their decisions about where to seek care. Radar plots provide a tool that can illuminate differences and similarities of rates within and across hospitals. This can be useful for patients seeking the best possible care, or for hospital quality departments trying to understand their hospital's performance compared to their peers	proc sgpanel, proc step	source	17
354	Want Clean Distribution and Email? A Reporting Dream	Ryan Bixler, Travis Himes	Presentation	excel, macro definition, dataset, macro variable, sas enterprise guide	source	17
355	Welcome to the Three Ring %CIRCOS: An Example of Creating a Circular Graph without a Polar Axis	Jeffrey P. Meyers	An internal graphics challenge between SAS and R programmers within Mayo Clinic led to the creation of a CIRCOS plot with the SAS 9.4M3 features. A CIRCOS plot is a circular visualization of the relationship between objects, positions and other time-point related data. The CIRCOS plot features a curved rectangle for each node or object around the perimeter of the circle, and a Bézier curve for each path between nodes that is pulled towards the center of the circle. Each rectangle and curve's size is determined by their proportion of the total population. The primary challenge of creating this plot with the current SAS SG procedures is that these procedures do not include polar axes to simplify plotting circular or curved objects. The macro %CIRCOS is an example of overcoming these limitations creatively using trigonometry to prove that these types of graphs are still possible without polar axes	macro variable, do loop, proc sql, order by	source	17
356	What is CASL?	Steven Sober	This is an introductory paper to help you understand why one would want to adopt the programming language CASL. SAS Cloud Analytic Services language (CASL) is a language specification used by SAS and other clients to interact with SAS Cloud Analytic Services (CAS). CASL is a statement-based scripting language that supports the entire analytical life cycle (data management, analytics, and scoring), as well as the monitoring of the CAS Server. The strength of this language can be seen in how easy it is to initialize parameters to CAS actions and to manipulate the results of these actions. The results can then be used to prepare the parameters for execution of another action. The goal is to provide an environment that enables a user to string together multiple actions to accomplish a specific result. CASL provides access to popular features in SAS including Base SAS in-database procedures, `DATA STEP`, DS2, FedSQL, formats, and Output Delivery System (`ODS`)	output delivery system, proc step, sas log, sas output, if-then-else, association rules, score code	source	16
357	What's New for Report Localization in SAS Visual Analytics 8.3	Elizabeth Bales, James Holman	Report localization was first introduced in SAS Visual Analytics Designer 7.1. This feature made it easy for a user or a language specialist to translate a single report into multiple languages for report users. Many SAS sample reports and customer reports have been translated using the 7.x releases of SAS Visual Analytics Designer. However, a user had to have a report-designing role to translate report content, which meant that the design of a report could be altered during localization. SAS Visual Analytics 8.3 is the first release based on SAS Viya that lets you localize reports. Starting in the 8.3 release, report content can be translated without the possibility of altering your report design. There is a new set of command-line interfaces (CLIs) in SAS Viya. The SAS reports CLI enables you to localize report content without requiring the report to be open for editing in SAS Visual Analytics	sas viya, sample	source	12
358	What's New in FCMP for SAS 9.4 and SAS Viya	Michael Whitcher, Aaron Mays, Bill McNeill, Andrew Henrick, Stacey Christian	Join us as we explore new features and functionality in the SAS function compiler (FCMP). Integration with Python, support for running analytic scoring containers (ASTORE), and a new FCMP action set are the main topics we will cover. Learn how to leverage your existing investment in Python by calling Python functions from an FCMP function. Get the most from your ASTORE by porting it from SAS Viya to SAS 9.4 (TS1M6), and then run it from an FCMP function called from the SAS `DATA STEP`. Learn how to port your favorite user-written functions and subroutines to SAS Cloud Analytic Services, and then use them within a computed column or another action. This paper will show you the tips and tricks you need to integrate your existing FCMP code with these new SAS technologies. Included are several examples to quickly get you started	sas viya	source	20
359	What's the Difference? - Comparing SAS Artifacts After a Migration or Upgrade	Brian K. Varney	There are times when it is convenient to be able to compare two file system areas that are supposed to be equivalent. This could be during a migration activity involving changing servers and/or SAS versions. This paper intends to cover techniques that can help facilitate the comparison of programs, logs, `EXCEL` files, and SAS data sets as well as techniques for digesting the results	data cleaning, excel	source	9
