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BUGSSGF 2018 Papers - Index
469 papers. Source: SAS Global Forum conference proceedings.
| # | Title× | Authors× | Abstract× | Keywords× | Extras | Pages |
|---|---|---|---|---|---|---|
| 1 | !WRONG PAPER FIEL - WAITING for FIX! | Nikki M. Carroll | Treatment effects from observational studies may be biased as patients are not randomly allocated to a treatment group. Propensity score methods are increasingly being used to address this bias. After propensity score adjustment, the distribution of baseline covariates will be balanced between treated and untreated patients. This paper reviews variable selection, balancing the propensity score, sensitivity analyses and presentation of results for 5 different propensity score methods: covariate adjustment, stratification, inverse probability of treatment weighted (IPTW), stabilized IPTW, and matching. Strengths and limitations of each method are illustrated by estimating the effect of anti-hypertension treatment on survival in advanced stage non-small cell lung cancer patients | propensity score, treatment effect, propensity score matching, logistic regression, baseline characteristics, sensitivity analysis, feature selection, logistic distribution, regression analysis | source | 9 |
| 2 | %Q-Index: A SAS Macro for a Conditional Item-Fit Index for the Rasch Model | Samantha Estrada, Sergio Nava-Munoz, Rafael Perez Abreu |
Presentation | source | 14 | |
| 3 | A 'Big Red Button' for SAS Administrators: Myth or Reality? | Sergey Iglov | Over the years, many attempts have been made to develop a solution that can make life for SAS Administrators easier. Some were more successful than others, though none reached the point at which the process is fully automated, simple, efficient, fault-tolerant, and manageable. This paper explores the subject of so called Big Red Button-one solution that can make life for SAS Administrators as easy as clicking one button. Starting with software provisioning through installation and configuration to support and maintenance, this paper looks at main steps of the SAS Platform lifecycle, highlighting challenges and trying to find optimal solutions that link them all together. With virtualisation, automation, and cloud providers on the rise, in recent years the possibility of creating such a solution has finally become a reality | sas viya, service provider | source | 11 |
| 4 | A Basic Introduction to SASPy and Jupyter Notebooks | Jason Phillips | With the recent introduction of the official SASPy package, it is now trivial to incorporate SAS into new workflows 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 provides an overview of Jupyter Notebooks for the uninitiated, along with the initial steps and options for working with your SAS instance using SASPy. Included along the way are the general principles of passing data between Python's DataFrames and SAS data sets, as well as the unique advantages SAS brings to the Notebook workspace and Python ecosystem | sas base, machine learning | source | 7 |
| 5 | A Case Study of Mining Social Media Data for Disaster Relief: Hurricane Irma | Bogdan Gadidov, Linh H. Le |
In the wake of two recent hurricanes, Harvey and Irma, local, state, and federal governments are trying to provide relief to the millions of affected people. With projected property damage in the hundreds of billions of dollars, these recent natural disasters will have long -lasting effects on their respective areas where recovery could take years. This paper aims to use social media data, specifically Twitter, to analyze how people in the affected areas reacted to these natural disasters in the days leading up to the storm, duri ng the storm, and after the storm. The goal is to see if there are any trends detected in the responses of affected citizens which can be used to help relief efforts in future natural disasters. For the most recent hurricane, Irma, we collecte d tweets in South Florida and analyze the discussed topics among civilians . Data was collected from Thursday (9/7/2017) to Wednesday (9/13/2017) (with the hurricane making landfall on Sunday morning) | time series analysis, parsing, proc freq, proc arima, homoscedasticity, data mining, stationarity, proc step, correlation, sample | source | 11 |
| 6 | A Cautionary Tale of Using Machine Learning within Credit Risk in the Utilities Industry | Spiros Potamitis, Paul Malley |
Machine learning is a scientific area that is constantly growing and enriched with evermore sophisticated and powerful algorithms. The question that arises is what is actually valuable in the business world and what algorithms can be applied in a business environm ent, such as Credit Risk, which is heavily regulated and interpretation of results constitutes a top priority? We will address th is issues by showcasing a real -life business case and the algorithms that have been used to provide the most accurate results a long with their advantages and disadvantages. In addition to providing an overview of the most popular machine learning algorithms that we use in SAS Enterprise Miner, we compare the algorithms' results with the incumbent methodology used within Credit R isk, namely Weight of Evidence (WoE) logistic regression. We identify the benefits that complex algorithms could bring to businesses, and in which business cases they could and should be implemented | data mining, logistic regression, credit risk, regression analysis, machine learning, logistic distribution, proc hpsplit, proc hpforest, gradient boosting, missing value, validation set, sample, credit scoring, data sampling, data cleaning, outlier | source | 8 |
| 7 | A Fraud Management Solution for Middle Market Banks and Ways to Reduce False Positives | Gourish Hosangady | Financial institutions generate enormous amounts of transaction data each day. The pressure on compliance and the need for quick detection of fraud continues to increase. As a consequence, the same institutions need to reduce losses from penalties and fraud. The challenge lies in how best to use a select set of rules coupled with modeling-using data science and machine learning techniques to address this challenge. Suspicious transactions should be flagged with minimal false positives. The process also should maximize productivity and create a degree of seamlessness in both alert creation and investigations. Once compliance and fraud are both addressed, further analysis of customer and transaction data might be performed to gain insights into customer behavior. Such an approach can achieve the following goals: a) Reduce false positives, achieve cost benefits | type i error, machine learning, case management, likelihood function, sample | source | 6 |
| 8 | A General SAS Macro to Implement Optimal N:1 Propensity Score Matching Within a Maximum Radius | Kathy Hardis Fraeman | A propensity score is the probability that an individual will be assigned to a condition or group, given a set of baseline covariates when the assignment is made. For example, the type of drug treatment given to a patient in a real-world setting might be non-randomly based on the patient's age, gender, geographic location, and socioeconomic status when the drug is prescribed. Propensity scores are used in many different types of observational studies to reduce selection bias. Subjects assigned to different groups are matched based on these propensity score probabilities, rather than matched based on the values of individual covariates. Although the underlying statistical theory behind the use of propensity scores is complex, implementing propensity score matching with SAS is relatively straightforward. An output data set of each subject's propensity score can be generated with SAS using PROC LOGISTIC |
propensity score, propensity score matching, proc logistic, logistic distribution, proc step, dataset, macro definition | source | 7 |
| 9 | A Macro for Ensuring Data Integrity When Converting SAS Data sets | Richard D. Langston | This paper describes the %COPY_TO_NEW_ENCODING macro, which ensures that a SAS data set does not experience data loss when being copied to a new encoding. The focus is on the FORMAT procedure's CNTLOUT data sets and the problems that can occur when converting from single- byte encoding like Windows Latin1 to multi-byte encoding like UTF-8 |
character variable, length statement, macro variable, proc step | source | 6 |
| 10 | A Macro for Last Observation Carried Forward | Jonson Jiang | This paper is about an application on the last observation carried forward (LOCF) method [1,2,3]. The last non-missing observed value is used to fill in missing values at a later time poin t when data is in longitudinal structure. There are two simple loops inside of Macro. The k-loop is for parameter variables and i-loop is for records continuously missing per parameter | last observation carried forward, macro call, missing value, clinical trial, imputation | source | 5 |
| 11 | A Macro That Creates U.S. Census Tracts Keyhole Markup Language Files for Google Map Use | Ting Sa | This paper introduces a macro that can generate the Keyhole Markup Language (KML) fil es for U.S. census tracts. The generated KML files can be used directly by Google Maps to add customized census tracts layers with user-defined colors and transparencies. When someone clicks on the census tracts layers in Google Maps, customized information is shown. To use the macro, the user needs to prepare only a simple SAS input data set and download the related KML files from the U.S. census Bureau. The paper includes all the SAS code for the macro and provides examples that show you how to use the macro as well as how to display the KML files in Google Maps | create table, sample | source | 10 |
| 12 | A Need For Speed: Loading Data via the Cloud | Hadley Christoffels | The value of the effective use of data is universally accepted, and analytical analysis methods such as machine learning make it possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results. However, before any such value can be realized, the data must be collected, moved, cleansed, transformed, and stored as efficiently and quickly as possible. SAS Viya not only addresses complex analytical challenges but can also be used to speed up data management processes. In this paper, we look at cloud infrastructure, code enhancements, and storage technologies that help you achieve this goal | sas viya, cpu time | source | 7 |
| 13 | A One-Sided Fisher's Exact Test: A Tail of Clinical Worsening | Bill Coar | In many disease areas, therapeutic intervention is designed to prevent clinical worsening of the disease, thereby increasing the quality and length of life for patients with life threatening diseases. While this research hypothesis may be simple to state in non-statistical terms, there are many endpoints that can be used to provide insight to this question. One of the common approaches to address this question is to simply ask if a patient experiences clinical worsening after some fixed exposure to a new therapy. For diseases that rapidly progress, intervention of a new therapy should delay clinical progression for the drug to be deemed efficacious. This suggests that the proportion of patients who experience clinical worsening while on active study drug should be lower than the proportion of patients experiencing clinical worsening in a control group. There are multiple ways to demonstrate this with a statistical test | alternative hypothesis, exact test, fisher exact test, treatment arm, proc freq, odds ratio, sample, proc step, class variable, response variable | source | 9 |
| 14 | A Periodic Table of Introductory SAS ODS Graphics Examples | Ted Conway | Presentation | source | 5 | |
| 15 | A Programming Approach to Implementing SAS Metadata-Bound Libraries for SAS Data Set Encryption | Deepali Rai | SAS metadata-bound libraries provide a robust means of protecting SAS data without compromising on accessibility of the secured data. Although seamless access to metadata-bound data controlled through metadata authorizations is an advantage of using metadata-bound libraries, their implementation and maintenance can potentially be a cumbersome task for administrators. Administrators face complexities in maintaining and managing metadata-bound libraries if multiple users create and own SAS data. Proper planning is required to handle complexities around heterogeneous directory ownerships, password and encryption key management, and monitoring of unencrypted SAS data. This paper explains those complexities and provides best practice recommendations for both administrators and SAS programmers working with SAS data. This paper provides code that can be used to empower SAS programmers to define metadata-bound libraries through a controlled process | libname statement, proc step, dataset, macro definition, proc copy, modify statement, stored macro, sas programmer, sample, proc sql, excel | source | 15 |
| 16 | A Quick Bite on SAS Studio Custom Tasks | Michael Kola | Custom Tasks in SAS Studio help user s generate reports through a point and click user interface (UI). As a developer , there will be a need to create custom tasks for non-SAS programmers as well as novice SAS user s or programmers. After creating a custom task, they can be shared with stakeholders using the common task model (CTM) or common task kit (CTK) files. Tasks are saved as CTM files or CTK files. CTK file are CTM files with roles and options pre-selected. The file could be shared through email or by just placing it on a shared area. The user simply has to run the file and it generates the User Interface, as the user navig ates through the UI , the SAS code is generated simultaneously , which will help the user review the SAS code related to the UI. The task framework is flexible. All tasks use the same common task model and the Velocity Template Language. The task consists of task elements and they are: Registration, Metadata, UI, Dependencies, Requirements and Code Template | proc step, sas studio | source | 14 |
| 17 | A Quick Guide for Your Data Load Problems in SAS Visual Analytics | Swetha Vuppalanchi | sas enterprise guide, sas management console | source | 10 | |
| 18 | A SAS Macro Implementation of a Test for Co-directional Interactions | Julia Sharp, Patrick Gerard, Bereket Tesfaldet |
In two-factor factorial experiments, caution is often advised in interpreting the main effects when the interaction term is significant. However, when the interaction is co-directional relative to a particular factor, the main effect test for that factor can be considered. Gerard and Sharp (2011) developed a formal method for testing for co-directional interactions based on union-intersection and intersection-union techniques. A SAS macro was developed for the implementation of this method in SAS. This presentation uses examples to demonstrate the method and the macro. The macro can be called within or outside of the GLM procedure, and it has four required and two optional input parameters |
anova, main effect, proc calis, proc glm, proc genmod, proc step, null hypothesis, degrees of freedom, interaction effect, significance level, macro definition, response variable | source | 16 |
| 19 | A SAS Macro That Uses PRX Functions to Verify Delimited Text File Formatting | Paul Genovesi | The task of importing delimited text files can often seem like a black box process. If the resulting data set contains errors, how do you know? Some errors are easy to spot, such as unspecified extra fields showing up in the data set. Other errors, like data ending up in the wrong field, can be more difficult to identify, especially when they occur well into the resulting data set. Fortunately, every correctly formatted, delimited text file complies with one of two formats 1, each containing its own text pattern. If every cell of the delimited text file matches the text pattern, then the file is said to be in that format. The text -qualified format is the more complex of the two and the one that this paper concentrates on. The non -text-qualified format is very simple and receives only minor mentioning. This paper introduces a SAS macro that uses SAS PRX functions to verify that a delimited text file is in text-qualified format | txt, sas enterprise guide, dataset, proc import, delimited file, macro definition, proc step | source | 37 |
| 20 | A SAS Program for a Model-Based Stratification Method | Xiaoli Lu | Presentation | source | 4 | |
| 21 | A SAS Visual Analytics Solution for the Centers for Medicare and Medicaid Services | Raymond Mierwald, Shanika Palm, Jonathan Mccopp, Manuel Figallo-Monge |
Presentation | source | 4 | |
| 22 | A Simple Approach to Text Analysis Using SAS Functions | Wilson Suraweera, Jaya Weerasooriya, Neil Fernando |
Analysts increasingly rely on unstructured text data for decision making than ever before. Text data mining is a process of deriving actionable insights from a lake of texts. It discovers unseen patterns of words in data or known words or textual patterns in undetected records in data bases. SAS has its own dedicated text mining tools such as SAS Contextual Analysis, SAS Text Minor. However, their use by general users is precluded by affordability and availability. We developed a simplified but robust approach for text analysis using a combination of 3 simple SAS string functions namely Index, IndexW and SoundeX in Base SAS macro environment. Application was further condensed to a few lines of SAS codes and tested with several large scale datasets in epidemiological studies to ascertain its applicability, reliability and scalability. This article explains our approach and discusses its applicability by using a commonly available SASHELP dataset | proc freq, sas base, macro variable, string functions, character functions, macro definition | source | 10 |
| 23 | A Simple Methodology for Customer Classification in Two Dimensions | Adriana Mara Guedes Barbosa, Alan Ricardo da Silva |
Presentation | source | 14 | |
| 24 | A Study of Modeling Approaches for Predicting Dropout in a Business College | Helmut Schneider, Xuan Wang |
Graduation rate is of interest for stakeholders of higher education including educational researchers and policymakers; and as widely acknowledged, retention rates are driving graduation rates. This study explores the use of predictive analytics in an academic institution for improving retention rates using data from the Louisiana State B usiness College as an example. The study applies SAS Enterprise Miner to build predictive models for identifying the students at risk of dropping out at different stages in their program. The institutional administrators can use the results from the models to identify students who need advising and remedial actions to help retain students and lead them to graduation. Preliminary findings from the study show that an ensemble model is the best for predicting student dropout. In addition, the predictive models can be further improved by collecting additional information about behavioral issues and study habits during the first year | data mining, nominal variable, ordinal variable, predictive analytics, proc hpsplit, gradient boosting, logistic regression, regression analysis, response variable, neural network, logistic distribution | source | 8 |
| 25 | A Survey of Some of the Most Useful SAS Functions | Ronald P. Cody | SAS Functions provide amazing power to your DATA STEP programming. Some of these functions are essential- some of them save you writing volumes of unnecessary code. This paper covers some of the most useful SAS functions. Some of these functions may be new to you and they will change the way you program and approach common programming tasks |
character variable, missing value, length statement, substr function, input statement, lag function, compress function, sas function, put statement, sysdate9, yrdif, character functions, numeric functions, call missing, call sortn, missing function, if-then-else | source | 16 |
| 26 | A Transition from SAS on a PC to SAS on Linux: Dealing with Microsoft Excel and Microsoft Access | Jesse Speer, Ruby Johnson, Sara Wheeless |
Transitioning from SAS installed on a PC running Microsoft Windows to a SAS Grid environment running on Linux presents many challenges for analysts, programmers, and IT professionals alike. Aside from navigating the idiosyncrasies of each operating system, like case sensitivity and the direction of slashes in file pathnames, there arise more significant issues such as handling data from commonly used Microsoft Office products. This discussion focuses on the challenges of importing and exporting data files from Microsoft EXCEL and Microsoft Access between SAS software on Windows and SAS Grid software (specifically SAS Enterprise Guide and SAS Studio) on Linux. The talk outlines the difficulties encountered and the solutions proposed in order to provide tips for other SAS programmers who must make this type of transition between SAS environments |
excel, sas grid, xls, proc import, proc step, proc export, infile statement, libname statement | source | 12 |
| 27 | Accelerate IoT Insights at the Intelligent Edge | Mark Barnum | The Internet of Things (IoT) unlocks efficiencies and innovation from connected sensors, assets, and devices. Organizations now have access to both immediacy and depth of analysis by using edge analytics. See how Hewlett Packard Enterprise and enables instantaneous, secure access to analytics for real-time decision-making, immediate action, and greater device control | iot, predictive maintenance, edge computing | source | 13 |
| 28 | Accelerate Your Analytics with SAS and Teradata Using Disparate Data Sources | Paul Segal | The SAS DS2 language originated from an effort to enhance the SAS DATA STEP. DS2 syntax overlaps significantly with DATA STEP and DS2 include additional data types, ANSI SQL types, programming structure elements, user defined methods and packages. Data sources for DS2 are expanded to include several RDBMSs, hence, row, column and table are used to describe data elements as opposed to observation, variable and data set in DATA STEP. DS2 programs can be submitted using SAS/ACCESS LIBNAME connection, through the new language interface delivered in SAS Base procedure PROC DS2. |
proc step, proc sql, proc transpose, by group processing | source | 6 |
| 29 | Accelerate your End-to-End Enterprise Decisions with SAS Viya | David R. Duling, Steve Sparano |
Do you need to add speed to your enterprise decision systems? SAS Viya to the rescue. This paper guides you through the entire process of building and deploying decisions systems that integrate advanced analytical models with precise business rules, using software available only on SAS Viya. This software includes SAS Visual Data Mining and Machine Learning, SAS Model Manager, and SAS Decision Manager. Decision processes can be deployed to SAS Cloud Analytic Services, Teradata and Apache Hadoop databases, SAS Micro Analytic Service for on-demand processing, and SAS Event Stream Manager for integration into high-speed data streams | data validation rules, sas model manager, proc hpsplit, neural network, sas viya, sas studio, score code, machine learning, network optimization, data science | source | 12 |
| 30 | Accessibility and ODS Graphics: Seven Simple Steps to Section 508 Compliance Using SAS 9.4M5 | Ed Summers, Julianna Langston, Dan Heath |
How do you create data visualizations that comply with the Section 508 amendment to the United States Workforce Rehabilitation Act, the Web Content Accessibility Guidelines (WCAG), and other accessibility standards? It's easy when you use the new Output Delivery System (ODS) Graphics accessibility features in SAS 9.4M5. This paper defines seven simple steps to create accessible data visualizations. The accessibility requirements that are satisfied by each step are explained, and additional references are provided. It includes sample code for real-world examples that has been tested by the SAS accessibility team. It also includes a handy one-page checklist that you can print separately for future reference |
sample, proc sgplot, data visualization, bar chart, ods graphics, sas programmer, output delivery system, label statement, proc step | source | 13 |
| 31 | Accessing Data from Microservices in SAS 9.4 Using DS2 Built-In Packages | James Kelley | This paper describes how, as of SAS 9.4M3, to use the DS2 built-in packages for HTTP get and post operations, JSON parsing, and hash mapping to access data from a restful microservice. The example code details how to get an HTTP response from a service, parse the JSON returned, normalize the values into data rows, and then use the SAS DS2 hash package to write those rows into a data set for further processing | hash table, order by, proc sql, proc step | source | 24 |
| 32 | Administering the Administrators: Who Watches the Watchers? | Andrew Howell | Presentation | source | 42 | |
| 33 | Adopt A Pet (Elephant?): Are You Enjoying Your Apache Hadoop Investment? | Rex Pruitt | Hundreds of companies have embraced Apache Hadoop as a component of their data storage and analytics strategy. But they have yet to realize the return on investment (ROI). Many have been focused on the savings from a move to cheaper hardware and software infrastructure. Others thought having large volumes of unstructured distributed data would lend itself to new and innovative ways to analyze that data. Their hope is to drive out valuable business insights, resulting in better decisions for the company. Seriously, have companies really just gone to an adopt-a-pet strategy to save a few bucks? What are you doing to achieve "full adoption?" Come and learn the path to success! | hadoop, data science, sas administrator, proc sql, proc step | source | 16 |
| 34 | Advanced Graphs Using Axis Tables | Sanjay N. Matange | An important feature of graphs used for the analysis data or for clinical research is the inclusion of textual data in the graph, usually aligned with the x or y axis. The axis table statements available with the SGPLOT procedure make it easy to add such data to the graphs. Axis tables can also be used for creating custom axes or custom graphs. This presentation will describe how to use axis tables to create complex custom graphs |
kaplan-meier plot, forest plot, proc sgplot, proc sgpanel, clinical research | source | 12 |
| 35 | Advanced Programming Techniques Using the DS2 Procedure | Viraj R Kumbhakarna | proc sql, proc step, hash table, proc fcmp, connection string, sas function, variable list, dataset, sample, data step, hash iterator, key, sas base, cpu time, select | source | 20 | |
| 36 | Algorithmic Marketing Attribution and Conversion Journey Analysis | Suneel Grover, Malcolm Lightbody |
Everyone has a marketing attribution1 problem, and all attribution measurement methods are wrong. You hear that all the time. Like all urban myths, they are founded on some truth. Most organizations believe they can do better on attribution. They all perceive gaps in missing touchpoint data, multiple identities across devices, arbitrary decisions on weightings for rules, and uncertainty on what actions to drive from | machine learning, artificial intelligence, stochastic economic scenario | source | 14 |
| 37 | Alternative Variance Parameterizations in Count Data Models | Robin High | SAS/STAT and SAS/ETS software have several procedures for working with count data based on the Poisson or negative binomial distributions. In particular, the GENMOD and GLIMMIX procedures offer the most conventional approaches for estimating model coefficients and assessing goodness of fit and also for working with correlated data. In addition, the COUNTREG procedure includes the Conway-Maxwell Poisson distribution with a statement to model dispersion and the negative binomial distribution with two different variance functions. The FMM procedure includes a generalized Poisson distribution as a method to deal with over-dispersion in count data. However, programming statements entered into the NLMIXED procedure in SAS/STAT can also model count data with other distributions and in particular to estimate the dispersion parameter as a function of covariates, and as a result modify the variance function to improve the fit. |
poisson distribution, binomial distribution, likelihood function, proc step, negative binomial regression, negative binomial distribution, proc nlmixed, regression analysis, estimate, poisson regression, goodness of fit, geometric distribution, data standard, proc genmod, proc glimmix, do loop, proc iml, response variable, macro variable, maximum likelihood estimation, dataset, predictor variable | source | 14 |
| 38 | An Analytics Approach to Determine the Impact of Leaks on TV Shows | Prathap Maniyur, Mansi Bhat, Prashanth Nayak |
Presentation | source | 25 | |
| 39 | An Easier and Faster Way to Untranspose a Wide File | Joe Matise, Arthur S. Tabachneck, Matthew J. Kastin, Gerhard Svolba |
PROC TRANSPOSE is an extremely powerful tool for making long files wide, and wide files less wide or long, but getting it to do what you need often involves a lot of time, effort, and a substantial knowledge of SAS functions and DATA STEP processing. This is especially true when you have to untranspose a wide file that contains both character and numeric variables. And, while the procedure usually seamlessly handles variable types, lengths and formats, it doesn't always do that and only creates a system variable (i.e., _label_) to capture variable labels |
proc transpose, proc step, data step, macro variable, call execute, proc sql, order by, numeric variable, dataset, by group processing, create table, sas library, character variable, variable list, macro definition, %if-%then-%else | source | 36 |
| 40 | An Ingenuity Journey to Set ROI and Benefits Tracking | John Jarrett | It is becoming increasing competitive within organizations to prioritize capital and operational funds for new projects. As business owners and project sponsors, it is our responsibility to create the business case for project funding, and equally important, to update the organization on the ROI and Value of the software investment along the way and as an ongoing future cadence. For many, the challenges are….How do we get started? How do we measure effectively? How do we op erationalize the process to be efficient and repeatable? Retailers and wholesalers invest heavily in technology, people, processes, and data to create relevant assortments across channels. While technology and vast amounts of data help localize assortments based on customer preferences, product attributes, and store performance, it's impossible to complete the assortment planning process down to the most granular level of size | correlation, data science | source | 3 |
| 41 | An Insider's Guide to SAS/ACCESS Software on SAS 9.4M5 in the Cloud | Jeffrey Bailey | You might not have data in the cloud today, but chances are you will in the very near future. Are you ready? Do you have any idea where to start? If "no" is the answer to either of these questions, then this presentation is just what you are looking for. We answer the following questions (and more): What are the steps involved in creating a database in a cloud environment? What is Database as a Service (DBaaS)? How does SAS define the terms "cloud variant" and "database variant"? How do I determine whether my cloud database is supported by SAS? The presentation would not be complete without discussing the major cloud players. We discuss Amazon Web Services (AWS), Microsoft Azure Cloud, Oracle Cloud, Teradata IntelliCloud, and Google Cloud Platform | proc sql, sql server, teradata, teradata access, amazon redshift, amazon s3, oracle, rest api | source | 8 |
| 42 | An Introduction to Clustering Techniques | Xinghe Lu | Presentation | source | 7 | |
| 43 | An Introduction to Parallel Processing with the Fork Transformation in SAS Data Integration Studio | Jeff Dyson | The SAS Data Integration Studio job is historically a sequential process. The user designs a series of extract, transform, and load steps that move data from source systems, apply derivation logic, and load to target tables one step at a time. Even when no true dependencies exist, the nature of the job structure forces an implied requirement that the previous step complete before starting each node. With the new Fork transformation, users can now run independent streams of logic in parallel within a single job, creating opportunities for improved performance and shorter runtimes | dataset, etl developer, mp connect, parallel processing, proc sql | source | 16 |
| 44 | An Investigation of the Factors Associated with Opioid Misuse | Claire Masson | The primary objective of this study is to identify and describe the factors with prescription opioid misuse. This study used the data from the 2016 National Study for Drug Use and Health (NSDUH) survey. The data provides evidence that a person who exhibits certain factors is more likely to misuse prescription opioids than one who does not share the same characteristics. The factors that influence the propensity to misuse opioids include; mental health, age, race, cigarette use, alcohol frequency, marijuana frequency, sedative use, and smokeless tobacco use | proc freq, response variable, sas enterprise guide | source | 6 |
| 45 | An unusual remedy using the usual "nbins" option to rectify anomalous histograms in SAS | Rachana Lele | Presentation | histogram | source | 12 |
| 46 | Analysis of At-Risk Behaviors Compared to Public Health Funding in 2011-2015 in 18-24 Year Olds | Jacob Baggs | Presentation | source | 9 | |
| 47 | Analysis of Nokia Customer Tweets with SAS Enterprise Minerand SAS Sentiment Analysis Studio | Vaibhav Vanamala | Presentation | data mining | source | 13 |
| 48 | Analysis of Profitability Bank Systems in South Korea Using Base SAS | Jinwoo Cho, Dongwoo Kim |
Presentation | sas base | source | 8 |
| 49 | Analysis of Unstructured Data: Topic Mining and Predictive Modeling Using Text | Ravi Teja Allaparthi | Presentation | source | 4 | |
| 50 | Analytics + IoT = Happy (Utility + Customer) | Bradley Lawson | The Public Utility Regulatory Policies Act (PURPA) changed the utility world in 1978. Power generation was no longer the domain of electric utilities, but was opened to independent power producers. Bigger challenges might be imminent with the falling price of batteries. Individual homes and businesses will add batteries to new and existing solar panels. The biggest disruptor, however, will be the acceleration of electric and hybrid vehicles. Large growth of electric vehicles (EVs) could affect local reliability and increase the cost of delivering electricity to customers. In many areas of the US and the world, one new EV charger can add as much demand as a new residential customer. Fast chargers that recharge EVs in just a few hours can add three times that demand. Analytics and the Internet of Things (IoT) must become partners to create systems that enable the customer and utility to continually communicate their status and options | iot, likelihood function | source | 9 |
| 51 | Analytics Applications in Targeted Marketing and Forecasting Demand: A Two-Stage Model | Anurag Hardikar | Identifying target customers for a product/service is one of the most important steps in developing a marketing plan for any business. A large electric utility company in Oklahoma with a customer base of more than 800,000 have recently launched its solar power program. The objective of this paper is twofold. In the first stage, build a classification model to identify; customers that have a higher propensity to enroll in the company's solar power program so as to drive savings on promotional mailing. Then, in the second stage, we see how we can forecast electricity consumption to predict the solar power capacity the company would need to fulfill the associated demand | time series analysis, feature engineering, f1 score, seasonality, sample, trend | source | 7 |
| 52 | Analytics Centralization | Raj Kannan | Presentation | source | 17 | |
| 53 | Analytics in the Cloud, Cross Functional Teams, and Apache Hadoop is not a Thing | Ryan Packer | Digital analytics is no longer just about tracking the number of visits to a page or the traffic from a campaign. Now the questions are about user experience and customer journeys. To answer these questions, we need a new breed of tracking tools and new ways of analyzing the data. The Bank of New Zealand recently implemented SAS Customer Intelligence 360. Over the last year, we would have done proof of concepts across multiple channels, made architecture decisions (and then remade them), integrated it into all our customer-facing digital channels, brought the data into our big data platform, provided near real-time reports and got our agile development teams using it. "One line of JavaScript" is the famous phrase spouted by people selling digital analytics platforms | digital analytics, big data, rest api, sas visual analytics | source | 8 |
| 54 | Analytics Like a Pro: The Differences between My Fantasy Team and Production Analytics in Sports | Timothy Trussell | Presentation | data science, output delivery system, big data, sas engineer, recommendation engine, sample | source | 21 |
| 55 | Analytics of Things: New Analytical Models for Creating Business Value from IoT Data | Ryan Gillespie | The number of devices and equipment generating sensor data is rapidly increasing. To intelligently handle this data and create tangible business value requires new analytic techniques and new ways to apply them. This paper covers new SAS models related to the Analytics of Things. The paper focuses on actions and procedures tailored to Internet of Things (IoT) and includes use cases for predictive maintenance, asset degradation, anomaly detection, and signal processing. We highlight what new models are available in SAS, their related use cases, and how they can be deployed in standard or real - time scenarios for IoT solutions from manufacturing to health care | proc princomp, proc reg, outlier, regression analysis, iot, predictive maintenance | source | 11 |
| 56 | Analyzing Text In-Stream and at the Edge | Simran Bagga | As companies increasingly use automation for operational intelligence, they are deploying machines to r ead, and interpret in real time, unstructured data such as news, emails, network logs, and so on. Real- time streaming analytics maximizes data value and enables organizations to act more quickly. For example, being able to analyze unstructured text in-stream and at the "edge" provides a competitive advantage to financial technology (fintech) companies, who use these analyses to drive algorithmic trading strategies. Companies are also applying streaming analytics to provide optimal customer service at the point of interaction, improve operational efficiencies, and analyze themes of chatter about their offerings | stream processing, real-time processing, machine learning, score code | source | 12 |
| 57 | Analyzing Theft Occurrences in Chicago Using SAS Enterprise Miner and SAS Enterprise Guide | Kunal Parekh, Shikha Prasad |
Presentation | sas enterprise guide, data mining | source | 4 |
| 58 | Animate Your Data! | Richann J. Watson, Kriss Harris |
When reporting your safety data, do you ever feel sorry for the person who has to read all the laboratory listings and summaries? Or have you ever wondered if there is a better way to visualize safety data? Let's use animation to help the reviewer and to reveal patterns in your safety data, or in any data! This hands-on workshop demonstrates how you can use animation in SAS 9.4 to report your safety data, using techniques such as visualizing a patient's laboratory results, vital sign results, and electrocardiogram results and seeing how those safety results change over time. In addition, you will learn how to animate adverse events over time, and how to show the relationships between adverse events and laboratory results using animation. You will also learn how to use the EXPAND procedure to ensure that your animations are smooth. Animating your data will bring your data to life and help i mprove lives! |
change from baseline, series plot, box plot, proc step, adverse events, by group processing, ods graphics, output delivery system | source | 15 |
| 59 | Application of Propensity Score Models in Observational Studies | Nikki M. Carroll | Treatment effects from observational studies may be biased as patients are not randomly allocated to a treatment group. Propensity score methods are increasingly being used to address this bias. After propensity score adjustment, the distribution of baseline covariates will be balanced between treated and untreated patients. This paper reviews variable selection, balancing the propensity score, sensitivity analyses and presentation of results for 5 different propensity score methods: covariate adjustment, stratification, inverse probability of treatment weighted (IPTW), stabilized IPTW, and matching. Strengths and limitations of each method are illustrated by estimating the effect of anti-hypertension treatment on survival in advanced stage non-small cell lung cancer patients | propensity score, treatment effect, propensity score matching, logistic regression, baseline characteristics, sensitivity analysis, feature selection, logistic distribution, regression analysis | source | 9 |
| 60 | Application of Support Vector Machine Modeling and Graph Theory Metrics for Disease Classification | Jessica Rudd | Disease classification is a crucial element of biomedical research. Recent studies have demonstrated that machine learning techniques, such as Support Vector Machine (SVM) modeling, produce similar or improved predictive capabilities in comparison to the traditional method of Logistic Regression. In addition, it has been found that social network metrics can provide useful predictive information for disease modeling. In this study, we combine simulated social network metrics with SVM to predict diabetes in a sample of data from the Behavioral Risk Factor Surveillance System. In this dataset, Logistic Regression outperformed SVM with ROC index of 81.8 and 81.7 for models with and without graph metrics, respectively. SVM with a polynomial kernel had ROC index of 72.9 and 75.6 for models with and without graph metrics, respectively. Although this did not perform as well as Logistic Regression, the results are consistent with previous studies utilizing SVM to classify diabetes | logistic regression, logistic distribution, regression analysis, proc step, machine learning, support vector machine, sample, data cleaning, predictor variable, adam dataset, data mining | source | 10 |
| 61 | Are New Modeling Techniques Worth It? | Tom Zougas | New modeling techniques are constantly being developed, and at an increasing pace. Keeping up with these techniques is possible if the technique is accompanied by a library or an example that implements the technique in one of the many analytic tools at our disposable. However, the main question will always be whether the new technique is adding value and actually addressing the business problem at hand. This paper addresses the question of when to apply new modeling techniques, and more importantly, when not to | data science, unsupervised learning, proc hpsplit, machine learning, regression analysis, deep learning, supervised learning | source | 8 |
| 62 | Are Your Color Choices Ruining Your Reports? | Jaime D'Agord | Presentation | source | 5 | |
| 63 | Association Rule Mining of Polypharmacy Patterns in Health Care Data Using SAS Enterprise Miner | Dingwei Dai | Pediatric polypharmacy is prevalent in the both outpatient and inpatient setting, and is associated with increased exposure to adverse drug events (ADEs). The current definition of polypharmacy is simply a c ount of concurrent medication exposures for a given patient being equally to or greater than five, but this patient-level approach does not offer insights regarding the reasons why polypharmacy occurs that could be gained by a complementary drug-level approach, examining polypharmacy patterns not only across patients but also across medications. Health care administrative databases offer opportunities to examine drug-level relationships and patterns. Association rule mining (ARM) is a well-established data mining technique that has been commonly used for mining commercial transactional databases. Link Analysis (LA) is a popular social network analysis technique that is used to discover and visualize associations between different items | association rules, link analysis, data mining, proc freq, histogram, transaction data | source | 17 |
| 64 | At Your Service: Using SAS Viya and Python to Create Worker Programs for Real-Time Analytics | Scott Koval, Jon Klopfer |
Being able to automatically analyze and score data on demand has always been a challenge faced when creating a service program. SAS Viya brings with it new and exciting ways to help tackle this task. When combining SAS programming with the Python language, it is possible to create worker programs that can be deployed to automatically process any incoming data. This paper provides ideas in how to create an intelligent worker program that will wake up when needed, analyze data, and then rest until it is neede d again. Utilizing SAS Viya programming is essential for creating any real-time analytics environment and can be integrated to suit any business need | sas viya, cas | source | 10 |
| 65 | Auto Scaling SAS Real-Time Decision Manager on Amazon Web Services | Chris West | SAS Real-Time Decision Manager combines SAS Analytics with business logic and contact strategies to deliver enhanced real-time recommendations and decisions to interactive customer channels such a s websites, call centers, point-of-sale (POS) locations, and automated teller machines (ATMs). SAS Real- Time Decision Manager helps you make smarter decisions by automating and applying analytics to the decision process during real-time customer interactions. Due to the interactive nature of these channels, the volume of requests can be subject to wide fluctuations, either hourly, daily, or seasonally. To enable our customers to accommodate the variance caused by a Black Friday or Cyber Tuesday event, this session presents a live demonstration of how elasticity can be brought to SAS Real -Time Decision Manager using Amazon Web Services (AWS), enabling the platform to dynamically grow or contract in response to the observed workloads | customer experience, parallel processing | source | 10 |
| 66 | Auto-populating Microsoft PowerPoint Presentations So You Don't Have To | Kaitie Lawson | In the field of clinical research, Data Safety Monitoring Board (DSMB) members are increasingly requesting high-level overview presentations to assess the current status of a clinical trial in lieu of lengthy binders. When multiple clinical trials report to the same DSMB, a certain level of standardization across presentations is desirable to facilitate ease in understanding and familiarity with the presentation format for the DSMB members. Manually entering text and data into a presentation is tedious and is subject to transcription errors. By utilizing ODS PowerPoint, ODS Text, ODS List, ODS Layout and a series of other familiar SAS procedures, we have created a suite of SAS macros that automatically populate presentations, therefore eliminating the need to manually enter data into the presentation slides |
macro call, macro variable, protocol deviations, primary endpoint, macro quoting function, clinical trial, macro definition, proc step, proc sgplot, proc report, %if-%then-%else | source | 17 |
| 67 | Automate the Tedious Stuff: Use Cases in Taking Your Time Back | Danni Bayn Jr. | With all of the buzz around big data, machine learning, and predictive analytics, it is easy to forget just how much of our day-to-day is spent on the far less glamorous task of data preparation. Analysts, researchers, and scientists alike spend a lot of their time trying to get the data into a workable state before they can begin getting to understand the story and the insights hidden within. This paper outlines three programs that were developed to handle common data wrangling tasks (e.g. cleaning, filtering, transforming, and merging). These programs took 60 hours of manual effort and replaced them with automated processes that run in less than 30 minutes. Code was developed with ActiveState Perl 5.24.2 and SAS 9.4 TS1M4 running under Windows 10. Novice users will be able to use all three programs, while further development and modification may require intermediate to advanced SAS expertise | excel, data wrangling | source | 7 |
| 68 | Automated Assistants for Fraud Investigation Productivity | Michael Ames | Analyst and investigator productivity is top of mind for many organizations, and they are looking to technology to boost both analyst productivity and investigation quality. The application of automation technology that allows employees to configure "automated assistants" often referred to as "robotic process automation". Automated assistants is an emerging form of clerical process automation based on the notion of software robots performing common investigator tasks | machine learning, feature engineering, feature extraction | source | 6 |
| 69 | Automated Management of UNIX/Linux Multi-Tiered SAS Services: The Enhanced SAS_lsm Utility | Clifford Meyers | Have you tried to manually restart SAS servers to avoid loss of productivity and failed, causing delays? Have you encountered issues and struggled to analyze the appropriate SAS logs as part of the troubleshooting process? After experiencing these issues, you can see that a utility that automates management of a SAS multi- tiered deployment's services would be a big benefit. To address these issues, SAS Technical Support developed the SAS Local Services Management (SAS_lsm) utility. The SAS_lsm utility is now available via SAS 9.4M5 and SAS Note 58231. It initially provided support for starting, stopping, and status checking a basic, multi-tiered deployment's services based on the sas.servers script | parallel processing, sas administrator, sas base, note, data cleaning | source | 20 |
| 70 | Automating Code Generation to Reduce Errors and Effort | Steve Cavill | This paper presents a series of solutions to automate code generation. This enables programmers to reduce effort and likelihood of errors by using some tools in SAS specifically for that purpose. In simple terms programmers can just write a macro and call the macro with variable parameters. This method still requires code editing. An alternative is to store the code variations in an external data set and have SAS read that data set and generate code automatically. This paper discusses techniques used to do this including CALL EXECUTE, CALL SYMPUTX and creating macro variables with PROC SQL INTO: clause. This technique has additional side benefits including reduced source code change control, simpler tracking of changes, and even the ability for the code changes to be managed by non-programmers This paper assumes reasonable knowledge of creating and using macro variables |
macro variable, call execute, proc sql, call symputx, proc step, cpu time, data step, proc print, sas log, into, macro put, macro call, sample, macro loop, macro quoting function | source | 15 |
| 71 | Automation of Clinical Trial Result Posting to ClinicalTrials.gov and EudraCT | Sherry Meeh | According to US Food and Drug Administration Amendments Act (FDAAA) and European Medicines Agency (EMA) regulations, companies are now required to disclose their applicable clinical trial's aggregated results to US ClinicalTrials.gov and to EMA European Clinical Tr ials Database (EudraCT) public websites: ClinicalTrials.gov (for US), and eudract.ema.europa.eu (for EMA). Currently in many pharmaceutical companies, the clinical trial aggregated results are prepared manually by a designated group based on the clinical study report. The manual process is time consuming and error-prone, with iterative back-and-forth steps and a lot of reviews among different groups. Therefore, an innovative and automated process is needed to proactively streamline the result posting process, improve the accuracy, efficiency, and consistency of the result posting, and reduce manual entry errors | clinical trial, dataset, macro definition, adverse events, clinical database, clinical sas | source | 9 |
| 72 | Automation of Linux Multi-tiered SAS and Load Sharing Facility (LFS) Services | Ghiyasuddin Mohammed Faraz Khan, Piyush K. Singh, Prasoon Sangwan |
Presentation | source | 18 | |
| 73 | Back to Basics: Get Better Insights from Data | Atrin Assa | Get the most out of your data, big or small. There is more data than ever, but more data doesn't always mean better insights. If you're not careful, more data can sometimes lead you down the wrong path. The worst thing that you can do is find patterns that aren't really there. SAS Visual Analytics gives you the tools to avoid these types of problems. With simple drag-and-drop functionality, you can explore your data. You can see the shape of your data. You can see potential relationships in your data. You can get a grasp of the power and limits of your data. With that understanding, you can get better insights. It's time to move beyond just pretty visualizations, and this paper shows you how | histogram, correlation, proc means, proc freq, outlier | source | 10 |
| 74 | Base SAS and SAS Enterprise Guide: Automate Your SAS World With Dynamic Code | Kent Phelps, Ronda Phelps |
Communication is the basic foundation of all relationships, including our SAS relationship with the server, PC, or mainframe. To communicate more efficiently ~ and to increasingly automate your SAS world ~ you will want to learn how to transform static code into dynamic code that automatically re-creates the static code, and then executes the re -created static code automatically. Our presentation highlights the powerful partnership that occurs when dynamic code is creatively combined with a dynamic FILENAME statement, macro variables, the SET INDSNAME option, and the CALL EXECUTE command within one SAS Enterprise Guide Program node. You have the exciting opportunity to learn how 1,469 time -consuming manual steps are amazingly replaced with only one time-saving dynamic automated step. We invite you to attend our session, in which we detail the UNIX and Microsoft Windows syntax for our project example and introduce you to your newest BFF (Best Friend Forever) in SAS |
filename statement, call execute, sas enterprise guide, macro variable | source | 20 |
| 75 | Bayesian Concepts: An Introduction | John Amrhein, Fei Wang |
You employ Bayesian concepts to navigate your everyday life , perhaps without being aware that you are doing so. You rely on past experiences to assess risk, assign probable cause, navigate uncertainty, and predict the future. Yet, as a statistician, economist, epidemiologist, or data scientist, you h old tight to your frequentist methods. Why? This paper explores the philosophy of Bayesian reasoning, explains advantages to applying Bayes' rule, and confronts the criticism of subjective Bayesian priors | bayesian statistics, posterior probability, likelihood function, sample, scenario analysis, confidence interval, proc calis, sample size, proc freq, regression analysis, data mining, prior probability | source | 10 |
| 76 | Bayesian Networks for Causal Analysis | John Amrhein, Fei Wang |
Bayesian Networks (BN) are a type of graphical model that represent relationships between random variables. The networks can be very complex with many layers of interactions. Graphical models become BNs when the relationships are probabilistic and uni-directional. Building BNs for causal analyses is a natural and reliable way of expressing (and confirming or refuting) our belief and knowledge about cause and effects. In addition, BNs can be easily reconfigured with minor modifications to facilitate our understanding of probabilistic mechanisms. This paper describes the construction of BNs for causal analyses and how to infer causal structures from observational and interventional data. The paper includes applications of causal BNs for classification using the HPBN Classifier node in Enterprise Miner. Visualization, inferences, and scenario analyses for the examples are discussed | probability distribution, posterior probability, response variable, data mining, proc step, feature selection, likelihood function, causal inference, latent variable, training set | source | 11 |
| 77 | Best Practices for Driving Business Value with CECL Compliance | Peter Baquero | The current expected credit loss (CECL) standard is a big change in credit loss provisioning and an even bigger opportunity to drive value through the Credit Risk function. At the very least you will be able to rationalize your existing credit risk models and pools. Ideally, you should be able to create a risk feedback loop to your product pricing and also be in synch with your liquidity risk projections. Understanding CECL is key to being able to drive the full value of the new requirement. We start with a review of CECL background before digging into methodology considerations. When discussing methodology considerations, we specifically cover segmentation, loan life and loan average life, loss rate options, forecasting, and qualitative adjustments. As we delve into these considerations you will see where the opportunities are to rationalize your existing loan loss process, provide enhanced risk-based pricing information, and share information with the Liquidity Risk function | credit risk, vintage analysis | source | 4 |
| 78 | BI Centralization: The Benefits of Implementing a Report Encyclopedia for Your Organization | John Mattox | In the world of Business Intelligence and Reporting, the standardization, maintenance/upkeep, accuracy, and reuse of reports across an organization are all key aspects to a well-groomed BI Solution - regardless of the technology. In a medium to large sized organization, it's highly likely that SAS is not the only reporting tool available to BI developers. As the sources of reports grow in number, it can become increasingly difficult to keep track of all of the reports and metadata associated with them. One solution to help solve this problem is to create a centralized catalog, or "Encyclopedia" to house all of this data. The report encyclopedia should serve as a method to accomplish the key aspects discussed above | proc sql, sas base, excel, sample, business intelligence, sql server, proc step, inner join, proc freq, macro definition | source | 13 |
| 79 | Big Data Analytic Models: The Challenges of Philosophies as Covariates in Higher Education | Sean W. Mulvenon | Use of BIG DATA analytics in business is commonplace and is supporting the growth of industries throughout the world. A major source of BIG DATA is K-12 and higher education. Since 2004 several billion in funding has been used to create BIG DATA systems in education, but there has been limited success in the use of this data to improve educational outcomes of students in the K -12 and Higher Education systems. Why? A singular goal of business is to generate revenue and evidence from B IG DATA facilitates these efforts. Too often in education, goals/outcomes are based on philosophies, rather than scientific evidence from BIG DATA. The purpose of this session is to outline the challenges of BIG DATA in higher education and the impact of philosophies as covariates | data mining, extract | source | 10 |
| 80 | Biomedical Image Analytics Using SAS Viya | Fijoy Vadakkumpadan, Saratendu Sethi |
Biomedical imaging has become the largest driver of health care data growth, generating millions of terabytes of data annually in the US alone. With the release of SAS ViyaTM 3.3, SAS has, for the first time, extended its powerful analytics environment to the processing and interpretation of biomedical image data. This new extension, available in SAS Visual Data Mining and Machine Learning, enables customers to load, visualize, process, and save health care image data and associated metadata at scale. In particular, it accommodates both 2-D and 3-D images and recognizes all commonly used medical image formats, including the widely used Digital Imaging and Communications in Medicine (DICOM) standard. The visualization functionality enables users to examine underlying anatomical structures in medical images via exquisite 3-D renderings | sas viya, machine learning, artificial intelligence, cas action, accuracy, deep learning, data cleaning, proc princomp | source | 12 |