360	Working with Big Data in SAS	Mark L. Jordan	This paper demonstrates the challenges you might face and their solutions when you use SAS to process large data sets. First, we demonstrate how to use SAS system options to program query efficiency. Next, we tune SAS programs to improve big data performance, modify SQL queries to maximize implicit pass-through, and re-architect processes to improve performance. Along the way, we identify situations in which data precision might be degraded, and then leverage FedSQL and DS2 to conduct full-precision calculations on a wide variety of ANSI data types. Finally, we look at boosting speed by using parallel processing techniques. We begin by using DS2 in Base SAS to speed up CPU bound processes, and then kick it up a notch with the SAS In-Database Code Accelerator for in- database processing on massively parallel processing (MPP) platforms like Teradata and Apache Hadoop	proc sql, libname engine, proc step, parallel processing, cpu time, sas base, sas log, in-database processing, drop statement, character functions, options statement, group by, if-then-else, sas library, sas viya, where statement	source	14
361	Working with ODS Trace Statement and ODS Output Objects	Louise S. Hadden	SAS procedures can convey an enormous amount of information - sometimes more information than is needed. Most SAS procedures generate `ODS` objects behind the scenes. SAS uses these objects with style templates that have custom buckets for certain types of output to produce the output that we see in all destinations (including the SAS listing). By tracing output objects and `ODS` templates using `ODS TRACE` (DOM) and by manipulating procedural output and `ODS` OUTPUT objects, we can pick and choose just the information that we want to see. We can then harness the power of SAS data management and reporting procedures to coalesce the information collected and present the information accurately and attractively	proc step, proc contents, estimate, ods output, output, proc means, sample, excel, data step, proc surveyfreq, logistic distribution, output delivery system	source	26
362	World Wide Wrangler: How to Create SAS Data Sets from HTML Files in Seconds	Ed Summers, Brice Smith, Sean Mealin	Have you ever found a table of data on a web page and wanted to scrape that data into a SAS data set? This paper shows you how to do it in seconds using SAS Graphics Accelerator for Google Chrome, which is a free extension for Google Chrome. First, you open the web page in Chrome. Then, you extract data from the table into your Laboratory within SAS Graphics Accelerator for Google Chrome. SAS Graphics Accelerator for Google Chrome automatically derives variable labels from column headings and variable lengths from observations. It also derives variable "types" if all observations in a column match patterns for a single type. If they don't, you can choose the type of a variable manually. Finally, you generate a SAS program that creates a data set in your WORK library with correct variable labels, lengths, and formats. With SAS Graphics Accelerator for Google Chrome, you'll be the fastest data wrangler on the web!	proc sgplot, label statement, dataset, work library	source	7
363	Writing a Gradient Boosting Model Node for SAS Visual Forecasting	Yue Li, Jingrui Xie, Iman Vasheghani Farahani	SAS Visual Forecasting, the new-generation forecasting product from SAS, includes a web-based user interface for creating and running projects that generate forecasts from historical data. It is designed to use the highly parallel and distributed architecture of SAS Viya, a cloud-enabled, in-memory analytics engine that is powered by SAS Cloud Analytic Services (CAS), to effectively model and forecast time series on a large scale. SAS Visual Forecasting includes several built-in modeling strategies, which serve as ready-to- use models for generating forecasts. It also supports custom modeling nodes, where you can write and import your own code-based modeling strategies. Similar to the ready-to-use models, these custom modeling nodes can also be shared with other projects and forecasters	gradient boosting, response variable, predictor variable, macro variable, time series analysis, data cleaning, proc step	source	19
364	Zero-Inflated and Zero-Truncated Count Data Models with the NLMIXED Procedure	Robin High	`SAS/STAT` and `SAS/ETS` software have several procedures for analyzing count data based on the Poisson distribution or the negative binomial distribution with a quadratic variance function (NB-2). Count data may either have an excess number of zeros (inflation) or the situation where zero is not an outcome (truncation). Zero-inflated Poisson and negative binomial models are available with the COUNTREG, GENMOD, and FMM procedures. The `FMM` procedure also provides options for the zero-truncated Poisson and negative binomial distributions. Other types of count data models include the restricted and unrestricted generalized Poisson, negative binomial with a linear variance function (NB-1), and Poisson-Inverse Gaussian (P-IG) and likewise may be subject to zero-inflation or zero truncation. Programming statements entered into the `NLMIXED` procedure in `SAS/STAT` can model zero-inflated and zero-truncated count data with these distributions and may improve model fit which can be examined with the Vuong test or by comparing various fit statistics.	poisson distribution, likelihood function, binomial distribution, negative binomial distribution, data standard, estimate, negative binomial regression, proc nlmixed, proc step, normal distribution, zero-inflated poisson, zero-inflated negative binomial, proc genmod, maximum likelihood estimation, sample, missing value	source	18

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