| 81 | Bitcoin Price Forecasting Using Web Search and Social Media Data | Travis Miller | As the world's first decentralized electronic currency system, Bitcoin has achieved great success and represents a fundamental change in financial systems. The unique feature of Bitcoin is that its price fluctuation relies mostly on people's pertinent opinions instead of institutionalized money regulation. Therefore, understanding the interplay between social media and the value of Bitcoin is crucial for Bitcoin price prediction. In this study, related comments posted on the Bitcoin forum were analyzed. Conceptual link of extracted key-words of interests were developed. Five major clusters to help gain insight into Bitcoin's user opinion were obtained from text mining analysis. Furthermore, cross-correlation between Bitcoin price fluctuation and web search-Google Trends, social media-Twitter were examined respectively | correlation, time series analysis, sample, data mining, stationarity | source | 22 |
| 82 | Bricolage: My Autobiography, with SAS Procedures | AnnMaria De Mars | When it comes to starting a fascinating career, SAS is as good a starting place as any, and probably better than most. All of my various careers have had their roots in statistics and SAS. Could I have learned the same concepts and gotten the same results using another programming language, taking a different path? Probably. But I didn't. I did it with SAS. In this paper, I'll explain the many ways in which I have built a career by bricolage - that is, from building stuff - programs, companies - using whatever was lying about. With its application in a wide range of fields, from psychology to biostatistics, SAS has a largely unrecognized potential to address the shortage and lack of diversity of software developers by introduction of fundamental building blocks to success outside of the standard computer science curriculum | proc step, proc freq, sample, exact test, fisher exact test, proc report, sas programmer, confidence interval | source | 13 |
| 83 | Bridging the Skills Gap between Post-Secondary Education Outcomes and Employment Opportunities | Hong-Yu Xavier Fu, Allan Esser |
Students are often expected to take a leap of faith in their pursuit of Post-Secondary Education. Their decisions to develop the necessary skillsets that they will need to be successful are often fraught with uncertainty and misinformation. For Post-Secondary Education Institutions, the monitoring of employment qualifications is critical to ensure that post-secondary education supplies graduates with the essential skills required in the industries. Prediction of the changes in industry qualifications is an increasingly difficult task. Colleges often seek the advice of a combination of aggregated labor market data as well as the advisory support of Program Advisory Committees comprised of industry experts and practitioners. The skill classification codes of aggregate labor market data are often too general, and have a tendency to miss emerging skill demands as they enter into the marketplace | data mining, sample, proc freq | source | 7 |
| 84 | Building a Scalable Analytic Ecosystem Based on the Data System's Process Requirements | Sarah Kalicin | Presentation | source | 4 | |
| 85 | Building Revenue Management Decision Support Platforms with SAS | Eric Schmidt | The revenue management function in travel and hospitality addresses a well -known economics problem, maximizing revenue. The main facets of the problem are segmenting demand, understanding the price - demand relationship, estimating future demand, and finding the price level that maximizes revenue. However, with InterContinental Hotels Group's (IHG's) 13 hotel brands spanning more than 5,300 hotels and nearly 800,000 guest rooms in almost 100 countries1, that analytics challenge is no small task. The problem becomes even more complex when multiple guest segments, macro factors, seasonality, different lengths of stay, and local special events are added to the equation. Further, with decision updates needed daily for every booking date one year ahead, the process speed requirement necessitates an analytics platform | revenue management, cas, sas grid, demand forecasting, time series analysis | source | 6 |
| 86 | Business Customer Value Segmentation for Strategic Targeting in the Utilities Industry Using SAS | Paul Malley, Spiros Potamitis |
Numerous papers have discussed the importance of businesses understanding the value of their customers, taking advantage of various segmentation techniques to target customers more efficiently, enhance business processes and improve the customer journey. The process of creating a segmentation is often considered as a combination of science and art to deliver meaningful and useful results for the business. Additionally, traditional approaches of dynamically calculating customer value are not always achievable when the data required for the calculations are sparse, involve too much complexity or not accessible | data mining, value segmentation, cost to serve, sample, sas enterprise guide | source | 9 |
| 87 | Capital Planning with SAS Infrastructure for Risk Management and SAS Risk and Finance Workbench | Minna Jin | In this paper, we demonstrate how to integrate SAS Infrastructure for Risk Management and SAS Risk and Finance Workbench to perform efficient capital planning. On one hand, SAS Infrastructure for Risk Management serves as the execution engine to perform high-performance computation with great auditability and scalability. A script automatically turns worksheet-style data and formulas into SAS data sets and computational job flows, performing end-to-end calculation without intermediate manual data movements. On the other hand, SAS Risk and Finance Workbench provides a central hub to manage the various aspects of the process such as data preparation and validation, capital projection, stress testing, and regulatory reporting. The seamless integration of both software applications streamlines the capital planning process with ease of use, fast and scalable computation, and sound process control | sas risk, capital planning, stress testing | source | 8 |
| 88 | Catch at the Speed of Light: Analytics on Live-Streaming Data Using SAS Event Stream Processing | Murali Krishna Pagolu | Two of the most important dimensions of data, variety and volume, have challenged the wor ld of data analytics for quite some time. While volume dictated how data needs to be stored and accessed, variety resulted in innovative techniques to mine non-traditional data types. The third but most important dimension of data, velocity, has significantly changed the paradigm of what constitutes big data. We are indeed living in interesting times from the perspective of analytics as we look to mine data on the verge of its creation. Performing analytics on samples of stored historical data has its due s hare of importance but that is not enough now. Insights at the speed of light-what is happening right now at this second can be of tremendous value. The timelines of data to insights and insights to actions are shrinking due to rapidly changing dynamics in the way businesses operate now. SAS Event Stream Processing perfectly caters to this need for capturing and capitalizing streaming data | stream processing, real-time processing, sample | source | 12 |
| 89 | Causal Mediation Analysis with the CAUSALMED Procedure | Michael Lamm | Important policy and health care decisions often depend on understanding the direct and indirect (mediated) effects of a treatment on an outcome. For example, does a youth program directly reduce juvenile delinquent behavior, or does it indirectly reduce delinquent behavior by changing the moral and social values of teenagers? Or, for example, is a particular gene directly responsible for causing lung cancer, or does it have an indirect (mediated) effect through its influence on smoking behavior? Causal mediation analysis deals with the mechanisms of causal treatment effects, and it estimates direct and indirect effects. A treatment variable is assumed to have causal effects on an outcome variable through two pathways: a direct pathway and a mediated (indirect) pathway through a mediator variable. This paper introduces the CAUSALMED procedure, new in SAS/STAT 14.3, for estimating various causal mediation effects from observational data in a counterfactual framework |
mediation analysis, treatment effect, response variable, sample, proc step | source | 20 |
| 90 | Cause-Specific Analysis of Competing Risks Using the PHREG Procedure | Changbin Guo | Competing-risks analysis extends the capabilities of conventional survival analysis to deal with time-to-event data that have multiple causes of failure. Two regression modeling approaches can be used: one focuses on the cumulative incidence function (CIF) from a particular cause, and the other focuses on the cause-specific hazard function. These two quantities, unlike the hazard function and the survival function in conventional survival settings, are not connected through a simple one-to-one relationship. The Fine and Gray model extends the Cox model to analyze the cumulative incidence function but is often mistakenly assumed to be the only modeling technique available. The cause-specific approach that simultaneously models all the cause-specific hazard functions offers a more natural interpretation. SAS/STAT 14.3 includes updates to the PHREG procedure to perform the cause-specific analysis of competing risks |
hazard function, survival analysis, cause-specific hazard, proc phreg, regression analysis, proc step, competing risks, sas risk engine, hazard ratio, likelihood function, survival function | source | 18 |
| 91 | Certified Smart: Steps to Becoming SAS Certified | Mark Craver | Have you been wondering what all the buzz is about SAS certification? Is that next big job promotion hinging on you becoming SAS certified? Do you want to make yourself more marketable to potential employers by becoming a SAS Global Certified Professional? If you have been considering becoming SAS certified and are not sure just where to start, then this seminar is for you! This seminar is designed to help you get started on your journey to becoming SAS certified. In this session, we discuss the available SAS certifications in various technology areas, show you how to find more information about the certification process, and even show you the resources that are available to you as you begin your preparation to become a SAS Global Certified Professional | sample, sas 9, sas base | source | 10 |
| 92 | Change is Good, or at Least Expected: Techniques for Visualizing Categorical Values Over Time | Lisa Ann Horwitz | Many techniques exist for showing how numeric values change over time. Bar charts, line charts, plots, and many other graph types are all excellent ways to demonstrate how temperature, expenses, and other measures increase and decrease over minutes, months or decades. On the other hand, such graphs don't lend themselves to showing how and when categorical values such as grade, rating, score, and status change over time. A simple combination of data manipulation, file merging, custom formatting, and the Output Delivery System (ODS) can produce a wide range of useful, easy-to-interpret, and effective reports. By using color, fonts, custom messages, and other features to indicate a change in a data value, these reports make it easy to monitor progress or to detect when things are going in the wrong direction |
user-defined format, label statement, proc step, sample, proc print, proc sql | source | 14 |
| 93 | Claim Analytics: A Litigation Prediction Case Study | Mei Najim | In the Property & Casualty Insurance industry, advanced analytics has increasingly penetrated into each of the core business operations - marketing, underwriting, risk management, actuarial pricing, and actuarial reserving, etc. Advanced analytics has been extensively used in claim areas to predict the claims' future in order to have early intervention to improve claim outcomes and claim processing efficiency. This is another important way to grow revenue and increase profit in the insurance industry. This paper will first introduce several current common claim models in predictive analytics. Then, a claim analytics standard modeling process will be introduced. Finally, a Worker's Compensation litigation propensity model will be introduced as a case study | data mining, data cleaning, feature selection, response variable, proc hpsplit, regression analysis, logistic regression, model validation, sas enterprise guide, insurance, incurred loss, neural network, proc genmod, logistic distribution, sas base, insurance claims, model testing, univariate analysis, validation set, significance level, casualty insurance, insurer, model training, predictive analytics, stepwise selection, feature engineering, data transformation, gradient boosting, claim severity, training set, correlation, imputation | source | 18 |
| 94 | Claim Risk Scoring Using Survival Analysis Framework and Machine Learning with Random Forest | Yuriy Chechulin | The Workplace Safety and Insurance Board of Ontario is an independent trust agency that administers compensation and no-fault insurance for Ontario workplaces. Claim risk scoring can allow claims at most risk of prolonged duration to be identified. Early identification of such claims helps targeting them with interventions and tailored claim management initiatives to improve duration and health outcomes. Claim risk scoring is done using a discrete time survival analysis framework. Logistic regression with spline for time to better estimate the hazard function and interaction of a number of factors with time spline to properly address proportional hazard assumption is used to estimate the hazards and the corresponding survival probability (very sophisticated "conventional" model). In recent years, Machine Learning methods, including Random Forests (RF), started to gain popularity, especially when the emphasis of the modelling is accurate prediction | proc hpforest, logistic regression, regression analysis, logistic distribution, machine learning, survival analysis, residual, sample, response variable, hazard ratio, validation set, data cleaning, time variable, data mining, nominal variable, feature importance, sas enterprise guide, proc step, proc logistic, predictor variable | source | 15 |
| 95 | Classifying and Predicting Spam Messages Using Text Mining in SAS Enterprise Miner | Mounika Kondamudi | Presentation | data mining | source | 22 |
| 96 | Code Like It Matters: Writing Code That's Readable and Shareable | Paul Kaefer | Coming from a background in computer programming to the world of SAS yields interesting insights and revelations. There are many SAS programmers who are consultants or work individually, sometimes as the sole maintainer of their code. Since SAS code is designed for tasks like data processing and analytics, SAS developers working on teams may use different strategies for collaboration than those used in traditional software engineering. Whether a programmer works individually, on a team, or on a project basis (delivering code and moving on to the next project), there are a number of best practices from traditional software engineering that can be leveraged to improve SAS code. These practices make it easier to read, maintain, and understand/remember why the code is written the way it is. This paper presents a number of best practices, with examples and suggestions for usage in SAS | sas enterprise guide, macro function, macro variable, sas programmer, proc step, proc sql, macro definition, sample | source | 12 |
| 97 | Come On, Baby, Light my SAS Viya : Programming for CAS | David Shannon | This paper is for anyone who writes SAS 9 programs and wants to learn how to take advantage of SAS Viya's Cloud Analytics Service (CAS) engine. By the end of this paper SAS 9 programmers will be familiar with CAS sessions, libraries and the scope and reuse of those sessions. A programming patt ern for the CAS data lifecycle will be presented and, finally, a set of benchmark performance metrics are presented that illustrate the potential performance gained from CAS, compared to SAS 9 | sas viya, sas 9, proc step, sas studio, macro variable, sas log, data step, cpu time, sas library, cas | source | 14 |
| 98 | Command-Line Administration in SAS Viya | Danny Hamrick | Administration of the SAS Viya environment gives users more flexibility with the sas-admin command-line utility. You can manage custom identity groups, create folder structures, or update the configuration, all from the command line. It is well-suited for scripting common changes in this complex environment. There are even capabilities that enable content promotion for building development and test environments. The command-line utility has the following benefits: It is automatically plannable as part of the server installation process. You can download it from the web on Red Hat Linux, Microsoft Windows, and even Apple Mac. It is deployed as a stand-alone executable-there's no require installation run-time environment as for Java. There is profile support, so you can manage multiple environments from one location | sas viya, options statement | source | 12 |
| 99 | Comparisons of Large Complex Data Sets to Catch Any Changes Using SAS Enterprise Guide | Kaiqing Fan | Presentation | sas enterprise guide | source | 17 |
| 100 | Competing Risk Survival Analysis: A Novel Way to Look at Treatment Wait Times | Colleen E. McGahan | Presentation | survival analysis, competing risks | source | 56 |
| 101 | Complicated Data? SAS User Formats Can Simplify Your Code | Margaret Burgess | Presentation | source | 5 | |
| 102 | Compute Service: A RESTful Approach to the SAS Programming Environment | Joseph Henry, Jason Spruill |
SAS Viya provides an open architecture that makes it easy to include SAS analytics into your application development via REST APIs. The Compute Service API provides access directly into the SAS language programming environment. The Compute service not only allows you to execute SAS code, but also provides access to SAS Data sets, SAS Files, Output Delivery System (ODS) results, and more. This paper provides an introduction to the Compute Service API, providing examples for how to best use the many feature this API provides. Learn how the Compute Service API will make a great addition to your application development pipeline |
sas viya, rest api, sas 9, sas log, sas output, sas programming, sample | source | 20 |
| 103 | Confirmatory Factor Analysis in SAS : Application to Public Administration | Niloofar Ramezani | When hypothesizing the factor structure of latent variables in a study, confirmatory factor analysis (CFA) is the appropriate method to confirm factor structure of responses. Learning about building CFA within any statistical package is beneficial as it enables researchers to find evidence for validity of instrume nts. An instrument was developed by Wright (2016) to assess Community-Based Development Organizations' (CBDO) ability to build capacity. This study addresses the continued development of the instrument's structure using CFA in SAS 9.4 and examines how it contributes to enhancing an organization's ability to create sustainable communities. Developing and evaluating the instrument is crucial due to the growing role of CBDOs in the offering of public services, yet the significant gap that exists in the literature about organizational factors affecting CBDO's ability to build capacity | factor analysis, weighted least squares, proc step, proc calis, latent variable, maximum likelihood estimation, confirmatory factor analysis, likelihood function, factor loading, binary variable, missing value, correlation, sample | source | 13 |
| 104 | Considerations for Analysis of Healthcare Claims Data | Bradley Casselman | Presentation | source | 7 | |
| 105 | Control, Coordinate, and Create Content with SAS Drive | Cheryl L. Coyle | Do you know how to quickly share a SAS file with your colleagues? Would you like to browse through a report before opening it? Can you organize your SAS content all in one place? This paper shows you how to do these things and more with SAS Drive. We are excited to announce SAS Drive, a new tool for SAS users. With SAS Drive, you can manage all your content in one convenient location. You can search for an object that was created in a SAS application. You can share, tag, and preview your SAS content. Sharing takes only one click on the object. You decide who you want to share it with and whether they can read, edit, or share it. Tagging content can help you organize your work. Previewing files can help you find the right one. In addition to controlling your SAS content, you can drag-and-drop most content types from your local machine, and SAS Drive will upload it for you | sas model manager, sas viya | source | 10 |
| 106 | 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. will be put on the web at www.LexJansen.com. That code has the same section organization as this paper and an interested person can get that code and 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. 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 . The eleventh section adds some small tweaks to Vince DelGobbo's excellent work on how to create a hyperlinked EXCEL workbook |
proc sql, where statement, proc step, create table, proc print, select, order by, pdv, excel, full join, macro variable, dataset, xls, if-then-else, inner join, having, data merging | source | 35 |
| 107 | Copy That! Using SAS to Create Directories and Duplicate Files | Nicole Ciaccia | Presentation | source | 25 | |
| 108 | Cost Analysis in Population Health Using SAS Real World Evidence | Jay Paulson | Health care organizations are faced with the challenge of analyzing high volumes of population-level health data to gain insights into ways to reduce cost while improving health care quality and efficiency. This paper explains how to develop statistical models using SAS Real World Evidence to analyze cost and understand the key factors associated with health care costs. Using SAS Real World Evidence, a population cohort based on a research question is created. Statistical modeling is performed on this cohort using the powerful Add-in Builder available in SAS Real World Evidence. The results from statistical models are then analyzed to understand characteristics associated with cost | group by, regression analysis | source | 13 |
| 109 | Create Awesomeness: Use Custom Visualizations to Extend SAS Visual Analytics to Get the Results You Need | Robby Powell, Renato Luppi |
SAS Visual Analytics is an excellent visualization and analytics tool, but sometimes you need to add custom functionality to create the exact tool, content, or analysis your team needs. This paper looks at how to extend SAS Visual Analytics 8.2 by adding your own custom visualizations. It shows the integration of JavaScript visualization libraries such as D3 through the SAS Visual Analytics data-driven content object. You will be able to extend SAS Visual Analytics with your own custom functionality to create the customized visual analytics experience your team needs for business intelligence and data- driven decisions | business intelligence, sas visual analytics | source | 7 |
| 110 | Creating a Multi-tenant Environment Using SAS Enterprise Guide and a SAS Grid | Timothy Acton | SAS Enterprise Guide is a powerful tool with an easy to use graphical interface. Combined with SAS Grid, it becomes a scalable platform capable of serving a growing number of users. In our environment, SAS tools provide customers with access to large healthcare data through private work areas and custom data products. In a shared environment, providing external users with access to secure data is challenging. Security is paramount, but there are also challenges with scale and complexity. This paper will outline methods to provide SAS Enterprise Guide users with access to large data sources in a secure, reliable, and scalable way | sas enterprise guide, sas grid | source | 7 |
| 111 | Creating a Spatial Weights Matrix for the SPATIALREG Procedure | Alan Ricardo da Silva | The purpose of this paper is to show a macro for cr eating a spatial weights matrix for the SPATIALREG procedure. The new PROC SPATIALREG estimates spatial regression models by including in its structure the effect of spatial proximity, represented in an × matrix known as W matrix. However, this W matrix cannot be created directly in the procedure. Thus, this paper will show some ways to do that using Has h Objects and PROC IML |
regression analysis, hash table, proc step, macro call | source | 17 |
| 112 | Creating a Successful Data Science Program: A Joint Academic and Industry Perspective | Krzysztof Dzieciolowski | There has been a surge of Data Science programs offered by various business schools in North America and around the world in the last three years. Some of these programs are accessible in a classroom setting, whereas others are given online. Most of them cover basic concepts and algorithms and are offered in combination with software training. However, the cost of these programs has skyrocketed triggering confusion surrounding the value of these programs. A successful Data Science program must include extensive and challenging applications developed from real world business and other disciplines. The teaching instruments and pedagogy need to be adapted to the unique theoretical and applied requirements of the modern Dat a Science education. In the talk, I will discuss unique challenges facing Data Science teaching, as well as discuss ideas to create for students an effective classroom experience | data science, sample | source | 6 |
| 113 | Credit Score: A Comparison of Gradient Boosting with Logistic Regression in Practical Cases | Paulo Di Cellio Dias, Marc Witarsa |
The usage of boosting methods are increasing every day with the constant evolution of software and hardware infrastructure and their cost reduction. However, we still facing some problems and issues, mainly when we refer to automatic processes, where in most cases you cannot interpret their parameters (the influence of each variable).This paper evaluates one of the main methods of boosting, gradient boosting, and its use in s coring models. We also evaluate the main methodology used today for s coring models, logistic regression, in order to compare the results with the boosting process. We present three real cases of models from the largest credit bureau in Brazil (Serasa Exper ian), and evaluate the main results, considering simulations of boosting and logistic regression. Finally, we analyze the results and indicate the pros and cons of using new methodology. Keywords: PROC treeboost, PROC GRADBOOST, gradient boosting, credit score,collection score, fraud score, Machine Learning |
gradient boosting, sample, logistic regression, regression analysis, logistic distribution, machine learning, data cleaning, proc step, proc hpsplit, response variable, outlier, model training | source | 25 |
| 114 | Customizing the Aggregation of Risk Data | Scott Gray, Stacey M. Christian |
You asked for real-time and on-the-fly queries of risk data, and SAS answered with SAS High- Performance Risk. Now with new and changing regulations such as the FRTB (Fundamental Review of the Trading Book) and SIMM (Standard Initial Margin Model), you need customizable queries. Adding to its established evaluation-stage actions, SAS High-Performance Risk now offers you control over how your query aggregates data. You can customize exposure calculations, weight aggregations, perform correlated aggregations, and calculate other nonlinear measures | correlation, proc step, variable list | source | 18 |
| 115 | Data Integration Best Practices | Harry Droogendyk | The creation of and adherence to best practices and standards can be of great advantage in the development, maintenance, and monitoring of data integration processes and jobs. Developer creativity is always valued, but it is often helpful to channel good ideas through process templates to maintain standards and enhancement productivity. Standard control tables are used to drive and record data integration activity. SAS Data Integration Studio (or Base SAS) and the judicious use of auto -call utility macros facilitate data integration best practices and standards. This paper walks you through those best practices and standards | data cleaning, raw data, autocall macro, data integration, record count, accuracy, flat file, proc sql, macro variable | source | 17 |
| 116 | Data Management in SAS Viya : A Deep Dive | Wilbram Hazejager | This paper provides an in-depth look into the new SAS data management capabilities in support of SAS Viya and SAS Cloud Analytic Services. The paper includes an overview of SAS Data Management in SAS Viya, and contains details about how the feature set integrates with SAS Cloud Analytic Services. Examples and usage scenarios of how to best leverage the technology are also included | sas viya, cas action, cas | source | 9 |
| 117 | Data Management Meets Machine Learning | Gregory S. Barnes Nelson | Machine learning, a branch of artificial intelligence, can be described simply as systems that learn from data in order to make predictions or to act, autonomously or semi-autonomously, in response to what it has learned. Unlike pre-programmed solutions or business-rules-engines, machine learning can eliminate the need for someone to continuously code or analyze data themselves to solve a problem. While there are a variety of applications of machine learning, and the more advanced "deep learning", most have been focused on machine learning that trains a computer to perform human-like tasks, such as recognizing speech, identifying images (or objects and events portrayed therein) and in making predictions. In this paper, we will explore the use of machine learning as an approach to helping with upstream activities in data management including classification and feature identification, as well as discuss implications for data quality, data governance and master data management | machine learning, artificial intelligence | source | 16 |
| 118 | Data Quality Control for Big Data: Preventing Information Loss with High-Performance Binning | Deanna Naomi Schreiber-Gregory | It is a well-known fact that the structure of real-world data is rarely complete and straight- forward. Keeping this in mind, we must also note that the quality, assumptions, and base state of the data we are working with has a very strong influence on the selection and structure of the statistical model chosen for analysis and/or data maintenance. If the structure and assumptions of the raw data are altered too much, then the integrity of the results as a whole are grossly compromised. The purpose of this paper is to provide programmers with a simple technique which will allow the aggregation of data without losing information. This technique will also check for the quality of binned categories in order to improve the performance of statistical modeling techniques. The SAS high performance analytics procedure, HPBIN, gives us a basic idea of syntax as well as various methods, tips, and details on how to bin variables into comprehensible categories | proc step, interval variable | source | 9 |
| 119 | Data Science and SAS : A Data Science Perspecitve | Richard W. La Valley | Presentation | data science, data mining, data warehouse, sas enterprise guide, big data | source | 32 |
| 120 | Data Visualization from SAS to Google Maps on Microsoft SharePoint | Xiaogang Tang | Google Maps is a very popular web mapping service developed by Google. Microsoft SharePoint is a popular web application platform and used for content management by companies and organizations. Connecting SAS with Google Maps and SharePoint combines the power of these three into one. As a continuation of my SAS Paper 1062-2017 "Data Visualization from SAS to Microsoft SharePoint", this paper expands on how to implement geocoding and data visualization from SAS to Google Maps on Microsoft SharePoint. The paper shows users how to use SAS Procedures to create and send XML data files from SAS to SharePoint Document Library. The XML data files serve as data feeds for Google Maps web pages on SharePoint and SAS code example is included. A couple of examples with different views on data visualization from SAS to Google Maps API on SharePoint are provided | data visualization, data mapping, data wrangling | source | 13 |
| 121 | Data-Driven Marketing Strategies for an Online Retailer | Jaideep Muley | Presentation | source | 4 | |
| 122 | Data-Driven Programming Techniques Using SAS Metadata | Kirk Paul Lafler | Data-driven programming, or data oriented programming (DOP), is a specific programming paradigm where the data, or data structures, itself controls the flow of a program and not the program logic. Often, data-driven programming approaches are applied in organizations with structured data for filtering, aggregating, transforming and calling other programs. SAS users can easily access metadata content to capture valuable information about the librefs that are currently assigned, the names of the tables available in a libref, whether a data set is empty, how many observations are in a data set, how many character versus numeric variables are in a data set, a variable's attributes, the names of variables associated with simple and composite indexes, and much more. The value of accessing the content of the contents of these read-only SAS metadata data sets called DICTIONARY tables or their counterparts, SASHELP views, is limitless | dictionary tables, proc sql, proc step, sas log, proc contents, sas library, select, options statement | source | 11 |
| 123 | Database-Ics II: Design, Standardize, and Control the Process for Better Data Management | Frank A. Ferriola | Developmental design is an art, and too many times we dive right into the coding and the results instead of taking the time to think through the process. This paper examines the entire development process and shows the audience different facets to think about during development. Areas covered are design, standardization, and control tables, as well as SAS tools that can be used to make your process more efficient. This paper updates concepts presented in "Database-ics: A Primer for Creating a Usable Database in SAS", which I presented in 2005 at MidWest SAS Users Group (MWSUG), Western Users of SAS Software (WUSS), and SAS Users Group International (SUGI), and adds additional concepts | data dictionary, data governance | source | 6 |
| 124 | Democratizing Big Data: How to Turn Clickstream Data from Google Analytics into Something Useful | Jennifer Seid | When one hears the phrase "Big Data" it is often in the context of financial data, genome mapping, or meteorological studies, but big data also exists in the webosphere. With more information about user behaviors, brands and advertisers can serve hyper-personalized content to every individual. With the sheer amount of data available from digital user behavior, many of us are turned off from participating in this new frontier of marketing because we do not have the background necessary to collect, clean, and run the types of statistical analyses needed, much less the resources to do so (or so we think). After wrestling with this very problem, our team figured out a way to use Google Analytics (GA) to mine clickstream data for free, extract it using SAS, and turn it into beautiful dynamic visuals powered by open-source D3 to bring clickstream analysis to the people. Code was developed with SAS 9.4 TS1M4 running under Windows 10 | advanced sas, big data | source | 7 |
| 125 | Demystify Income Models with Advanced Machine Learning Techniques for Ultimate Accuracy | Vickey Chang | Consumer IncomeView is the Equifax next-gen income estimation model that estimates a consumer's individual annual wage income. This model provides an optimal income solution to our clients by incorporating premier multi-source data assets and advanced machine learning modeling techniques. As a result, the new model significantly improves the scorable population rate compared with the older models and significantly enhances the prediction accuracy . The results of Consumer IncomeView ha ve been successfully validated by an array of new proprietary accuracy metrics for model performance measurement, both on internal out -of-time and clients' data. This paper is to describe the design, development and main results of the model | predictor variable, regression analysis, proc reg, accuracy, response variable, data cleaning, sample, neural network, proc adaptivereg, c-statistic, backward selection | source | 18 |
| 126 | Demystifying Buzzwords: Using Data Science and Machine Learning on Unsupervised Big Data | Ben Murphy | Each month it seems there is a new technology introduced that can transform your organization in unimaginable ways. Each technology arrives with its own set of industry buzzwords that make it difficult to understand how your organization would benefit. We will go through the meaning of the buzzwords associated with machine learning, data science, artificial intelligence, and advanced analytics. We can immediately apply this knowledge to a discussion about how a team of data scientists can follow a process guided by answering business questions using the best tools for each task. We review the relative strengths and weaknesses of the various tools, techniques, and technologies associated with these buzzwords. You will walk away a winner by knowing how to put each to its best use | machine learning, artificial intelligence, big data, data science | source | 10 |
| 127 | Deploying and Maintaining Models in a Big Data Environment: An Intelligent SAS Workflow | Anvesh Reddy Minukuri, Ramcharan Kakarla, Sundar Krishnan |
Creating predictive models is one element of the data mining. Implementation and maintenance are another ones. Mostly, We will have two differ ent kinds of data which is real-time and historical data . Historical data is given priority over real-time because of its huge volume and past proven performance. Moreover, real-time needs to predict/capture the data within range of milliseconds and make recommendations using huge-computations and quick Algorithm API 's. This paper focuses on the historical data model deployments and maintenance to store the models on Hadoop. With the advent of big data ecosystem, it is critical for organizations to create a coherent SAS workflow that can work with different analytical platforms. With different organizations using the different platforms, ability to execute diverse models with independence, traceability, and reusability becomes imperative | flat file, proc freq | source | 8 |
| 128 | Deploying SAS Grid Systems on VMware ESXi Virtually Provisioned Storage | Tony Brown | Creating performant SAS file systems that are compliant with VMware ESXi can be a challenge. There are several ways to implement virtual and software-defined storage resources within a VMware ESXi hypervisor controlled system. This paper covers several considerations for file system and logical unit number (LUN) construction from several perspectives: host logical volume, virtual machine file system (VMFS) and virtual machine disk (VMDK), and VMFS and raw device mapping (RDM) implementations. Also discussed are non-LUN mounts to hyper-converged storage systems. The focus is on tuning and performance of the method used, as well as pros and cons between hypervisor-level, and back-end storage-level utility operations | sas grid, character functions | source | 13 |
| 129 | Detecting Defective Equipment in the Healthcare Service Industry | Sean Cohen, Sarith Mohan |
Healthcare equipment service companies confront the challenge of how to identify equipment showing repeated patterns of service events in order to minimize cost to the supplier, downtime for the customer and significant delay in the treatment of patients. Current methodologies employ sensors to detect large variations and alert the service provider before failure or break down. Since time is of the essence, the engineers scramble to make a quick break fix without looking at a holistic view of the problem and the history of the machine. This results in prolonged repair cycles, causing excessive stress to both the service provider and customer; and impacting the reputation of the company. Using modern visualization tools like SAS Visual Analytics and JMP, we are able to apply meaningful visualizations to analyze large volumes of service event data | service provider, regression analysis | source | 5 |
| 130 | Determine Differences in Health Care Costs Before and After Alzheimer's Diagnoses Using SAS Studio | Catherine Olson, Thomas Iverson |
Presentation | source | 6 | |
| 131 | Developing BI Best Practices: Texas Parks and Wildlife's ETL Evolution | Drew Turner | The development of extract, transform, load (ETL) processes at Texas Parks and Wildlife has undergone an evolution since we first implemented a SAS Business Intelligence (BI) system. We began constructing our data mart with subject matter experts who used SAS Enterprise Guide and who had a need to share that data among less-experienced staff in our user community. This required us to develop best practices for assembling a data mart architecture that maximized ease-of-use for consumers, while offering a secure reporting environment. We also developed best practice guidelines for determining whether data was best served to and from the data mart via physical SAS tables, information maps, OLAP cubes, or stored processes. Originally, we used Microsoft Task Scheduler to automate SAS Enterprise Guide processes and developed best practices in designing process flows to run on an automated schedule | proc sql, sas enterprise guide, excel, odbc connection, options statement | source | 12 |
| 132 | Developing Business Intelligence Best Practices: Lessons Learned from a Successful BI Implementation | Drew Turner | When Texas Parks and Wildlife initiated the implementation of a SAS Business Intelligence (BI) system, it was driven by the business units, and not fully supported by our Information Technology division. Although our implementation team, primarily consisting of staff with backgrounds in biology and accounting, had to learn "on the job" about how to get a complex information technology project off the ground, this project has been successful, and has radically transformed and improved the efficiencies of many aspects of our business processes. There were many lessons learned by our team along the way, and this paper will highlight some of the best practices we developed, as well as several tips and tricks we discovered and would recommend for anyone attempting to implement a SAS BI installation | dataset, financial reporting, data mart, proc sql, data warehouse, excel, business intelligence, sas enterprise guide | source | 20 |
| 133 | Development of an Individual-Level Social Determinants of Health (SDoH) Classification Model | Ryan C. Butterfield, Paul A. LaBrec |
Growing evidence in the health services literature suggests that social determinants of health (SDoH) can affect a person's risk for adverse health events and increased cost of care. Individuals can be scored on those factors associated to SDoH measures that are individual in nature and weights can be created that can be used for generating greater insight into risk, costs, and health care utilization patterns. To develop this prototype model, data elements will consist of Z Codes from the claims data of a large Midwest Medicaid payer. Z Codes were created as a new set of supplemental codes with the advent of ICD-10 and are physician-coded attributes at a person level. We examine the subset of Z Codes (55-65) which indicate "Persons with potential health hazards related to socioeconomic and psychosocial circumstances." These are set as binary indicators at the individual subject level. A subset of the Z Codes is selected for use in this analysis | insurance claims, likelihood function, latent variable, risk adjustment, proc princomp | source | 13 |
| 134 | Diamonds in the Rough: New Discoveries with the SAS Visual Investigator Text Analytics Workspace | Danielle Davis | Take your investigative process to a new dimension and incorporate the analysis of your unstructured data. Adding text analytics to your process gives you the potential to discover new relationships between existing or newly discovered entities. Discover new locations, organizations, or people that can further your investigative process. Uncover new patterns of common phrases that were previously undetected. Let the analytics reveal common themes that might cluster the unstructured data into meaningful results. The new "Text Analytics Workspace" in SAS Visual Investigator provides these features to enhance your investigation | bar chart, account | source | 15 |
| 135 | Discovering Insightful Relationships inside the Panama Papers Using SAS Visual Analytics | Stephen M. Overton | Network analytics is a broad methodology which supports the desire to perform link analysis through visual tools such as SAS Visual Analytics, SAS Social Network Analysis, and SAS Visual Investigator. Link analysis visually displays all possible relationships which exist between e ntities, based on data available, to provide insight into direct and indirect associations. This can be a very helpful tool to support an investigation as a part of a fraud or anti-money laundering investigation process. Beneath the surface, data management techniques and advanced analytical routines are used to discover relationships, transform data and build the appropriate data structures to support link analysis. Network statistics can help describe networks more accurately by using quantitative data to define complexities and unusual connections between entities. This paper will explore an approach to support network analytics and link analysis by using the Panama Papers as a real-world example | link analysis, financial crime | source | 21 |
| 136 | Distribution Circuit Load Forecasting Using Advanced Metering Infrastructure Data | Prasenjit Shil, Tom Anderson |
The ability to perform very short to very long-term forecasts of distribution circuit loads at intermediate distribution circuit locations between customer meters and substation feeder buses using AMI (Advanced Metering Infrastructure) data provides significant advantages to distribution system planners and operators in a number of areas. Some of the important applications of these forecasts include anticipation of device overloads, facilitation of switching operations, and helping with DER (Distributed Energy Resources) integration into system operations. This session presents forecasting results for distribution circuits using SAS Energy Forecasting, which uses methods such as GLM to generate forecasts. Results for different circuit locations are derived from Ameren Illinois AMI and circuit taxonomy data. Included in the presentation are details of the forecasting methodology and a discussion of applications to distribution system operations | outlier, time series analysis, mape, proc genmod, champion model, data cleaning, missing value, proc step, predictor variable | source | 12 |
| 137 | Do You Need to Put Your Data on a Diet? | Howard Plemmons Jr., Priya Sharma |
You have amassed an amazing amount of data in your Hadoop environment in a relatively short amount of time. Unfortunately, you realize that all of this data cannot be processed in the environment in which it is contained. You have come up with alternatives that cannot be presented to the executive sponsors of this data rich environment. What's next? Many have traveled the path that you are now on. This presentation will address the concept of how to do more with less by coming up with a diet plan for your data. Big data examples will be discussed along with analytical concepts used to trim the fat | proc step, feature selection, sas viya, proc princomp, big data, proc varreduce, proc hpsplit, data wrangling, response variable, feature importance, proc hpforest, proc freq, proc hpreg | source | 7 |
| 138 | Docker Toolkit for Data Scientists: How to Start Doing Data Science in Minutes! | Donna De Capite | SAS continues to grow its capabilities of running SAS in the cloud with containers! Learn what a container is and how it can be used to run SAS Analytics for Containers and leverage the power of in- memory compute capabilities with SAS Viya. This paper discusses how SAS containers run in a variety of cloud platforms including Amazon Web Services, Google Cloud Platform, Microsoft Azure, as well as how they run on a private OpenStack cloud using Red Hat OpenShift. Additional topics include provisioning web-browser based clients via Jupyter Notebooks and SAS Studio to empower data scientists with the tool of their choice, and how to take advantage of working with SAS Analytics for Containers with Hadoop | sas studio, libname statement, sas viya, data science, red hat | source | 29 |
| 139 | Doin' Data Quality in SAS Viya | Brian Rineer | SAS Viya introduces data quality capabilities for big data through Data Preparation and DATA STEP programming for SAS Cloud Analytic Services (CAS). In this session, a Senior Software Development Manager at SAS shows how to configure SAS Data Quality transformations in SAS Data Studio and how to submit DATA STEP functions that are created in SAS Data Quality for execution in CAS. We also cover management of the vital SAS Quality Knowledge Base in SAS Environment Manager |
sas viya, data cleaning, cas action, cas | source | 9 |
| 140 | Drug Abuse: Is the Number of Breweries Playing A Role? | Su Li, Nilam Adhikari |
Presentation | source | 15 | |
| 141 | Easing into Data Exploration, Reporting, and Analytics Using SAS Enterprise Guide | Marje Fecht | Whether you have been programming in SAS for years, are new to it, or have dabbled with SAS Enterprise Guide before, this hands-on workshop sheds some light on the depth, breadth, and power of the Enterprise Guide environment. With all the demands on your time, you need powerful tools that are easy to learn and deliver end-to-end support for your data exploration, reporting, and analytics needs. Included are the following: data exploration tools formatting code-cleaning up after your coworkers enhanced programming environment (and how to calm it down) easily creating reports and graphics producing the output formats you need (XLS, PDF, RTF, HTML, PPT) workspace layout * productivity tips. This workshop uses SAS Enterprise Guide 7.1, but most of the content is applicable to earlier versions | sas enterprise guide, bar chart, scatter plot, vertical bar chart, macro variable, proc freq, proc step | source | 18 |
| 142 | Economic Capital Modeling with SAS Econometrics | Mahesh V. Joshi | A statistical approach to developing an economic capital model requires estimation of the probability distribution model of the aggregate loss that an organization expects to see in a particular time period. A well-developed economic capital model not only helps your enterprise comply with industry regulations but also helps it assess and manage risks. A loss distribution approach decomposes the aggregate loss of each line of business into frequency and severity of individual loss events, builds separate distribution models for frequency and severity, and then combines the two distribution models to estimate the distribution of the aggregate loss for each business line. The final step estimates a copula-based model of dependencies among the business lines and combines simulations from the dependency model with the aggregate loss models of individual business lines | sample, claim cost model, proc freq, proc step, commercial banking, economic capital, probability distribution, loss event, retail banking, proc means, dataset, operational risk, poisson distribution, likelihood function, macro definition | source | 19 |
| 143 | Effective Management of Non-performing Loans Using SAS Credit Assessment Manager | Sumanta Boruah, Satish Garla |
High levels of nonperforming loans (NPL) or bad debt are problematic both to the bank and to the economy. NPLs from the 2008 financial crisis have persisted, and there is evidence of steady growth. Banks are under pressure to devise strategies to manage NPL portfolios. Financial regulations, such as IFRS 9, U.S. CECL and BCBS guidance on accounting for credit losses, have proposed key principles and pragmatics to identify, measure, and manage NPLs. Banks are in need of a solution to meet the systemic requirements of an NPL strategy-specifically, individual assessment of significant nonperforming exposures and high-risk performing loans (also called "watch-list"). However, given the high volume, individual credit assessment of loans without resulting in delayed expected credit loss (ECL) recognition is a challenge that banks are facing now. SAS Credit Assessment Manager provides a framework for both qualitative and quantitative assessment of NPLs individually | ifrs 9, npl, expected loss, credit risk, stochastic economic scenario | source | 11 |
| 144 | Efficient Elimination of Duplicate Data Using the MODIFY Statement | Paul M. Dorfman | Many an ETL transformation phase starts with cleaning the extracted data from "duplicate" data. For example, transactions with the same key but different dates may be deemed "duplicate", and the ETL needs to choose the latest transaction. Usually, this is done by sorting the extract file by the key and the date and then selecting the most recent record. However, this approach may exact a very high processing cost if the non-key (aka the satellite) variables are numerous and long and especially if the result needs to be resorted to bring the extract into the original order. This paper shows that this kind of data cleansing can be accomplished by using a principally different algorithm based on merely marking the duplicate records for deletion in the original extract file. In real-life scenarios, the proposed approach, particularly when the "duplicates" are relatively rare, may result in cutting the processing time by more than an order of magnitude | modify statement, work library, hash table | source | 7 |
| 145 | Efficient Use of Disk Space in SAS Application Programs | Thomas E. Billings | A tutorial on managing disk space for SAS data sets and files created by or for the SAS system. Basic housekeeping is covered: keep files that are in-use and backup or discard files that are not in use. Backup methods are discussed, including the important question whether the operating system that your SAS site runs on might change in the future, necessitating use of the special transport format for backup files. SAS procedures that are commonly used for disk file management are described: PROC DELETE, DATASETS, and CATALOG. SQL DELETE and SAS DATA STEP functions for file management are also discussed. File compression is a very important tool for saving disk space, and the SAS features for this are described. Logical deletion of rows in a data set can waste disk space; prototype SAS code to detect files with this condition is supplied in an appendix |
proc step, work library, proc sql, character variable, options statement, dataset, proc cport, numeric variable, constraint, proc format, proc delete, proc contents, catalog, sas enterprise guide, delete | source | 14 |
| 146 | Efficiently Join a SAS Data Set with External Database Tables | Dadong Li, Michael Cantor |
Joining a SAS data set with an external database is a relatively common procedure in research, and there are several methods to improve its performance. If only one external database table is involved, and the join is an inner join, using the dbkey data set option should be quick and easy. The second method is to generate a macro variable based on the SAS data set, which can then be used to abstract data. Since the macro variable works in both implicit and explicit SQL pass-through connections to the external database, this method is very flexible. If an analyst or an investigator has the permission, he could send the SAS data set into the database as a permanent table and use it like other database tables for data abstraction. However, in most cases, data analysts or investigators are not allowed to perform this kind of operation due to security or other reasons, limiting its use in the real work environment | dataset, macro variable, create table, proc sql, proc step, inner join, sql pass-through | source | 6 |
| 147 | Enable Personal Data Governance for Sustainable Compliance | Vincent Rejany | In the context of European Union's General Data Protection Regulation (EU GDPR), one of the main challenges for data controllers and data processors is to demonstrate compliance by documenting all their data processing activities and where appropriate, to assess the risk of these processes for the individuals. Such requirements cannot be achieved without being able to build an efficient data governance program combining Legal driven top-down activities through personal data compliance and IT driven bottom-up operations through personal data mapping including personal data categories definition and discovery. We use several processes developed in SAS Data Management Studio to identify the personal data and update the governance view within SAS Business Data Network and SAS Lineage | data protection, data governance, data mapping, data cleaning, data profiling, data wrangling, rest api, sas library | source | 24 |
| 148 | Enhancing Subscription-Based Business by Predicting Churn Likelihood | Smitha Etlapur | Customer retention is a challenge faced by most businesses in today's competitive market. Predicting customer churn would help a subscription business such as KKBox in creating substantial difference in their revenue stream. This paper describes work relating to predicting churn likelihood using SAS 9.4, SAS Enterprise Miner for data cleaning, preparation and analysis | data cleaning, likelihood function, customer retention, cluster analysis, churn, data mining | source | 12 |
| 149 | Enterprise, Prepare for Analysis! Using SAS Data Management to Prepare Data for Analysis | Bob Janka | Data analysis teams often have few resources for many needs. Using SAS Data Management, we can integrate enterprise-level data from different sources, cleanse that data, and enrich it for analysis. These steps reduce the workload for the analysis team, enabling them to focus on the actual analytics. This paper highlights some uses of enterprise-level tools in SAS Data Integration Studio and SAS Data Quality in data preparation. Several of the techniques shown are also applicable to users of Base SAS (with the SAS macro facility) | proc step, raw data, create table, proc sql, macro definition, missing value, proc standard, sas base, data wrangling, proc means, proc rank, data science | source | 17 |
| 150 | Estimation of Correlation Coefficient in Data with Repeated Measures | Katherine Irimata, Paul Wakim, Xiaobai Li |
Repeated measurements are commonly collected in research settings. While the correlation coefficient is often used to characterize the relationship between two continuous variables, it can produce unreliable estimates in the repeated measure setting. Alternative correlation measures have been proposed, but a comprehensive evaluation of the estimators and confidence intervals is not available. We provide a comparison of correlation estimators for two continuous variables in repeated measures data. We consider five methods using SAS/STAT software procedures, including a naïve Pearson correlation coefficient (PROC CORR), correlation of subject means (PROC CORR), partial correlation adjusting for patient ID (PROC GLM), partial correlation coefficient (PROC MIXED), and a mixed model (PROC MIXED) approach. Confidence intervals were calculated using the normal approximation, cluster bootstrap, and multistage bootstrap |
correlation, confidence interval, proc mixed, bootstrap, partial correlation, repeated measures, sample, proc step, longitudinal data, covariance, auc, macro definition, pearson correlation, proc corr, repeated measures model, interval variable, macro call, null hypothesis | source | 19 |
| 151 | Evaluating the Accuracy of Clinical Prediction Models for Binary and Survival Outcomes | Joseph C. Gardiner | Clinical prediction models employ regression-based methods to elucidate potential predictors of outcomes. For a binary outcome, the LOGISTIC and HPLOGISTIC procedures offer options for model development, testing and validation. Several fit statistics can be used to gauge the predictive accuracy of a model as well as for comparisons between competing models. In the appropriate context these statistics include sensitivity, specificity, positive and negative predictive values, the receiver operating characteristic (ROC) curve and concordance indices. A similar development for survival models faces many challenges, including the feature of time-dependent outcome and censoring in accrued survival data. New options in the PHREG procedure permit calculation of some of the aforementioned fit statistics. We discuss their interpretation and illustrate their application with empirical data sets |
roc curve, logistic distribution, confidence interval, proc calis, type i error, proc means, kaplan-meier estimator, bootstrap, resampling, competing risks, logistic regression, proc phreg, sample, regression analysis | source | 21 |
| 152 | Evaluation of Different Approaches to Reject Inference: a case study in Credit Risk | Sergiu Luca | In the age of Internet of Things (IofT), variables with missing values are part of the norm. With the current available processing power and software that niftily capitalizes on this trend (SAS being an example of such), hundreds of variables can be considered for a model. Even as such raw data is aggregated and combined in more elegant forms (ex. a ratio of a 12-month averages), the missing value problem tags along nonetheless. This issue is especially pernicious when it comes to the target/explained variable. In Credit Risk Modelling, this is the case for good/bad performance for rejected applications. Such applications are part of the adjudication scorecard dataset. Different approaches are currently in circulation to deal with this data inconvenience. Papers and book chapters discussing these approaches point oftentimes at the scarcity of empirical work related to this problem. The scarcity is indeed intrinsic to the problem | sample, proc logistic, logistic distribution, proc step, missing value, credit risk, training set, proc rank | source | 9 |
| 153 | Examining the Drivers of Hospital Re-admission of Type-2 Diabetic patients | Shashank Reddy Gudipati | In an uncertain business environment, there is an increasing demand by healthcare companies for data and resources about patient's health. Analyzing this data and building models will help healthcare service providers to improve their efficiency and effectiveness. Healthcare industry is moving from only reporting facts out of this data to discovery of insights and predicting prospective trends by studying past patterns. Diabetes Mellitus is one of the major health hazards in developing countries. A large pr oportion of patients, diagnosed with type-2 diabetes, readmit in hospitals after their first admission. These readmissions are expensive and are caused generally by either patient's bad health or because of bad healthcare provider. Predicting these potential readmissions can help in improving patient care, quality of care by health care providers | proc hpsplit, hazard function, logistic regression, cumulative hazard, data wrangling, feature selection, data mining, logistic distribution, regression analysis, data cleaning | source | 9 |
| 154 | Excelling to Another Level with SAS | Matthew J. Kastin, Arthur S. Tabachneck, Tom Abernathy |
Have you ever wished that with one click (or submitting one macro call) you could copy any SAS dataset, include or exclude variable names or labels, automagically create or modify EXCEL workbooks, or paste tables into files? Or how about doing any of the above but, at the same time, create Pivot Tables and/or, if desired, base new worksheets on existing EXCEL templates or preformatted workbooks so that you don't have to spend time duplicating such things as font usage, effects, formulas, highlig hting and/or graphs? You can and, with just base SAS, there are some little known but easy to use methods that are available for automating many of your (or your users) common tasks |
excel, xls, proc export, proc step, label statement, macro variable, dataset, class variable, data step, %if-%then-%else, macro loop, macro definition | source | 36 |
| 155 | Experiences and Pitfalls Establishing a Smart Data Lab and Transferring Prototypes into Production | Christian Wrobel | In March 2015, Fraport AG in Frankfurt carried out an experimental Smart Data Lab (SDL). For the first time, experts from a variety of departments worked together in a laboratory situation. They defined four concrete problems to be solved with analytics, using a huge collection of data from different sources within the company. The effective communication of problems and results helped promote the acceptance of the lab's data based recommendations. Analytics and the use of huge amounts of real-time data which were integrated into the IT systems for the first time, allowed for the correction of some established decision rules. Most of all, not only the four problems were solved and the business processes behind were improved, but also several new data-based business ideas with cross-departmental impact were generated. Results from the Smart Data Lab were widely noticed and accepted by Fraport's executive board, which in turn has decided to make SDL a permanent institution | big data, problem solving | source | 4 |
| 156 | Exploring Web Services with SAS | Richard Carey | Web services are the building blocks of the APIs that drive the modern web. From Amazon Web Services to Philips Hue light bulbs, web services enable almost limitless integration. In this paper, we explore what a web service is and how we can use them via SAS. We look at mechanisms to call web services from SAS using the HTTP and SOAP procedures, how to create web services from SAS Stored Processes using SAS BI Web Services, and consider the future of web services and SAS with SAS Viya, along with some practical examples | web service, proc http, sample, filename statement, proc print, macro variable, proc step, libname engine, macro quoting function | source | 19 |
| 157 | Extending the Capability of SAS Forecast Studio to Enable Custom Exception Handling | Divya Guru Rajan | SAS Forecast Studio allows for the generation of a large number of forecasts in a fast and automated way. However, examining forecast exceptions can become very time-consuming. This paper demonstrates how to automate the process of selecting alternate models and adjust forecasts for different types of exceptions. See how our method uses model related datasets and catalogs generated as part of a Forecast Studio project based on data from a large fashion retailer. In a programmatic way, alternate models are selected and the related datasets are updated. The resulting alternate model selections can be viewed in Forecast Studio. This paper describes the exception types addressed, the overall process flow for selecting forecast exceptions, and the method used to select alternate models. In addition, out-of-sample accuracy results demonstrate how applying this approach improves forecast results | feature selection, sample | source | 7 |
| 158 | External Databases: Tools for the SAS Administrator | Mathieu Gaouette | The SAS Management console administration tool allows the configuration of several external data sources for end users. The literature often diminish the complexity and importance of the configuration aspect of external databases making this task somehow ambiguous. Most administrators find themselves overwhelmed by the number of options available to them when configuring these sources. As it is possible to fly through the configuration process by choosing the default values, the resulting database access can easily be flimsy or have sub- par performance. This paper provides the administrator with a complete set of tools that will help them define from scratch an external database. It will also help optimize the available options (standards and advanced) in order to make the most out of that connection | proc sql, sas management console, proc step, sas enterprise guide, libname statement, dataset, sql pass-through, options statement | source | 13 |
| 159 | Extracting Data from Zipped XML Files with the ZIP and XML FILENAME Options in SAS | Maura Bardos, Andrew Thomson |
Presentation | source | 4 | |
| 160 | Extracting the Embedded Git Repository from a Project in SAS Enterprise Guide 7.1 | Shahriar Khosravi | Presentation | sas enterprise guide | source | 11 |
| 161 | Factors versus Clusters | Diana D. Suhr | Factor analysis is an exploratory statistical technique to investigate dimensions and the factor structure underlying a set of variables (items) while cluster analysis is an exploratory statistical technique to group observations (people, things, events) into clusters or groups so that the degree of association is strong between members of the same cluster and weak between members of different clusters. Factor and cluster analysis guidelines and SAS code will be discussed as well as illustrating and discussing results for sample data analysis. Procedures shown will be PROC FACTOR, PROC CORR alpha, PROC STANDARDIZE, PROC CLUSTER, and PROC FASTCLUS |
cluster analysis, proc step, factor analysis, proc cluster, proc fastclus, hierarchical clustering, correlation, factor loading, latent variable, sample, agglomerative clustering, distance measure, residual, euclidean distance, proc tree, normal distribution, proc corr, proc factor, dataset, standard deviation, proc freq, scree plot, proc princomp | source | 16 |
| 162 | Files Arriving at an Inconvenient Time? Let SAS Process Your Files with FILEEXIST While You Sleep | Matthew Shotts | Presentation | source | 9 | |
| 163 | Find Your SAS Sensei 先生 | Allan Bowe | In martial arts, a sensei (先⽣生) is one with more experience who can guide you along the path. When practicing the art of SAS, it is wise to pay heed to the SASensei - for many paths log to darkness. Fortunately, there are many communities to be found. The web is overflowing with gurus - you may even have read it somewhere. Professionals can be found so long as you are in the right links. Rookie's are weLcomed every year! Don't be a twit - as a last resort, you can even find Support. Experience new possibilities in your SAS, with the power of knowing how to find right resources for your needs. Join us to level up your ranking, with not a training course or webex in sight! No matter what your experience level, you can always improve. Those who can, teach | sas base, macro definition | source | 5 |
| 164 | Finding and Generalizing a 'Best Before' Date | Marje Fecht | Do you need a "Best Before" date 18 months from manufacture, and represented as a month end date? Do you manually provide dates as input to your processes? Do you struggle to get dates into the right format for database queries, or for reports and dashboards? This presentation will help you find the right date, and then generalize the coding to avoid manual input, repetitive and messy coding, and frustration. Examples emphasize the easy manipulation of dates, and focus on generalization to support flexible coding, including: Dynamically identifying date ranges, such as reporting and analytics periods (current calendar year; most recent 6 months; past 90 days; current fiscal year; year over year) Dynamically generating field names that represent date values or ranges Controlling the appearance of date values in reports Generating date-time stamps for file names, without special symbols | date variable, sysdate9, macro function, macro variable, calendar year | source | 9 |
| 165 | Finding the Best Tools for SAS Programs Change Management within a Regulated Environment | Sy J. Truong, Pradheep Raman |
Change management within regulated environments among a multi-functional team, over different projects can pose many challenges. Recent enhancements to SAS Enterprise Guide have added new debugging features along with tools to monitor and maintain audit trails of SAS programs. These features are easy to use and powerful. There are also other tools apart from SAS such as GitHub that can also be used to manage changes to SAS programs. This paper will evaluate all the tools mentioned to find the best solution. Some of the criteria included in this evaluation include: 1. Ease of Use - How easy is it for users to gain program change management capabilities. 2. SAS Environments - Apart from an EG project file, can it adapt to SAS files in batch mode or stored processes. 3. Configuration and Administration - Ease of setup and does it require additional administration efforts beyond those of a SAS programmer. 4 | sas programmer, sas enterprise guide project, sas enterprise guide, sas programming | source | 14 |
| 166 | Finding the Treasure: Using Geospatial Data for Better Results with SAS Visual Analytics | Patricia S. Aanderud | Traditional business intelligence systems have focused on answering the who, what, and when questions, but organizations need to know the where of data as well. SAS Visual Analytics makes it easy to plot geospatial data, which can add a completely new element to your data visualizations and analysis. When looking at a tabular report, you notice multiple columns that represent customers, competitors, and demographic information. But when you place the same geocoded data on a map, new insights jump off the page! Perhaps you see where the better customers are, where they are in relation to competitors, and the regions that provide the most market potential based on underlying demographics. These finding are about the where of the data. It's like creating a treasure map | bar chart, dataset, bubble plot | source | 19 |
| 167 | Fine-Tuning Topical Content in Written Expressions in Cloud-Based Environments Using SAS | Gurpreet S. Bawa | Our recent work in the examination of variability in topic content among different social groups in social media demonstrates our ability to automatically detect different words and expressions that imply the same or simi lar meaning. The approach relies on first carrying out a vector -based topic identification that is computed across the entire conversational corpus in the social media collection. In the next step, various social groupings or sub -nets are identified. Socia l network membership and role (leader/follower) are identified so as to weight potential conversational influence. Finally, word and phrase lists are generated for each of the topic scores in each of the social groupings. The word and phrase lists are identified through rule induction through the use of either machine learning or specialized procedures such as the Boolean Rule generator in SAS Text Analytics | macro variable, proc freq | source | 10 |
| 168 | Fitting Compartment Models Using PROC NLMIXED | Raghavendra Rao Kurada, Fang K. Chen |
The CMPTMODEL statement is a new enhancement to the NLMIXED procedure in SAS/STAT 14.3. This statement enables you to fit a large class of pharmacokinetics (PK) models, including one-, two-, and three-compartment models, with intravenous (bolus and infusion) and extravascular (oral) types of drug administration. The CMPTMODEL statement also supports multiple dosages and PK models that have various parameterizations. This paper introduces the new statement and illustrates its usage through examples. Related concepts are also discussed, such as the %PKCONVRT autocall macro (which converts PK data sets that are stored according to industry standard to data sets that can be directly used by PROC NLMIXED), extension toEmax models, prediction, visualization, and fitting Bayesian PK models (by using the MCMC procedure) |
proc step, estimate, proc nlmixed | source | 26 |
| 169 | Five Approaches for High-Performance Data Loading to the SAS Cloud Analytic Services Server | Robert Collum | SAS Viya offers the SAS Cloud Analytic Services (CAS) Server as the in-memory analytics engine to serve the demands of powerful analytics, the scope and volume of which are ever increasing. However, before that analysis can occur, large quantities of data must often be loaded into the CAS memory space. Therefore, the transfer of huge amounts of data from source systems into CAS is a major consideration when planning the architecture of your SAS solution. High-performance data transfer is often provided by loading data using multiple parallel channels. This process uses many pathways simultaneously to transfer data at a rate several times faster than is typical using a single, serial path. High-performance data transfer is a natural complement to the speed and efficiency of in-memory analytics provided by CAS. CAS now provides five different major techniques for the parallel transfer of data over a wide range of potential data sources | cas, sas viya, proc step, dataset, numeric variable, sas base, in-memory analytics, parallel processing | source | 14 |
| 170 | Flight 3411 and Its Aftermath: A Burlesque Analysis via Tweets | Vinoth Kumar Raja, Padmashri Janarthanam |
The aviation industry today is in oligopolistic conditions where major airlines control the market and create entry barriers to low-cost domestic airlines and fast-growing rivals from other markets. This has led to stifling competition between the two groups, and when controversies such as service failures surround the oligopolies, competitors strike back by sharing comparative advertisements on social media platforms. The posters act as a catalyst for already dissatisfied customers resulting in a more negative attitude towards the mocked brand and indirectly building a substantial brand equity and loyalty for the competitors. This paper focuses on overall sentiment surrounding three major controversial passenger incidents in the airline industry from the year 2017 and how subsequent parody advertisements from competitors helped the latter gain an advantage over the market leaders and boost their brand value | sample, dataset, data mining, parsing | source | 22 |
| 171 | Fluid and Dynamic: Using measurement-based workload prediction to dynamically provision your Cloud | Nikola Markovic | As the capabilities of SAS Viya and SAS9 are brought together, alongside the integration with open - source technologies such as R and Python, it appears that a set of technologies with wide -ranging resource requirements will inevitably end up sharing the same infrastructure. It's more than likely that some traditional SAS batch jobs will continue to run throughout the night, probably joined by some newly scheduled Python programs; increasing amounts of data will be loaded into memory every day, but for many customers month-end processing cycles will always be a thing. As organizations naturally look to progress toward the Cloud, the resource requirements for such an environment will appear complex and might seem overwhelmingly expensive | batch job, iot | source | 8 |
| 172 | Forecasting the Value of Fine Wines | Joseph Breeden | Fine wines have gained attention globally as an investment opportunity with possible diversification benefits relative to more traditional investments and potentially high rates of return. Using a database from auctionforecast.com with over two million prices from auctions globally, we analyzed the price dynamics of fine wines to quantify predictive factors for investors. Brand, vintage, ratings, auction houses, bottle size, age of the wine, market trends, and more are considered in this analysis. Although data mining techniques are common in other applications, long -range forecasting requires a careful separation between current discrimination factors and long -term drivers. In this article we demonstrate how one must handle these issues in model development. The models used are a combination of Age- Period-Cohort models and traditional scoring techniques | regression analysis, proc phreg, proc step, hazard function, monte carlo, ridge regression | source | 9 |
| 173 | Forecasting: Something Old, Something New | David A. Dickey | ARIMA (AutoRegressive Integrated Moving Average) models for data taken over time were popularized in the 1970s by Box and Jenkins in their famous book. SASTM software procedures PROC ESM (Exponential Smoothing Models) and PROC UCM (Unobserved Components Models which are a simple subset of statespace models - see PROC SSM) have become available much more recently than PROC ARIMA. Not surprisingly, since ARIMA models are universal approximators for most reasonable time series, the models fit by these newer procedures are very closely related to ARIMA models. In this talk, some of these relationships are shown and several examples of the techniques are given. At the end, the listener will find that there is something quite familiar about these seemingly new innovations in forecasting and will have more insights into how these methods work in practice |
proc esm, proc step, residual, unit root, estimate, proc calis, seasonality, time series analysis, likelihood function, binary variable, first difference, maximum likelihood estimation, proc ucm, moving average, irregular component, trend, weighted mean, sample, outlier, regression analysis | source | 26 |
| 174 | Fraudsters Love Digital : An Emerging Threat Missed on Many Insurers Digitization Roadmaps | David Hartley | In most financial services companies and at almost every conference, there is a real buzz around digital transformation programs and digital disruption initiatives that will significantly impact, if not reinvent, many insurers and their business role today. This is all seen as positive and good, helping to deliver new and improved service levels, broaden appeal, and potentially reduce servicing costs. So, for the vast majority of customers this is good news. But it is also good news to the small minority (perhaps up to 10% of claimants) who are looking to actively defraud the insurer. In this presentation, we explore the need to build in suitable safe guards to ensure that the rise of the armchair fraudster doesn't go unchallenged. The presentation illustrates how insurers are embracing real-time fraud analytics to help them in this fight-but without compromising the good that such programs deliver for the majority of customers | outlier, auto insurance | source | 5 |
| 175 | From Academy to Industry: An Experimental Student Journey | Francesco Costigliola | Presentation | data mining, social network analysis, logistic regression, likelihood function, logistic distribution, regression analysis | source | 28 |
| 176 | From Idea to Implementation: How a SAS Communities Thread Changed How Sleep Number Direct Markets | Aaron Carlson | Presentation | source | 14 | |
| 177 | From Unstructured Text to the Data Warehouse: Customer Support at the University of North Texas | Nick Evangelopoulos | Traditional business intelligence uses a data warehouse to generate reports that increase organizational knowledge. In the current big data environment, organizational data include large collections of unstructured documents, especially in the domain of customer support. However, a formal process of expanding the traditional dimensional model to include elements that are derived from such collections is often missing. In this presentation, we provide a case study from IT Shared Services (ITSS) division at t he University of North Texas (UNT). As part of the UNT System's ServiceNow initiative, we examine a collection of ITSS' service work-order data to take unstructured text to the data warehouse. Going beyond traditional reporting elements, such as service requests by time, or request category by department, we show how text analytics help uncover the hidden dimension of service topic | proc corresp, proc freq, business intelligence, data mining, fact table, proc princomp, parsing | source | 10 |
| 178 | Fun with Address Matching: Use of the COMPGED Function and the SQL Procedure | Alexandra Varga | Address matching is often a challenging task for a SAS programmer. What seems like a relatively straightforward quest ends up amounting to hours of frustration and manual record review. Who knew there were more than five ways to spell the word "North"? Now, multiply this by 20+ words that might have varying naming conventions (I want to scream just thinking about it). This paper discusses some common data cleaning techniques used in address matching, including the TRANSWR D and COMPRESS functions. A roadmap for the use of the COMPGED function and the SQL procedure to identify matches is provided. The advantages and flexibility of this approach are sure to drive you on your way to address matching in no time |
proc sql, compress function, proc step, tranwrd function | source | 6 |
| 179 | Fuzzy Matching Programming Techniques Using SAS Software | Stephen B. Sloan, Kirk Paul Lafler |
Data comes in all forms, shapes, sizes and complexities. Stored in files and data sets, SAS users across industries know all too well that data can be, and often is, problematic and plagued with a variety of issues. When unique and reliable identifiers are available, users routinely are able to match records from two or more data sets using merge, join, and/or hash programming techniques without problem. But, what happens when a unique identifier, referred to as the key, is not reliable or does not exist | proc step, proc sql, proc sort, character variable, proc freq, proc contents, order by, missing value, numeric variable, sas function, data merging, where statement, nominal variable, create table, data transformation, proc print, data cleaning, proc transpose, character functions | source | 24 |
| 180 | Get Better Weight of Evidence for Scorecards by Using a Genetic Algorithm | Xia Ke Shan, Peter W. Eberhardt |
Presentation | source | 13 | |
| 181 | Getting More Insight into Your Forecast Errors with the GLMSELECT and QUANTSELECT Procedures | Gerhard Svolba | Is it sufficient just to monitor the quality of your forecast models over time? Can data science methods identify the drivers for large forecast errors and provide more insights than descriptive statistics? Do demand planners really improve forecast accuracy with their manual overwrites? Using a real-life case study, this paper answers these questions. It shows how you can study the impact of factors like product group, forecast horizons, seasonality, or the forecast model type on forecast accuracy and convert them into actionable results. You learn how univariate methods provide first insights into the structure and relationships of your forecast data. You gain insight into how manual overwrites of the statistical forecast change forecast accuracy in both directions and how you use analytical and graphical methods to illustrate these findings. You see how multivariate analytical methods like linear and quantile regression provide additional relevant insight | residual, regression analysis, proc quantreg, proc reg, sas viya, time series analysis, output, proc glmselect, estimate, ods output, proc sgplot, multivariate analysis, sample, response variable, univariate analysis, proc glm, proc step, demand forecasting, proc means, binary variable, predictor variable, outlier, histogram, date variable, data mining, line chart | source | 26 |
| 182 | Getting Started with Bayesian Analysis | Danny R. Modlin | Presentation | bayesian statistics, posterior probability, prior probability, markov chain, sample, monte carlo, mcmc, regression analysis, simple linear regression, proc reg, proc genmod, likelihood function | source | 14 |
| 183 | Getting Started with Survival Analysis | Marc Huber | survival function, survival analysis, hazard function, predictor variable, confidence interval, likelihood function, hazard ratio, rank test, proc step, log-rank test, proc phreg, time variable, regression analysis, cumulative hazard, censoring, estimate, sample, null hypothesis, class variable, wilcoxon test, nonparametric test, proc calis, dataset, exponential distribution, kaplan-meier estimator, response variable, sample size, proc means, proc reg, interval variable, kaplan-meier plot, proportional hazards assumption, main effect, follow-up period, maximum likelihood estimation, time-dependent covariate, proc lifereg | source | 76 | |
| 184 | Getting Started with Time Series Models | Danny R. Modlin | Presentation | time series analysis, sample, time series data, proc esm | source | 15 |
| 185 | Getting to Know the No-Show: Predictive Modeling of Missing a Medical Appointment | Joe Lorenz | Patients not showing up for appointments, without cancelling, are still a major loss of efficiency for healthcare clinics, and reduce patient face-time with healthcare providers. This in turn leads to a loss of revenue for healthcare clinics, and overbooking of patient appointments. Using a dataset of 110,000 Brazilian medical appointments collected over a 3-month period, different healthcare populations were identified based on age and frequency of healthcare use. Once healthcare populations were selected, stepwise logistic regression was performed, using PROC LOGISTIC, in order to identify factors within and across populations that are relevant indicators of no-show appointments. The data was spilt for cross validation, with approximately 80% allocated to build the fitted models, and 20% reserved for validation |
data cleaning, odds ratio, logistic regression, logistic distribution, regression analysis, likelihood function, roc curve, proc step, proc freq | source | 10 |
| 186 | Harmonize Your SAS Environments with Hot Fix Versions | Jitendra Pandey | Presentation | source | 8 | |
| 187 | Harvesting Unstructured Data to Reduce Anti-Money Laundering (AML) Compliance Risk | Austin Cook, Beth Herron |
As an anti-money laundering (AML) analyst, you face a never-ending job of staying one step ahead of nefarious actors (for example, terrorist organizations, drug cartels, and other money launderers). The financial services industry has called into question whether traditional methods of combating money laundering and terrorism financing are effective and sustainable. Heightened regulatory expectations, emphasis on 100% coverage, identification of emerging risks, and rising staffing costs are driving institutions to modernize their systems. One area gaining traction in the industry is to leverage the vast amounts of unstructured data to gain deeper insights. From suspicious activity reports (SARs) to case notes and wire messages, most financial institutions have yet to apply analytics to this data to uncover new patterns and trends that might not surface themselves in traditional structured data | machine learning, suspicious activity, unstructured data, financial crime, parsing, sar, aml, artificial intelligence, trade finance | source | 10 |
| 188 | High-Availability Services in an Enterprise Environment with SAS Grid Manager | Andrey Turlov | Many organizations, nowadays, rely on services for the upkeep of critical day-to-day business operations. Consequently, these services must be constantly available and accessible, even when underlying infrastructure experiences failures. SAS Grid Manager provides a high-availability SAS Enterprise Business Intelligence platform, out of the box, based on a multi-machine architecture with LSF and service failover. In a large multinational organizational setting, the base installation and configuration of LSF and SAS should be extended to provide HA services according to company requirements (e.g. server topology, resource management, multi-tenancy). This paper presents such cross-component configuration samples, tips and tricks on all layers of SAS, LSF, EGO that come together to provide better and reliable HA services for global enterprises | grid computing, business intelligence | source | 7 |
| 189 | Home Value Prediction Using Data Mining | Vivek Singh | Presentation | source | 4 | |
| 190 | House Prices Segmentation | Mettila Kaimathuruth | Presentation | source | 4 | |
| 191 | How a Code-Checking Algorithm Can Prevent Errors | Thomas Hirsch | Presentation | create table, macro variable | source | 21 |
| 192 | How Effective is Change of Bowling Pace in Cricket? | Deven Chakraborty | Presentation | source | 6 | |
| 193 | How Good is That Forecast? The Nuances of Prediction Evaluation Across Time | Aric LaBarr | When predicting across time, typical methodologies of prediction evaluation no longer hold true. It is not practical to take a hold-out sample randomly from observations in the data set or even use a typical k -fold cross-validation structure. Even newer methods of prediction evaluation in cross-sectional data like target shuffling should not just be applied to data where a temporal structure is inherent. How then can we determine if we have a good forecast? This talk highlights advantages and disadvantages to techniques evaluating predictions when forecasting future observations. It also discusses possible biases arising from time structures of data that should be considered | data cleaning, time series analysis, sample, time series data, cross-validation, model validation, response variable, cross-sectional data, training set, validation set | source | 7 |
| 194 | How SAS Helps to Price Auto Insurance in Brazil | Anna Mattos | In an increasingly dynamic and complex market such as auto insurance, it is absolutely mandatory to construct advanced pricing methodologies with strong predictive power in order to ensure profitability. Due to our business being in a country with continental dimensions, finding the right price in face great risk and customer behavior disparity is a major challenge. Therefore, we developed methodologies for risk discrimination, geo classification, forecasting and optimization using SAS platforms. These have allowed us to build a more holistic view of our business and integrate pricing intelligence across risk and demand elasticity profiles | auto insurance, sas risk engine, time series analysis | source | 7 |
| 195 | How Statistically Analyzing System Behaviors with SAS Visual Analytics Revealed Unknown Data Issues | Jessica Fraley | Automatic loading, tracking, and analysis of data readiness in SAS Visual Analytics is easy when you combine SAS Data Integration Studio with the DATASET and LASR procedures. This paper is a follow- up to a previous paper presented at SAS Global Forum 2017 on the methodology used at the University of North Carolina at Chapel Hill to track data preparation and readiness with SAS Visual Analytics reporting. This paper covers a real-world example of how analysis and visualization methods surfaced unknown data integrity issues brought about by anomalous system behaviors. This paper also covers how we recognized the issues, and created SAS Visual Analytics visualizations to help any SAS customer quickly identify potential data integrity issues that originate from system behaviors | standard deviation, line chart, raw data | source | 8 |
| 196 | How to Build a Recommendation Engine Using SAS Viya | Jared Dean | Helping users find items of interest is useful and positive in nearly all situations. It increases employee productivity, product sales, customer loyalty, and so on. This capability is available and easy to use for SAS Viya customers. This paper describes each step of the process: 1) loading data into SAS Viya; 2) building a collaborative filtering recommendation model using factorization machines; 3) deploying the model for production use; and 4) integrating the model so that users can get on-demand results through a REST web service call. These steps are illustrated using the SAS Research and Development Library as an example. The library recommends titles to patrons using implicit feedback from their check -out history | recommendation engine, sas viya, sample | source | 15 |
| 197 | How to Load Relational Data into SAS Cloud Analytic Services Using Java | Salman Maher | Java is one of the most popular programming languages available today. Did you know that you can easily use Java with SAS Cloud Analytic Services (CAS)? This presentation shows you how to do this and more. During this presentation, we cover the following tasks: how to load the Java libraries required to invoke CAS actions within your Java program how to invoke CAS actions from Java * how to load data from a relational database into CAS You will walk away from this presentation confident that you can read and write data from CAS to a relational database using a Java program | cas, cas action, sas viya, proc step, structured data, proc sql | source | 7 |
| 198 | How to Prevent Turnover of the Best and Most-Experienced Employees with SAS Enterprise Miner | Liyuan Liu, Lauren Staples |
Presentation | data mining | source | 4 |
| 199 | How to Use Streaming Analytics to Create a Real-Time Digital Twin | Bradley W. Klenz | As the Internet of Things (IoT) expands, connected devices are frequently located in remote places, operating in different physical environments. These devices communicate with control systems and with each other. It can be challenging to know and understand the environment in which these devices operate , and whet her devices are operating properly and efficiently. To meet these challenges, you can create a "digital twin" of a device: a virtual representation of it in real time. A digital twin tells you how a device is operating, 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. This paper explains how streaming analytics in SAS Event Stream Processing can be used to create a real-time model of a remote device | outlier, time series analysis, proc princomp, response variable | source | 14 |
| 200 | Human Resources Analytics: Why Do the Best Employees Leave? | Ankita Khurana | Presentation | source | 4 | |
| 201 | Identifying Behaviors and Irregularities in Public Procurement in the Brazilian Federal Government | Andre Castro | Public procurement in Brazil is responsible for a large volume of resources and consequently has a high level of importance for the country's economy. The evolutions in frameworks and legal devices that surround the theme have shown a concern with the improvement of the processes responsible for guaranteeing the supply of goods and services for the Brazilian State in its three spheres, federal, state and county. Concerned with the continuous improvement of these processes, Ministry of Planning, Development and Management, responsible for regulating and monitoring the processes and information systems of federal public procurement, seeks to identify supplier behaviors in electronic reverse auctions that hurt the isonomy of these processes, specifically with the use of bots for automatic bidding, fraud in the exclusive use of benefits for micro company and small Business (MC/SB) and also in the identification of collusions between suppliers with the presence of "rabbits" | data validation rules, market basket analysis | source | 15 |
| 202 | Identifying Duplicate Variables in a SAS Data Set | Bruce F. Gilsen | In the big data era, removing duplicate data from a data set can reduce disk storage use and improve processing time. Many papers have discussed removal of duplicate observations, but it is also useful to identify duplicate variables for possible removal. One way to identify duplicate variables is with PR OC COMPARE, which is commonly used to compare two data sets, but can also compare variables in the same data set. It can accept a list of variable pairs to compare and determine which variable pairs are identical. This paper shows how to obtain a summary report of identical variables for all numeric or character variables in a data set, using the following steps: 1. Dynamically build a list of all possible numeric or character variable pairs for PROC COMPARE to analyze. 2 |
proc compare, proc step, macro variable, conditional do loop, numeric variable, proc sql, character variable, if-then-else | source | 15 |
| 203 | Identifying Semantically Equivalent Questions Using Singular Value Decomposition | Varsha Reddy Akkaloori | In the past few decades, inquisitive people who are in constant pursuit of knowledge, are visiting Question & Answer sites, such as Quora, Stack Overflow, Yahoo! Answers etc. to find out new things authored b y mavens in their respective fields. In order t o maintain "content quality", most of the Q&A sites would want its visitors to search their website for an answer to their question before posting a new one. With over 100 million monthly visitors, it's not surprising that many people ask similarly worded questions causing site visitors to spend more time discovering the best response to their question. This also frustrates authors because they feel they need to answer multiple versions of the same question. This paper aims at solving a challenge released by Quora to improve the experience of its authors and site visitors by grouping queries with similar intent using SAS. Two queries are assumed semantically equivalent, if they could be answered with the exact content | proc freq, data mining, proc distance, euclidean distance | source | 6 |
| 204 | If You Need These OBS and These VARS, Then Drop IF and Keep WHERE | Jayanth Iyengar | Reading data effectively in the DATA STEP requires knowing the implications of various methods, and DATA STEP mechanics; The Observation Loop, and the PDV. The impact is especially pronounced when working with large data sets. Individual techniques for subsetting data have varying levels of efficiency and implications for input/output time. Use of the WHERE statement/option to subset observations consumes less resources than the subsetting IF statement. Also, use of DROP and KEEP to select variables to include/exclude can be efficient depending on how they're used |
data=, dataset, drop statement, if-then-else, where statement, subsetting if, cpu time, set statement | source | 9 |
| 205 | Image Classification Using SAS Enterprise Miner 14.1 | Akshay Arora | neural network, deep learning, data mining | source | 5 | |
| 206 | Image Processing: Seeing the World through the Eyes of SAS Viya | Ivan Gomez | Ever wonder how the algorithms used by Facebook and Google detect your friends in your photos? Image recognition and classification algorithms, such as deep neural networks, can extract important information from photos and classify them almost instantly after you post a picture. Not only is this process useful on social media, but there are numerous applications of image classification algorithms in healthcare, manufacturing, and security screenings. Using the latest machine learning capabilities available in SAS Viya for text and image processing, organizations can leverage in-memory processing with SAS Cloud Analytic Services (CAS) and enhanced parameter tuning to develop more sophisticated deep learning models. In this paper, we discuss key components of building an image classification algorithm | neural network, deep learning, machine learning, data cleaning, model training, sas viya, cas action | source | 11 |
| 207 | Implementing Analytics: Perspectives from the Client Side | Suresh Divakar | Presentation | source | 54 | |
| 208 | Implementing External File Processing with No Record Delimiter via a Metadata-Driven Approach | Princewill Benga | Most of the time, we process external files that have fixed wid th columns, or that contain data separated by a common delimiter, such as comma, tab, pipe, etc. However, there are times where we have to deal with unusual and/or inconsistent file structure and format. Th is paper will describe an innovative approach to read external files with no available delimiter wit h which to parse the data. Furthermore, the structure and type of the data can change from one record to an other. Each record may have a unique format, sometimes requiring looping constructs to properly set the required data. This case is derived from a real world example in the automobile insurance sector. The solution uses external metadata to define required characteristics of the raw file, along with has h object lookup and advanced INFILE statement processing to achieve the required business outcome. This paper can be of interest to anyone who needs to read external files whose structure is uncommon | hash table, infile statement | source | 15 |
| 209 | Implementing Privacy Protection-Compliant SAS Aggregate Reports | Leonid Batkhan | Personal data protection in published reports is an important human right, and is required by US Federal Law, European Union General Data Protection Regulation, and many other jurisdictions. This paper highlights the importance of producing privacy protection-compliant reports, discusses aspects of potential privacy breaches, and suggests a robust algorithm for producing well-protected aggregate reports. This paper walks through the complex logic of an enhanced complementary suppression process, and demonstrates SAS coding techniques to implement and automatically generate aggregate tabular reports compliant with privacy protection law. The result is a set of SAS macros ready for use in any reporting organization responsible for compliance with privacy protection | proc step, class variable, proc report, macro variable, proc freq, dataset, raw data, macro definition | source | 14 |
| 210 | Improved Campaigning at Africa's Largest Pay TV Service Provider with SAS Marketing Optimization | Jean De Villiers | Traditionally, the entertainment industry incurs significant cost in the engagement of a diverse customer base with appropriate messaging and slow marketing turnaround, leading to reduced customer satisfaction levels and customer retention rates. MultiChoice South Africa, Africa's largest Digital Satellite Television service provider leverages a rich set of historical and personal customer information, in both structured and unstructured format, in order to understand and predict customer behavior and marketing campaign propensity. These predictive models are utilized to improve campaign targeting, generating significant incremental revenue, whilst reducing costs of communication. This paper describes MultiChoice's data science journey, using SAS Marketing Optimization, to campaign to the right customers at the right time, through the right communication channel to ensure that the company maximize profit, improve retention rates, and improve overall customer satisfaction. 2 | data science, propensity score, sample, feature selection, proc sql, data cleaning, data validation rules, model training, logistic regression, sql server, regression analysis, sas enterprise guide, predictor variable, logistic distribution | source | 16 |
| 211 | Improving Financial Reporting Accuracy Using Smart Meter Data | Prasenjit Shil, Tom Anderson |
Presentation | financial reporting | source | 4 |
| 212 | Improving Game Strategies with Data Analytics: The Case of Euroleague Professional Basketball | Raha Akhavan-Tabatabaei, Cem Yigman |
Presentation | proc means, logistic regression, logistic distribution, regression analysis, machine learning, proc hpforest, predictor variable | source | 35 |
| 213 | Improving Scheduling and Strategic Planning at J. P. Morgan Chase's Card Production Center | Jay P. Carini | JP Morgan Chase's manufacturing-based card production center (CPC) is beginning to leverage their vast array of production data and take advantage of the benefits provided through analytics using end -to- end SAS-based solutions. Due to the complex nature of their operation, CPC desired a more efficient process to support strategic planning and scheduling, strategy related to unit cost and revenue generation, and optimizing resource allocation. Utilizing the CPM Procedure within the SAS/OR suite, we built a model which allows the business to schedule tasks and test various strategic scenarios subject to priority, resource and time constraints, across a variety of products within their desired planning horizon. In order to operationalize the solution and demonstrate business value before investing in costly IT resources, we developed a user interface integrating SAS stored processes, the SAS Add -In for Microsoft Office (SAS AMO) and customized VBA code |
excel, proc step, sample, data cleaning | source | 12 |
| 214 | Indication of Irregularities in the Accumulation of Public Sector Jobs in the State of Ceará (Brazil) | Jose Silva, Raimundo Filho |
About 8 million dollars a month. This is the volume of financial resources that the State of Ceará (Brazil) is saving monthly, with the implementation of data analysis and analytical intelligence project to identify irregularities (fraud) in the accumulation of public posts of its servers, using the SAS Fraud Framework for Government solution. This work will present the methodology and procedures used with the implementation of SAS software solution, which will allow a saving of more than one million dollars per month to the public coffers of the State of Ceará, as well as several results from the application of analytics in the TCE-Ceará, state of Brazil | data science, sas enterprise guide | source | 11 |
| 215 | Infrastructure for Healthcare Analytics: A Foundational Approach to Development | Jennifer R. Popovic | Healthcare-related organizations and the data they generate are notoriously siloed, with varied systems, structures, data standards, and governance processes and policies. Research teams and organizations analyzing these data can be similarly siloed and challenged in their ability to create r obust analytic infrastructure that would enable the sharing of their most valuable analytic assets: data and the analytic expertise and methods, approaches and tools to analyze them. These challenges often lead to inefficient analytic development based on one-off approaches that do not prioritize or promote the ideas of reusability, flexibility, scalability or reproducibility. This paper discusses the concept of analytic infrastructure and will discuss strategies that research teams and organizations can use in their approaches to develop infrastructure | health insurance, insurance claims | source | 11 |
| 216 | Innovate with Data: Use Cases of SAS in a Modern Hadoop, AI, Machine Learning, and IoT World | Raj Dalal | Presentation | machine learning | source | 38 |
| 217 | Innovating Government Decision-Making through Analytics | Steve Bennett | Everything that a government agency does revolves around decision making. While the types of decisions can be highly varied, from strategic decisions about agency budget priorities and delivery of social benefits, to split-second operational decisions on the battlefield, decisions are at the center of what governments do. But how do we make decisions? What evidence and information gets considered? And ultimately, since humans either alone or in combination are the ultimate makers of decisions, how does that evidence and information get interpreted, processed, synthesized, and assessed in our brains to ultimately arrive at a particular alternative, given an array of possible choices? Understanding how we as humans make decisions, and even more importantly, how we can make them better - is critical for successful execution of government agency missions | numeric functions, outlier, deep learning, proc freq, machine learning, data visualization | source | 19 |
| 218 | Insights from a SAS Technical Support Guy: A Deep Dive into the SAS ODS Excel Destination | Chevell T. Parker | SAS is a world leader in data analytics while Microsoft EXCEL, with over 30 million active users, is a leader when it comes to spreadsheet packages. EXCEL spreadsheets are heavily used for calculations, information organization, statistical analysis, and graphics. SAS can leverage the power of its world-class analytics and reporting capabilities to produce stylistic and highly functional EXCEL spreadsheets using the EXCEL destination in the SAS Output Delivery (ODS) System |
excel, proc step, ods excel, output delivery system, proc report, options statement, xls, filename statement, sas enterprise guide, proc template, format statement, sample, proc format | source | 24 |
| 219 | Insights into Using the GLIMMIX Procedure to Model Categorical Outcomes with Random Effects | Kathleen Kiernan | Modeling categorical outcomes with random effects is a major use of the GLIMMIX procedure. Building, evaluating, and using the resulting model for inference, prediction, or both requires many considerations. This paper, written for experienced users of SAS statistical procedures, illustrates the nuances of the process with two examples: modeling a binary response using ran dom effects and correlated errors and modeling a multinomial response with random effects. In addition, the paper provides answers to common questions tha t are received by SAS Technical Support concerning these analyses with PROC GLIMMIX. These ques tions cover working with events/trials data, handling bias issues in a logistic model, a nd overcoming convergence problems |
proc glimmix, proc step, proc mixed, covariance, likelihood function, binomial distribution, covariance structure, odds ratio, estimate, proc calis, logistic distribution, sas log, proc genmod, degrees of freedom, fixed effects, maximum likelihood estimation, logistic regression, random intercept, roc curve, correlation, regression analysis, error message, relative risk, proc logistic, response variable, sample, predictor variable | source | 23 |
| 220 | Integrating SAS Analytics into Your Web Page | James Kochuba, David Hare |
sas viya, score code, machine learning, rest api | source | 11 | |
| 221 | Integrating SAS and Data Vault | Patrick Cuba | Data Vault (DV) modelling technique is fast gaining popularity around the world as an easy to learn, easy to manage, easy to maintain and easy to adapt (to business change) data modelling technique. In this paper we will briefly explore what DV is; DV artifacts and we will explore how SAS can be used to automate its data loading patterns | sample, surrogate key, natural key, sas data integration studio, macro call, proc sql | source | 19 |
| 222 | Integrating SAS and Elasticsearch: Performing Text Indexing and Search | Edmond Cheng | Presentation | proc step, proc http, dataset | source | 18 |
| 223 | International Dates and Times: Around the World with SAS | Derek P. Morgan | Presentation | source | 14 | |
| 224 | Interpreting Black-Box Machine Learning Models Using Partial Dependence and Individual Conditional Expectation Plots | Ray Wright | One of the key questions a data scientist asks when interpreting a predictive model is "How do the model inputs work?" Variable importance rankings are helpful for identifying the strongest drivers, but these rankings provide no insight into the functional relationship between the drivers and the model's predictions. Partial dependence (PD) and individual conditional expectation (ICE) plots are visual, model -agnostic techniques that depict the functional relationships between one or more input variables and the predictions of a black-box model. For example, a PD plot can show whether estimated car price increases linearly with horsepower or whether the relationship is another type, such as a step function, curvilinear, and so on. ICE plots enable data scientists to drill much deeper to explore individual differences and identify subgroups and interactions between model inputs | partial dependence, machine learning, sample, neural network, training set, proc hpsplit, gradient boosting, proc hpforest, data science, group by, score code, proc sgplot, macro definition | source | 19 |
| 225 | Introducing SAS Decision Manager 5.1 with Business Rules for SAS Viya | Chris Upton | SAS Business Rules Manager 1.2 was introduced in 2012 for customers who need software for building and managing their collections of valuable conditions and actions that drive business decision- making. These rules are combined with advanced analytical models and deployed into batch and real- time processing systems. SAS Decision Manager 5.1 brings full decision processing to SAS Viya. This paper tours those new features, including brand new editors for interactively building business rules, graphical process flow decisions, and reference data management; integration with SAS Visual Analytics and SAS Model Manager; computations based on the SAS Cloud Analytic Services (CAS) Server; model deployment to multiple operational servers; and compatibility with open-source environments such as Python. SAS Decision Manager is now cloud-ready, multi-tenant-capable, and provides a full REST API for custom application integration | data validation rules, reference data | source | 10 |
| 226 | Introducing SAS Model Manager 15.1 for SAS Viya | Glenn Clingroth | SAS Model Manager has been a popular product since 2006 for customers who need software for managing their collections of valuable analytical models with functions for organization, search, editing, testing, deployment, and monitoring performance. Some customers have thousands of models. SAS Viya brings a new version of Model Manager with a modern set of features and capabilities. This paper will tour those new features including a new web application for managing models and projects; integration with SAS Visual Statistics, Model Studio, SAS Decision Manager, and SAS Event Stream Manager; computations based on the SAS Cloud Analytic Server; model deployment to multiple operational servers; and compatibility with open source environments such as Python. Model Manager is now cloud-ready, multi-tenant capable, and provides a full REST API for custom application integration | source | 11 | |
| 227 | Introduction to ETL with SAS | Vasilij Nevlev | Presentation | source | 5 | |
| 228 | Invoiced: Using SAS Text Analytics to Calculate Final Weighted Average Price | Alexandre Carvalho | SAS Contextual Analysis brings advantages to the analysis of the millions of Electronic Tax Invoices (Nota Fiscal Electrônica) issued by industries and improves the validation of taxes applied. Tax calculation is one of the analytical challenges for government finance secretari es in Brazil. This paper hi ghlights two items of interest in the public sector: tax collection efficiency and the calculation of the final weighted average consumer price. The features in SAS Contextual Analysis enable the implementation of a tax taxonomy that analyzes the contents of invoices, automatically categorizes the product, and calculates a reference value of the prices charged in the market. The first use case is an analysis of compliance between the official tax rate-as specified by the Mercosul Common Nomenclature (NCM)- and the description on the electronic invoice | weighted mean, sample, data cleaning | source | 16 |
| 229 | Is Your Data Viable? Preparing Your Data for SAS Visual Analytics 8.2 | Gregor Herrmann | We all know that data preparation is crucial before you can derive any value from data through visualization and analytics. SAS Visual Analytics on SAS Viya comes with a new rich HTML5 interface on top of a scalable compute engine that fosters new ways of preparing your data upfront. SAS Data Preparation that comes with SAS Visual Analytics brings new capabilities like profiling, transposing or joining tables, creating new calculated columns, and scheduling and monitoring jobs. This paper guides you through the enhancements in data preparation with SAS Visual Analytics 8.2 and demonstrates valuable tips for reducing runtimes of your data preparation tasks. It covers integrating existing SAS 9 tools and programs in your data preparation efforts for SAS Viya | data wrangling, sas viya, cas, sas 9, excel, data cleaning, sas enterprise guide, proc sql | source | 10 |
| 230 | Just Enough SAS Cloud Analytic Services: CAS Actions for SAS Visual Analytics Report Developers | Michael Drutar | SAS Visual Analytics includes all of the point-and-click functionality required to load data, manage data, and perform other back-end work necessary to make Visual Analytics visualizations efficient and effective. But there are a handful of critical tasks that are sometimes just easier to do with a few lines of code, especially if your end goal is to automate that that process. While there is a substantial and growing codebase for CAS Actions and SAS Viya procedures, Visual Analytics report developers need to focus on the creation and implementation of reports. What we need is just enough CAS code to simplify the back-end work. This paper presents some of the most common, most useful CAS actions that can be run from any SAS Viya powered code window, including the SAS Studio interface | sas viya, sas studio, proc step, cas action, cas, proc reg, set statement, sas administrator, raw data, regression analysis | source | 14 |
| 231 | Key-Independent Uniform Segmentation of Arbitrary Input Using a Hash Function | Paul M. Dorfman, Donald J. Henderson |
Aggregating or combining large data volumes can challenge computing resources. For example, the process may be hindered by the system limits on utility space or memory and, as a result, either fail or run too long to be useful. It is a natural inclination to try solving the problem by segregating the input records into a number of smaller segments, process them independently, and combine the results. However, in order for such a divide-and-conquer tactic to work, two seemingly contradictory criteria must be met: First, to aggregate or combine the data correctly, no segment can share its key-values with the rest; and second, the segments must be more or less equal in size. In this paper, we show how a hash function can be used to achieve it for arbitrary input with no prior knowledge of the distribution of the key -values among its records | composite key, create table, sample, proc sql, hash table, data wrangling, proc step, where statement, proc freq | source | 18 |
| 232 | Latest and Greatest: Best Practices for Migrating to SAS 9.4 | Alec S. Fernandez, Leigh Fernandez |
SAS customers benefit greatly when they are using the functionality, performance, and s ecurity available in the latest version of SAS. However, the task of moving all SAS collateral such as programs, data, catalogs, metadata (stored processes, maps, queries, reports, and so on), and content to SAS 9.4 can seem daunting. This paper provides an overview of the steps required to move SAS collateral from systems based on SAS 9.2 and SAS 9.3 to the current release of SAS 9.4 | error message, data cleaning, credit migration | source | 14 |
| 233 | Lazy Programmers write Self-Modifying code OR Dealing with XML file Ordinals | David B. Horvath | The XML engine within SAS is very powerful but it does convert every object into a SAS data set with generated keys to implement the parent/child relationships between these objects. Those keys (Ordinals in SAS speak) are guaranteed to be unique within a specific XML file. However, they restart at 1 with each file. When concatenating the individual tables together, those keys are no longer unique. We received an XML file with over 110 objects resulting in over 110 SAS data sets, which our internal customer wanted concatenated for multiple days. Rather than copying and pasting the code to handle this process 110+ times, and knowing that I would make mistakes along the way-and knowing that the objects would also change along the way-I created SAS code to create the SAS code to handle the XML. I consider myself a Lazy Programmer. As the classic "Real Programmers…" sheet tells us, Real Programmers are Lazy | proc step, proc contents, proc import, dataset | source | 9 |
| 234 | Let's Get FREQy with our Statistics: Data-Driven Approach to Determining Appropriate Test Statistic | Lynn Mullins, Richann J. Watson |
Presentation | macro conditional, ods output, output delivery system, binomial distribution | source | 24 |
| 235 | Leverage Custom Geographical Polygons in SAS Visual Analytics | Murali Nori, Falko Schulz |
Discover how you can explore geographical maps using your own custom map regions. SAS Visual Analytics supports several predefined geo codes including various country and sub -division lookups. However often you have your own custom polygons or shape files drawing exact boundaries of the regional overlay you are trying to explore. From generic sales regions, floor plans or even pipe lines - there are many use cases for custom polygons in visual data analysis. Using custom regions is now easier than ever with a user-interface driven support for importing and registering these custom providers. This paper demonstrates not only the different types of custom providers supported but also shows how to leverage custom polygons within SAS Visual Analytics by showcasing various industry example s | reference data, unique key | source | 19 |
| 236 | Leveraging Multivariate Testing for Digital Marketing Using SAS Enterprise Guide | Vandana Reddy, Jui Salunkhe |
Presentation | sas enterprise guide | source | 4 |
| 237 | Leveraging SAS In-Database Technology to Improve the Efficiency of Data Analytics | Michael Santema | Presentation | source | 18 | |
| 238 | LinAcc: A SAS Macro for Assay Linearity and Accuracy Determination | Jesse Albert Canchola, Pari Hemyari |
Linearity and accuracy are essential metrics for a linear response assay. Linearity can be defined in various ways while the accuracy is defined as the log observed difference from the "perfect accuracy" line. The LinAcc SAS Macro packages both linearity and accuracy in one easy to use implementation on the SAS System | macro definition, regression analysis, macro call, proc step | source | 16 |
| 239 | Location Matters: Evidence from Spatial Econometric Analysis of Opioid Prescription Rates | Jonathan Mccopp | A substantial number of Medicare patients are at risk of opioid abuse. A Centers for Medicare and Medicaid Services (CMS) analysis identified approximately 225,000 beneficiaries who received potentially unsafe opioid dosing. Among the beneficiaries with the highest number of opioid prescriptions filled in 2012, 23 opioid prescriptions were filled at an average cost of $3500 per beneficiary. A key to understanding beneficiary data effectively is to identify high-risk geographic clusters by applying location analytics based on historical data, demographic data, and health trends. Location analytics blends health data and socio-economic data with geographic data to reveal the location of opioid abuse among the Medicare population. Specifically, location analytics that includes spatial econometric modeling (like the SPATIALREG procedure) combined with SAS Visual Analytics on SAS Viya is a powerful and easy - to-use solution to identify high-risk spatial clusters |
feature selection, regression analysis, estimate, predictor variable, response variable, interaction effect, geospatial analytics, sas studio, correlation | source | 21 |
| 240 | Looking Beyond the Model with SAS Simulation Studio: Data Input, Collection, and Analysis | Edward P. Hughes, Emily Lada |
Discrete-event simulation as a methodology is often inextricably intertwined with many other forms of analytics. Source data often must be repaired or processed before being used (indirectly or directly) to characterize variation in a simulation model. Collection of simulated data needs to coordinate with and support the evaluation of performance metrics in the model. Or it might be necessary to integrate other analytics into a simulation model to capture specific complexities in the real-world system that you are modeling. SAS Simulation Studio is a component of SAS/OR software that provides an interactive, graphical environment for building, running, and analyzing discrete-event simulation models. In a broader sense, it is also an integral part of the SAS analytic platform. This paper illustrates how SAS Simulation Studio enables you to tackle each of these discrete-event simulation challenges. You have full control over the use of input data and the creation of simulated data |
dataset, probability distribution, confidence interval, proc step, sample, key performance indicator, cpu time | source | 20 |
| 241 | Machine Learning Bots for Fraud, Waste, and Abuse | Michael Ames | One of the most exciting developments has been the advent of machine learning "bots" for combating fraud, waste, and abuse of data. SAS has developed an adaptive intelligent agent, also known as "the bot", to identify suspicious, anomalous, and fraudulent data across a number of domains. Through continuous monitoring and learning, the intelligent agent has been deployed to augment existing surveillance strategies, identify areas that have been under-surveilled, as well as uncover new and emerging trends | machine learning, data cleaning, cross-validation, feature extraction, gradient boosting | source | 9 |
| 242 | Macro Method to use Google Maps and SAS to Geocode a Location by Name or Address | Laurie Smith | Google Maps is a very useful tool in finding driving directions between addresses and defining a geographic representation of an address (geocoding) which can be used in many applications in research. Using macro and DATA STEP SAS code, the latitude and longitude of a location can be found by searching the html code resulting from a Google Maps search, which can be developed using SAS v9.4 for Windows |
index, raw data | source | 8 |
| 243 | Macro that Can Get Geo Coding Information from the Google Maps API | Ting Sa | Presentation | sas programmer | source | 14 |
| 244 | Macros I Use Every Day (And You Can, Too!) | Joe DeShon | SAS macros are a powerful tool which can be used in all stages of SAS program development. Like most programmers, I have collected a set of macros which are so vital to my program development that I find that I use them just about every day. In this paper, I will 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 facility, sas base, macro loop, macro variable, sas function, excel | source | 9 |
| 245 | Make SAS Enterprise Miner Play Nicely with SAS Viya | Beth Ebersole | With a few simple tricks, your SAS Enterprise Miner on SAS 9 will run seamlessly with your new SAS Viya installation. You can enjoy the perks of your old, familiar interface and your existing SAS Enterprise Miner projects, PLUS the advantage of faster, distributed, multi-threaded processing on SAS Viya. We will walk you through all the steps needed to make this work effortlessly, and we provide the code that will help you. Why choose, when you can have the advantages of both SAS Enterprise Miner AND SAS Viya? | data mining, sas viya, neural network, macro variable, proc hpsplit, missing value, histogram, imputation, data cleaning, sample | source | 31 |
| 246 | Making Data More Familiar: Region and Language Settings with SAS Visual Analytics | Scott Dobner | Do you like using SAS with English text in the user interface (UI), but miss date, time, DATETIME, and numeric formats that are familiar to you? SAS Visual Analytics has a solution for you! Beginning in SAS Visual Analytics 8.2, you can separate the language of your UI from the regional preferences used to format your data. By giving you more options to control your environment, SAS is giving you more power to know around the globe! |
source | 7 | |
| 247 | Management and Usage of User-Defined Formats in the SAS Cloud Analytic Services Server | Denise M. Poll | Since the "dawn of SAS," user-defined formats have been created and applied to data values. Formats are used to control the written appearance of data values, or, in some cases, to group data values together for analysis. SAS Cloud Analytic Services (CAS) server actions support the creation and management of user-defined formats. Other CAS server actions use the formats to manipulate data and apply formats to generate tables or reports. Learn the basic concepts to create and manage format libraries. Get a deeper understanding of how a library can be global and local to a CAS session. Recognize the role of the format search list when applying formats | proc step, proc format, user-defined format, cas | source | 13 |
| 248 | Managing Complex SAS Metadata Security Using Nested Groups to Organize Logical Roles | Stephen M. Overton | SAS Metadata security can be complicated to setup and cumbersome to maintain over time. Often times organizations need to manage many itemized groups and roles across multiple logical roles, especially with some industry-specific SAS solutions. In some cases, user groups and roles have no standards or framework to follow, which forces SAS administrators to guess to get an initial security model and process in place. To make matters even more complicated, permissions could be mutually inclusive across logical roles. This paper will present a scalable methodology to manage any number of itemized groups and roles within SAS metadata by providing a standardized template and approach using nested groups which roll up into customizable logical roles. This approach can be scaled for larger organizations by layering more nested groups as the number of users and group grow | access control, sas administrator | source | 7 |
| 249 | Managing the Capital Adequacy Process (CAP) Using SAS | Sukhbir Dhillon | The Dodd-Frank Wall Street Reform and Consumer Protection Act (DFA) requires the thirty-plus domestically significant banks in the U.S. to conduct mid-year and annual stress testing to assess their capital adequacy under different scenarios. This exercise consists of various stress testing processes, which are rectified through the bank's process narratives, supervisory expectations, and internal controls related to the processes. Process flows choreograph the actual capital adequacy process (CAP) operationalization cycle. Compliance with supervisory expectations related to capital adequacy, which are mentioned in regulations SR 15-18[1] and SR 15-19[2], are carried out through a workflow-managed CAP. SAS offers a comprehensive solution, SAS Qualitative Assessment Manager, that lets banks manage and keep track of these expectations, processes, related control tests, and subsequent findings | capital adequacy, stress testing, capital planning, internal audit | source | 9 |
| 250 | Managing the Expense of Hyperparameter Autotuning | Patrick Koch | Determining the best values of machine learning algorithm hyperparameters for a specific data set can be a difficult and computationally expensive challenge. The recently released AUTOTUNE statement and autotune action set in SAS Visual Data Mining and Machine Learning automatically tune hyperparameters of modeling algorithms by using a parallel local search optimization framework to ease the challenges and expense of hyperparameter optimization. This implementation allows multiple hyperparameter configurations to be evaluated concurrently, even when data and model training must be distributed across computing resources because of the size of the data set. With the ability to both distribute the training process and parallelize the tuning process, one challenge then becomes how to allocate the computing resources for the most efficient autotuning process. The best number of worker nodes for training a single model might not lead to the best resource usage for autotuning | test set, model validation | source | 20 |
| 251 | Manipulating Statistical and Other Procedure Output to Get the Results That You Need | Vincent DelGobbo | Many scientific and academic journals require that statistical tables be in a specific format (for example, the American Psychological Association [APA] style). This paper shows you how to change the output of any SAS procedure to conform to any journal or corporate style. You'll learn how to save data from any SAS procedure, change the data format using the DATA STEP, and dynamically create a format based on your data. You'll also use the SAS Output Delivery System (ODS) inline formatting functions and style overrides, and produce several output formats, including HTML, RTF (for Microsoft Word), PDF, and Microsoft EXCEL files. Finally, you'll learn how to create and deliver the output on-demand using SAS server technology. This paper is appropriate for all SAS skill levels |
dataset, proc step, least squares mean, proc report, proc mixed, macro variable, sas output, excel, proc means, repeated measures, ods output, output delivery system, treatment effect, repeated measures model, character variable, output, sample, proc format, ods excel | source | 37 |
| 252 | Mapping Roanoke Island Revisited: An OpenStreetMap (OSM) Solution | Barbara B. Okerson | In a previous presentation, SAS was used to illustrate the difficulty and solutions for mapping small pieces of coastal land that are often removed from map boundary files to smooth boundaries. Roanoke Island, one of the first areas of the current United States to be mapped (1585), was used as an example since it is smoothed out of many current maps. While these examples isolated Roanoke Island, they didn't provide detail beyond city names on the map. Originally limited to SAS Visual Analytics, SAS now makes available background maps with street and other detail information for SAS/GRAPH using open source map data from OpenStreetMap (OSM). This paper will review the previous solutions and then look at how to map Roanoke Island using SAS/GRAPH and OSM |
macro variable, proc gmap, proc step | source | 12 |
| 253 | Maximizing Metadata Management | Lewis Mitchell | As businesses strive to meet the regulatory and internal demands for strong governance and data lineage there is a necessity to gain as much value as possible from the information they have. For those using SAS Data Integration Studio there is often a wealth of information stored in metadata that can be of significant value if properly harnessed. This is the first paper in the Metadata Management series and it focuses on the concepts and building blocks required to begin managing metadata | metadata management, temporary dataset, macro definition | source | 19 |
| 254 | Measuring Response to the Ultimate Driving Machine: Consumer Sentiments and Brand Advertising | Amit K Ghosh, Goutam Chakraborty, Praveen Kumar Kotekal |
Increasingly, customers use social media and other Internet-based applications (e.g., review sites) to voice their opinions and to express their sentiments about brands. These reviews profoundly influence brand performance, either directly (by affecting consumer behavior) or indirectly (by generating positive or negative word-of-mouth through online social networks.) We present a methodology that can be used to collect textual reviews from popular brand review sites and discussion boards. These reviews are then analyzed to uncover brand sentiments. Strategic implications are discussed as we explore the influence of actual product experience and brand expectations (that are shaped by advertising) on brand sentiments. In order to demonstrate the utility of our methodology, we first collected data using customized Python code from Edmunds.com - a popular review site, where car owners post detailed evaluations of the cars they have purchased | proc freq, regression analysis, estimate, data mining | source | 8 |
| 255 | Medical Appointments: Show/No-Show | Shubham Panat | Presentation | source | 4 | |
| 256 | Merge with Caution: How to Avoid Common Problems when Combining SAS Datasets | Joshua M. Horstman | Although merging is one of the most frequently performed operations when manipulating SAS datasets, there are many problems which can occur, some of which can be rather subtle. This paper examines several common issues, provides examples to illustrate what can go wrong and why, and discusses best practices to avoid unintended consequences when merging | merge statement, by group processing, in=, out=, set statement, dataset, if-then-else, subsetting if, sas log, pdv, proc sql, length statement | source | 16 |
| 257 | Mile-High Visual Analytics: Ways to Enhance Reports and Dashboards | Scott P. Leslie | The recent versions of SAS Visual Analytics include enhancements to several useful features that can elevate your reports and dashboards from good to great. This presentation describes workable solutions to common obstacles faced by report developers and data scientists. Topics inc lude the use of parameters, when and how to calculate items, adding dynamic chart titles and creating hierarchies to add drill-down functionality. This paper is suitable for users of all experience levels and demonstrates how to optimize SAS Visual Analytics to elevate your reports and dashboards to the next level | bar chart, line chart, time series analysis, series plot | source | 13 |
| 258 | Mine Mass Fragments Using SAS and Python for Metabolite Identification of Antibody-Drug Conjugates | Hao Sun, Kristen Cardinal |
Presentation | source | 10 | |
| 259 | Minimum Information for Training a Classifier | Catherine Halsey | Presentation | source | 5 | |
| 260 | Mitigating the Effects of Class Imbalance Using SMOTE and Tomek Link Undersampling in SAS | Kyle Biron | When dealing with imbalanced data, many standard learning algorithms have trouble adequately learning the underrepresented class(es). One approach to mitigating the effects of class imbalance is using sampling methods to alter the training data in a way that makes it easier for the classifier to learn the class(es) of interest. Two such techniques are SMOTE, which generates new, synthetic minority class examples, and Tomek link undersampling (Tomek), which helps to clear majority class examples from class boundaries. Both methods were implemented in SAS along with a combination of SMOTE followed by Tomek link undersampling (SMOTE+Tomek) | credit card, roc curve, proc hpforest, data cleaning, type ii error, support vector machine, in=, type i error, card fraud, imbalanced data, neural network, sample, proc step, out=, training set, validation set, proc sql, proc freq, proc princomp | source | 20 |
| 261 | Model Life Cycle Automation Using REST APIs | Wenjie Bao | SAS has created a new release of its model life cycle management offerings. This release provides an open, RESTful API that enables easier and more customizable integrations. Applications, including third- party applications, can communicate with the model life cycle management system through the RESTful web service APIs. RESTful integration has become the new standard for web service integration. SAS APIs conform to the OpenAPI specification, so they are well documented and easy to understand. This article walks readers through the major steps of the model management life cycle: build, register, compare, test, compare, approve, publish, monitor, and retrain. Models that can participate in the model management life cycle are created in SAS Enterprise Miner, SAS Visual Data Mining and Machine Learning, or in open-source modeling tools such as the Python scikit-learn toolkit or Google TensorFlow | sas model manager, champion model, sas viya, score code, rest api, cas, training set | source | 17 |
| 262 | Model Selection Using Information Criteria (Made Easy in SAS ) | Wendy Christensen | Today's statistical modeler has an unprecedented number of tools available to select the "best" model from a set of candidate models. Based on a focused search of SAS/STAT procedure documentation, over 30 procedures include one or more information criteria as part of their output. It is, however, unusual for applied statistics courses to discuss information criteria beyond "smaller is better". The focus of this breakout session is twofold. First, I provide an accessible conceptual overview of what information criteria are and why they are useful for model selection. Second, I demonstrate how to use Base SAS functions to compile information criteria from multiple models into a convenient and easy-to-read format |
sample size, sample, proc mixed, proc step, feature selection, likelihood function, output, ods output, sas base, hypothesis testing, null hypothesis, maximum likelihood estimation, response variable, proc rank | source | 12 |
| 263 | Model Selection with Higher-Order Interactions in SAS MIXED and GLIMMIX Procedures | Yin Zhang, Nanhua Zhang |
Presentation | proc mixed, feature selection, macro definition, proc step | source | 11 |
| 264 | Model-Based Fiber Network Expansion Using SAS Enterprise Miner and SAS Visual Analytics | Nishant Sharma | Charter Communications is the second largest cable service provider in the nation and has started making huge investments to expand its fiber network in order to maintain a competitive edge in the industry. Thus, it is very important that this investment is managed in the most effi cient way by using a model-driven strategy to accomplish the following: identify the best go-to markets for prospects; determine the cost of expanding the fiber network to each business location; and get the maximum ROI for the company. Spectrum Enterprise used SAS Enterprise Guide, SAS Enterprise Miner, and SAS Visual Analytics to create and deploy logistic and linear regression models to help achieve these goals. The models help create a focused segmentation strategy for marketing campaigns, resource allocation for the telesales group, and an expansion strategy for the network construction team nationwide | regression analysis, proc reg, sas enterprise guide, logistic regression, scatter plot, logistic distribution, sas visual analytics | source | 5 |
| 265 | Monte Carlo K-Means Clustering | Donald Wedding | Presentation | data mining, macro definition, centroid, proc print, proc step, monte carlo, sample | source | 227 |
| 266 | Multi-Factor Authentication with SAS and Symantec VIP | Jody Steadman, Michael Roda |
Organizations with strict security requirements might require that SAS integrate with a multi-factor authentication (MFA) solution such as Symantec's Validation Identification Protection (VIP). While most organizations already secure VPN access with MFA, the need for it in a SAS environment might stem from the sensitive nature of the data SAS is accessing or compliance-related requirements. MFA requires that an authenticating user not only provide a valid user name and password, but also a one-time password in the form of a security code or approve a push notification sent to a registered mobile application. This paper will show how components in both SAS 9.4 and SAS Viya can be configured to integrate with Symantec VIP, so that any authentication attempt into SAS would require that a user not only successfully enter a user name and password, but also successfully respond to the other configured authentication factors | sas viya, sas enterprise guide, data cleaning | source | 16 |
| 267 | Multiple Regression Diagnostics in SAS: Predicting House Sale Prices | Karen Bui | Presentation | multiple regression, regression analysis | source | 7 |
| 268 | My Experiences in Adopting SAS Cloud Analytic Services into Base SAS Processes | Steven Sober | The SAS Platform is greatly enhanced by SAS Cloud Analytic Services (CAS) and SAS Viya. As a Base SAS programmer, I want to share my experiences in adopting CAS into Base SAS processes that prepare data for modeling, reporting, and visualizations that are enabled for CAS | sas base, sas viya, proc step, sample, proc sql, cas, proc freq, proc means, cas action | source | 17 |
| 269 | My Top 10 Ways to Use SAS Stored Processes | Philip A. Mason | SAS Stored Processes are a powerful facility within SAS. Having recently written a book about SAS Stored Processes I have picked up the 10 best ways to use them so that I can illustrate the different abilities and ways to use them. I will explain how to run almost any code from your web browser, how to use them to export data from SAS to systems like Tableau, how to optimise their use for thousands of users, how to build mobile applications, build visualisations like seen in SAS Visual Analytics, how to make web services to integrate with other clients, and much more. All of these techniques are not generally well known, although they are not complex. If you don't already understand these techniques, then adding these to your skill set will enable to you to achieve much more | web service, excel, proc step, proc export, macro variable | source | 20 |
| 270 | Mylan's EpiPen Controversy: Leveraging Text Analytics During a PR Crisis | Nikhila Kambalapalli | The impact of social media on businesses is huge as the number of people sharing their unbiased opinions on platforms like Twitter has increased drastically over the past few years. Both good and bad publicity can spread to the masses in a matter of few seconds. Mylan N.V., an American global pharmaceutical company that acquired the right to market the EpiPen is one such company which has been appearing on the Social Media from the past one year. People tweeted their concerns when the price of EpiPens has been increased drastically in 2016, when more than 80,000 EpiPens have been recalled in March 2017 across multiple countries and in April 2017 when Sanofi filed antitrust lawsuit against Mylan over EpiPen[5]. The primary objective of this research is to know about the attitude of people towards the company and the industry and if prior reputation of the company (post price hike) influenced how people reacted to EpiPen product recall and lawsuit against EpiPen | data mining, dataset | source | 14 |
| 271 | Natural Disaster Planning and Recovery Using SAS Visual Analytics on SAS Viya | Mary Osborne, Adam Maness |
Planning for natural disasters, evacuation processes, and recovery is always a top priority for local governments and relief organizations. They must mobilize in an instant to set up shelters and provide supplies for victims. After a disaster, the often lengthy recovery phase begins. They must work to help people return to their homes by ensuring that areas are safe, while also working to restore power and communications. Using SAS Visual Analytics on SAS Viya and integrating with Esri tools, local governments and relief organizations can use location analytics to analyze storm predictions, search for shelter locations, plan evacuation routes, and enrich their existing data with Esri demographic data to stay informed and quickly make mission-critical decisions | dataset, proc step | source | 20 |
| 272 | Navigating the Analytics Life Cycle with SAS Visual Data Mining and Machine Learning | Susan Haller, Brett Wujek |
Extracting knowledge from data to enable better business decisions is not a single step. It is an iterative life cycle that incorporates data ingestion and preparation, interactive exploration, application of algorithms and techniques for gaining insight and building predictive models, and deployment of models for assessing new observations. The latest release of SAS Visual Data Mining and Machine Learning on SAS Viya accommodates each of these phases in a coordinated fashion with seamless transitions and common data usage. An intelligent process flow (pipeline) experience is provided to automatically chain together powerful machine learning methods for common tasks such as feature engineering, model training, ensembling, and model assessment and comparison. Ultimate flexibility is offered through incorporation of SAS code into the pipeline, and collaboration with teammates is accomplished using reusable nodes and pipelines | machine learning, sas viya, data wrangling, supervised learning, score code, champion model, feature engineering, dataset, data cleaning, missing value, imputation, feature selection, challenger model, model deployment, raw data, response variable, correlation, proc means | source | 29 |
| 273 | Navigating through Code with the DATA Step Debugger | Mina Chen | How would you know if there is a logic error in your program? What is a good way to determine whether there is a logic error in the program? Have you ever run an intricate DATA STEP and the results are not as you expect? Viewing the SAS log will not help you debug the program because the data are valid and no errors appear in the log. The DATA STEP Debugger (DSD) in SAS Enterprise Guide provides a nice interactive way to watch what's going on in the DATA STEP and quickly identify data and logic errors. It allows you to control the execution of a DATA STEP program, step through your program line by line, or suspend execution of your program at selected statements. It not only allows you to watch code execute, but also allows you to change the values manually in the program data vector as your program is running. The DATA STEP Debugger is a useful tool for all SAS users, from the beginner to advanced programmer |
sas enterprise guide, sas log, vital signs | source | 13 |
| 274 | Need Dynamic Interactions in Your Dashboards? Use Parameters! | Abhilasha Tiwari | SAS Visual Analytics is a robust in -memory environment for creating stunning interactive and dynamic reports. The quick summarization of key performance indicators and the option to navigate the dashboard help in governance and steering operations. Interactiv e reporting is much more accessible and fun when combined with the use of parameters. Parameters are variables whose value can be changed and referenced by any other object in the reports. When the value of the parameter changes, any report objects that reference the parameter detect the change accordingly. Parameters not only make the dashboards dynamic, but they also make the dashboards intuitive and handy for users. This paper covers how to use parameters to view your data dynamically in a graph or table over a specified span of days | source | 11 | |
| 275 | New Frontiers in Pricing Analytics | Sharat Mathur | Presentation | revenue management, data mining, machine learning, supervised learning, deep learning, outlier, recommendation engine | source | 22 |
| 276 | New Location Analysis and Demographic Data Integration with SAS Visual Analytics and Esri | Murali Nori | SAS continues to expand the location analytics capabilities that are offered in SAS Visual Analytics on SAS Viya. The goal of this paper is to present the new capabilities that can help you use the location information in business data. There are a variety of situations where location data is vital. Often business data can contain a large number of data points, sometimes in the order of 100,000 or a million. The preferred method for visualizing that many points on a geo map is by using geo-clustering. Another situation is when you are analyzing customers in a given location. Adding demographic data will improve insights. If you are performing a task such as analyzing crimes around a location in a city, using travel- time analysis and travel-distance analysis can provide valuable insights. If you need to represent various points of interest on geo maps, using symbols or icons helps make the map clearer | geospatial analytics, data profiling | source | 11 |
| 277 | New Ways to Incorporate Continuous Predictors in Generalized Linear Models | Anthony Salis, Angela Wu, Gordon Johnston |
Generalized linear models (GLMs) are used in many fields because they accommodate responses with a variety of distributions and regressors that can be either continuous or categorical. In the insurance industry, GLMs are often used for ratemaking to estimate the relative risk between policyholders. Traditionally, we specify linear or polynomial effects for continuous regressors. However, alternative approaches are available for incorporating continuous regressors when the relationship between the regressor and the response is complex and unknown. One approach is to bin the regressor, apply a weight of evidence (WOE) transformation, and specify the binned regressor as a linear effect. Another approach is to specify spline effects for continuous regressors. This approach provides greater flexibility in relating the regressor to the response | proc step, interval variable, degrees of freedom, estimate, proc genmod, proc freq, response variable, regression analysis, data cleaning, correlation, nominal variable, validation set, proc gam, training set, insurance claims | source | 15 |
| 278 | Non-metadata Methods to Keep Passwords and Sensitive Strings out of SAS Source Code and Logs | Thomas E. Billings | The SAS system provides metadata-based methods to keep most passwords and encryption keys out of source code and logs. Here we provide a brief overview of these methods and then describe alternative methods that do not require or depend on SAS system-provided metadata features. The first method is the simplest: function-style SAS macros that supply sensitive/confidential strings. For security, these macros must be written in a certain way and the executable file for the compiled macro must be vetted to make sure that no sensitive data are visible. This method is relatively secure when used in open code, but may not be secure if the invocation of the compiled macro is inside a wrapper macro with MPRINT enabled. The second method is an extension of work by Sherman and Carpenter (2009): a secure, compiled, encrypted macro that assigns LIBNAMEs to relational databases and ODBC. The macro uses an encrypted SAS data set that contains the login parameters | stored macro, macro variable, proc step, relational database, options statement, proc sql, sas enterprise guide, connection string, dataset, macro definition, error message, sas base, macro quoting function, libname statement, sas log, macro put, sample, excel, macro catalog, macro facility, select | source | 21 |
| 279 | Numeric Precision in SAS and Microsoft Excel: Similar Problem, Different Solution | Joe Matise | In this paper aimed at SAS programmers who have limited experience with DATA STEP programming, we discuss the basics of Data-Driven Programming, first by defining Data-Driven Programming, and then by showing several easy to learn techniques to get a novice or intermediate programmer started using Data- Driven Programming in their own work. We discuss using PROC SQL SELECT INTO to push information into macro variables; PROC CONTENTS and the dictionary tables to query metadata; using an external file to drive logic; and generating and applying formats and labels automatically. Prior to reading this paper, programmers should be familiar with the basics of the DATA STEP; should be able to import data from external files; basic understanding of formats and variable labels; and should be aware of both what a macro variable is and what a macro is. Knowledge of macro programming is not a prerequisite for understanding this paper's concepts |
proc step, macro variable, proc sql, sas programmer | source | 9 |
| 280 | ODS PDF Accessibility in SAS 9.4M5: Going Beyond the Basics to Create Advanced Accessible Reports | Greg Kraus | SAS 9.4M5 covers the basics of PDF accessibility, and many simple reports might be accessible without any additional work. However, if you produce reports that use advanced reporting features and accessibility is a requirement, you might need to provide additional manual accessibility remediation beyond the automatic accessibility features provided in SAS 9.4M5. This paper identifies advanced features that might require remediation and teaches you how to address those gaps using Adobe Acrobat Pro | proc step, proc report, ods pdf | source | 30 |
| 281 | ODS PDF Accessibility: How SAS 9.4M5 Enables Automatic Production of Accessible PDF files | Daniel O'Connor, Woody Middleton |
No longer do you need to begin the accessibility challenge with a raw, inaccessible PDF file. SAS 9.4M5 now provides a framework for creating accessible PDF files. This paper will not only explain the 'automatic' accessible features provided by SAS, but show programming changes to improve the accessibility of the PDF files. SAS creates PDF files that could best be described as uninformative to those with low or no vision. Now with SAS 9.4m5, SAS provides a framework for creating accessible PDF files. This paper will not only explain the 'automatic' accessible features provided by SAS but show "good practices" to improve the accessibility of the PDF files you create | proc step, proc report, proc print, proc tabulate, output delivery system | source | 9 |
| 282 | OpenID Connect Opens the Door to SAS Viya APIs | Michael Roda | As part of the strategy to be open and cloud-ready, SAS Viya services leverage OAuth and OpenID Connect tokens for authentication. OpenID Connect and its base technology OAuth 2.0 provide a simple security framework built on the HTTP protocol and are quickly becoming the de facto standard for public APIs. This paper describes how SAS Viya uses these standards and demonstrates how developers and administrators can use simple commands such as curl to authenticate to SAS Viya APIs | sas viya, rest api, proc http, proc step | source | 15 |
| 283 | Optimization Modeling with Python and SAS Viya | Sertalp Cay, Jared Erickson |
Python has become a popular programming language for both data analytics and mathematical optimization. With SAS Viya and its Python interface, Python programmers can use the state-of-the-art optimization solvers that SAS provides. This paper demonstrates an approach for Python programmers to naturally model their optimization problems, solve them by using SAS Optimization solver actions, and view and interact with the results. The common tools for using the optimization solvers in SAS for these purposes are the OPTMODEL and IML procedures, but programmers more familiar with Python might find this alternative approach easier to grasp |
odds ratio, dataset | source | 11 |
| 284 | Optimizing Inventory of Slow-Moving Products Using SAS Optimization | Malleswara Sastry Kanduri, Amy Mcarthur |
Aiming to use data and science to set service-level goals, Advance Auto Parts engaged CoreCompete to deliver a fully integrated service-level (Inventory) optimization system using SAS Inventory Optimization and SAS/OR software. The system has the ability to run inventory simulations and execute large-scale optimization for service-level goal optimization, leveraging the batch services on Amazon Web Services. A very large mixed integer optimization problem for inventory cost reduction is solved using the OPTMODEL procedure. The solution has the ability to recommend optimized service-level goals at the SKU/location level. System design integrates a simple Microsoft EXCEL user interface, data processing in Apache Hadoop, and optimization in SAS in the cloud and the dashboards in SAS Visual Analytics in order to review results. The end- to-end process flow for implementing simulations and optimization in large scale is discussed in this paper |
proc step, inventory optimization, excel, key performance indicator, data wrangling, odds ratio | source | 8 |
| 285 | Optimizing Red Hat GFS2 on SAS Grid | Tony Brown | Red Hat Global File System 2 (GFS2) is one of the most popular shared file systems for use with SAS Grid. This paper will serve as a "one-stop shop" for understanding GFS2 installation precepts as they relate to SAS Grid operations and performance. It will also cover GFS2 cluster limitations, recommended LUN and file system construction and sizes, underlying hardware bandwidth and connection requirements, software placement on SAS Grid nodes, and GFS2 tuning for Red Hat Enterprise Linux (RHEL) operating systems, versions 6 and 7 | sas grid, lookup, red hat | source | 8 |
| 286 | Organizational Contracts in Analytics of Things Ecosystems | Michael Goul | The impending convergence of big data, analytics and IoT has been referred to as an era of the Analytics of Things (AoT). Companies working in AoT ecosystems like smart cities and inter-organizational collaborations will build business relationships through contracts related to the sharing (or not) of data and analytics resources. Data and analytics ownership issues are already challenging the legal system, and with the proliferation of AoT, these challenges will only become more pervasive. This research addresses inter-organizational data and analytics ownership contracts. Findings provide guidance for both ecosystem owners and renters of multi-tenant AoT ecosystem resources. While recent research has reported that data and analytics sharing provide a significant innovation advantage, what we know and understand about ownership contracts lags behind | iot, big data | source | 6 |
| 287 | Oscars 2017: Text Mining and Sentiment Analysis | Karthik Sripathi | Presentation | source | 13 | |
| 288 | Outputting Your Data to Microsoft Excel Is Inevitable. So Is the SAS ODS Excel Destination | Claudia Hauck, Hezekiah Bunde |
Presentation | excel | source | 9 |
| 289 | Parallel Programming with the DATA Step: Next Steps | David Bultman, Jason Secosky |
The DATA STEP has been at the core of SAS applications and solutions since the inception of SAS. The DATA STEP continues its legacy, offering its capabilities in SAS Cloud Analytic Services (CAS) in SAS Viya. CAS provides parallel processing using multiple threads across multiple machines. The DATA STEP leverages this parallel processing by dividing data between threads and executing simultaneously on the data assigned to each thread. The LAG function, RETAIN statement, and the automatic variable _N_ require special attention when running DATA STEP programs in CAS. This paper describes each of these features, discusses how you can continue to use them with CAS, and compares performance with that in |
by group processing, lag function, proc step, missing value, big data | source | 16 |
| 290 | Penalized Variable Selection and Quantile Regression in SAS : An Overview | Russell M. Lavery | This friendly, and picture oriented, paper is about some new PROCs for modeling using penalized variable selection and some PROCS for building models that are a richer description of your data than you can get from OLS. The four PROCS we will cover are: Reg, GLMSelect, QuantReg and QuantSelect. The paper explains theory and gives examples of SAS code and output for four PROCS. The new penalized methods that are discussed in this paper help, only help, find a parsimonious model. They help us by using algorithms that are more stable, continuous and computationally efficient than stepwise | proc step, regression analysis, proc reg, proc quantreg, sample, elastic net, bootstrap, proc glmselect, ridge regression, goodness of fit, feature selection, estimate, proc glm, proc mixed, correlation, proc genmod, predictor variable, macro variable, maximum likelihood estimation, odds ratio, p-value, training set, feature importance, likelihood function, data cleaning | source | 28 |
| 291 | Performing Machine Learning Techniques in a Contextual Marketing Scenario | Francisco Capetillo, Alvaro Velasquez |
Although information for identifying high-potential customers is a key piece in a Customer Value Management strategy, it is hardly available in real-world databases. This paper describes an analytical framework developed to find the next best offer for each of the over five million customers in the m obile prepaid business of Telefonica Chile. This fra mework is based on the analytical life cycle methodology proposed by SAS best practices, which promotes the business understanding as a crucial control point in the use of advanced analytics in the real world. A supervised learning approach using artifici al neural networks in SAS Enterprise Miner is used to predict purchase behavior s in a window of time based on the state of a set of variables. Then the best offer for each customer is assigned, based on the scoring propensity for every single product | data mining, neural network | source | 11 |
| 292 | Personal Lending: What is Customer Price/Credit Optimization? Is Experimental Design Inevitable? | Yuri Medvedev | What is customer price/credit optimization? The author has faced this challenging question numerous times with various analytical and business management professionals. On the surface, the question itself can appear straightforward to both analytics professionals and business management facing the complex decisions around customer value management. However, in reality, variations in understanding of customer price and credit optimization concept are quite vast and have led to a high degree of confusion around the optimization concept within the financial industry. This paper will define the concept of customer pricing/credit optimization and the key role experimental design plays in the definition of the optimal solution. Eventually, any candidate for an optimal pricing/credit solution must be taken through a proper validation based on a proper experimental design | correlation, data cleaning, financial services, interest rate, price elasticity, default rate, clinical trial, phase i | source | 20 |
| 293 | Persuading with Data Stories: Is Jaws Just Misunderstood? | Jaime D'Agord | Presentation | source | 8 | |
| 294 | Phonetic Search Helps To Fight Risks | Yaobin Chen | Presentation | source | 6 | |
| 295 | Planning for Migration from SAS 9.4 to SAS Viya | Don B. Hayes, Spencer Hayes, Michael Shealy, Rebecca Hayes |
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 | 8 |
| 296 | Plot Your Custom Regions on SAS Visual Analytics Geo Maps | Jitendra Pandey | Presentation | source | 20 | |
| 297 | Power Analysis for Generalized Linear Models Using the New CUSTOM Statement in PROC POWER | John M. Castelloe | The CUSTOM statement that was added to the POWER procedure in SAS/STAT 14.2 extends the scope of supported data analyses to include generalized linear models and other extensions of existing capabilities. It works in concert with an exemplary data set and the SAS/STAT procedure that you plan to use for the eventual data analysis. This paper explains the method and demonstrates it for a variety of data analyses, including Poisson regression, logistic regression, and zero-inflated models. It also discusses how you can use CUSTOM statement options to refine the method for sample size inflation/deflation or for extra covariates. Prologue: The CUSTOM Statement in PROC POWER Comes In Where the LOGISTIC Statement Leaves Off Y ou're an industrial statistician working in a plant that rolls ingots into steel |
power analysis, logistic distribution, proc step, proc power, regression analysis, logistic regression, likelihood function, sample, sample size, poisson distribution, poisson regression, proc logistic, proc genmod | source | 17 |
| 298 | Predicting Crime Incidents in Los Angeles for the Year 2017 | Zaid Shaikh | Presentation | source | 4 | |
| 299 | Predicting Major League Baseball Game Outcomes | Justin Long | The game of baseball can be explained as a Markov Process, as each state is independent of all states except the one immediately prior. Probabilities are calculated from historical data on every state transition from 2010 to 2015 for Major League Baseball games and are then grouped to account for home-field advantage, offensive player ability, and pitcher performance. Using the probabilities, transition matrices are developed and then used to simulate a game play-by-play. For a specific game, the results give the probability of a win as well as expected runs for each team | transition matrix, markov chain, data cleaning | source | 6 |
| 300 | Prediction and Interpretation for Machine Learning Regression Methods | David Richard Cutler | The last 30 years has seen extraordinary development of new tools for the prediction of numerical and binary responses. Examples include the LASSO and elastic n et for regularization in regression and variable selection, quantile regression for hete roscedastic data, and machine learning predictive method such as classification and regressio n trees (CART), multivariate adaptive regression splines (MARS), random forests, gradient bo osting machines (GBM), and support vector machines (SVM). All these methods are implement ed in SAS, giving the user an amazing toolkit of predictive methods. In fact, the set of available methods is so rich it begs the question, "When should I use one or a subset of these methods instead of the other methods?" In this talk I hope to provide a partial answer to this question through the application of several of these methods in the analysis of several real datasets with num erical and binary response variables | regression analysis, predictor variable, proc hpforest, sample, credit card, multiple regression, proc hpsplit, proc reg, residual, logistic regression, proc gam, machine learning, feature importance, logistic distribution, proc step | source | 16 |
| 301 | Preparing Students to Take the Base SAS Certification Exam, Training Students to Think Like SAS | Herman Ray | Preparing students to the take the Base SAS Certification exam is challenging in a formal classroom setting. The diverse backgrounds of the students combined with the unique features of SAS requires careful planning and insightful examples. The course is part of the core required in the Master of Science in Applied Statistics (MSAS) program at Kennesaw State University. The execution of the course has changed over time due to the experiences of the students and feedback from industry. The topics discussed in this presentation include an overview of the population that takes the class at Kennesaw State University, the technology available to the students, the current organization of the course, the materials provided by KSU, the materials and support provided by SAS, as well as the pitfalls that were encountered in the past that informed some of the current design decisions. Examples of the materials are incorporated into the presentation, as well as challenges faced by the students | sas studio, data cleaning | source | 4 |
| 302 | Prescriptive Analytics: Using Optimization with Predictive Models to Find the Best Action | Mamdouh Refaat | Presentation | source | 81 | |
| 303 | PROC FCMP: A Powerful SAS Procedure You Should Be Using | Bill McNeill | The FCMP procedure is the SAS Function Compiler procedure. As the name suggests, it enables you to create and save functions (and subroutines) that can be of varying size and complexity. The functions and subroutines can then be integrated with other built-in SAS functions in your SAS programs. This integration simplifies your SAS programming and facilitates code reuse that can be shared with your fellow SAS programmers. You should be using this procedure. The modularity of your code will increase. The readability of your code will increase. The maintainability of your code will increase. Your pro ductivity will increase. Come learn how this powerful procedure will make your programming life simpler |
proc fcmp, proc step, hash table, macro definition, where statement, sas function, sas output, missing value | source | 17 |
| 304 | PROC SORT (then and) NOW | Derek P. Morgan | The SORT procedure has been an integral part of SAS since its creation. The sort-in-place paradigm made the most of the limited resources at the time, and almost every SAS program had at least one PROC SORT in it. The biggest options at the time were to use something other than the IBM procedure SYNCSORT as the sorting algorithm, or whether you were sorting ASCII data versus EBCDIC data. These days, PROC SORT has fallen out of favor; after all, PROC SQL enables merging without using PROC SORT first, while the performance advantages of HASH sorting cannot be overstated. This leads to the question: Is the SORT procedure still relevant to any other than the SAS novice or the terminally stubborn who refuse to HASH? The answer is a surprisingly clear "yes". PROC SORT has been enhanced to accommodate twenty-first century needs, and this paper discusses those enhancements |
proc sort, proc step, sas programmer, proc sql, sample | source | 10 |
| 305 | Producing a Format Library and Test Data for Case Report Forms Using a Data Definition Table | Ellen Dematt | Presentation | source | 7 | |
| 306 | Ranking Between the Lines: A Macro for Interpolated Medians | Joe Lorenz | Presentation | macro definition | source | 12 |
| 307 | Reading, Wrangling, Visualizing, and Modeling the Surface Temperature of the Great Lakes | Laura Kapitula | Presentation | source | 7 | |
| 308 | Real-Time Image Processing and Analytics Using SAS Event Stream Processing | Frederic Combaneyre | Image processing is not new and has been here for a while. But now, with the advent of the Internet of Things, this has become one of the most promising and exciting applications of real-time streaming analytics technologies. Historically focused on character or face recognition, the image-processing world has evolved to include enhanced predictive and prescriptive analytics applications using real-time streams of rich image and video content. From predictive maintenance in manufacturing to health care or security, there are countless examples of image analytics domain applications. In this session, we explore how to implement image analysis with SAS Event Stream Processing and how to deploy image- processing analytic models created with SAS Viya | stream processing, real-time processing, deep learning, neural network, machine learning | source | 10 |
| 309 | Recent SAS 9.4 Middle Tier Platform Updates for Fun and Profit | Zhiyong Li | The SAS middle tier includes the middle-tier infrastructure software such as SAS Web Server, SAS Web Application Server, Java Message Service broker, and the system management software such as SAS Environment Manager, all of which are critical to all SAS products with a web frontend and all SAS middle-tier services. Since the last SAS Global Forum, we have made a lot of updates and significant improvements to the middle tier that are driven by customer requirements and input from consultants and SAS technical support. We believe it is extremely important to provide a detailed review of the updates s o that SAS administrators can better manage their SAS middle-tier environment and satisfy their corporate IT requirements with improved confidence and fun. This paper first reviews the key components of the SAS middle tier, and then discusses updates made in recent SAS 9.4 releases (mainly SAS 9.4M4 and SAS 9.4M5) | sas administrator, customer experience | source | 13 |
| 310 | Reducing Customer Attrition with Machine Learning for Financial Institutions | Nathaniel Derby | As financial institutions market themselves to increase their market share against their competitors, they understandably focus on gaining new customers. However, they must also retain (and further engage) their existing customers. Otherwise, the new customers they gain can easily be offset by existing customers who leave. Happily, predictive analytics can be used to forecast which customers have the highest chance of leaving so that we can effectively target our retention marketing toward those customers. This can make it much easier and less expensive to keep (and further cultivate) existing customers than to enlist new ones. A previous paper showed a simple but fairly comprehensive approach to forecasting customer retention. However, this approach fails in many situations with more complicated data. This paper shows a much more robust approach using elements of machine learning. We provide a detailed overview of this approach and compare it with the simpler (parametric) approach | data cleaning, machine learning, gain chart, macro definition | source | 14 |
| 311 | Reducing Traveling Times for the Cobb County Fire Department | Bogdan Gadidov, Yiyun Zhou |
Presentation | source | 4 | |
| 312 | Regime-Switching Models: Capturing Structural Changes in Time Series | Xilong Chen, Ji Shen |
Stock market conditions, government policy changes, or even weather patterns can be regarded as stochastic processes that are driven by unobserved regimes. A powerful tool to explore these behavioral patterns is the regime-switching model (RSM) that is offered in the HMM procedure and the associated action in SAS Econometrics software. This model, which is widely used in finance, economics, science, and engineering, has two characteristics: it allows different parameter values for different regimes, and it models the transition probabilities between regimes. These characteristics enable it to fully capture the structural changes in the time series. This paper uses two examples to illustrate how you can use RSMs to better understand the regime patterns in your data and improve your economic analysis. The first example demonstrates how regime-switching autoregression (RS-AR) models help you characterize the volatility and dynamics of stock returns |
sample, likelihood function, time series analysis, estimate, significance level, regression analysis, probability distribution, feature selection | source | 14 |
| 313 | Regression Model Building for Large, Complex Data with SAS Viya Procedures | Weijie Cai, Robert N. Rodriguez |
Analysts who do statistical modeling, data mining, and machine learning often ask the following question: "I have hundreds of variables-even thousands. Which should I include in my regression model?" This paper describes SAS Viya procedures for building linear and logistic regression models, generalized linear models, quantile regression models, generalized additive models, and proportional hazards regression models. The paper explains how these procedures capitalize on the in-memory environment of SAS Viya, and it compares their syntax, features, and output with those of high-performance regression modeling procedures in SAS/STAT software |
regression analysis, proc quantreg, forward selection, proc gam, sas viya, data cleaning, proc genmod, logistic regression, poisson distribution, proc step, likelihood function, logistic distribution | source | 22 |
| 314 | Remodeling Your Office: A New Look for the SAS Add-In for Microsoft Office | Tim Beese | Millions of people spend their weekdays in an office. Occasionally they hang a new picture, or add a new piece of furniture. Eventually their office gets so full they need to reorganize it to find items quicker and work more efficiently. After 15 years, the time has come for a new user interface for the SAS Add-In for Microsoft Office. A redesigned ribbon allows quicker access to the most common operations. Where there used to be several task panes that each served a specific purpose, the task panes are now blended together into one panel that allows users to easily discover, execute, and manage SAS content. Come see these enhancements and many more in the latest version of the SAS Add-In for Microsoft Office | sample, excel | source | 9 |
| 315 | restAF: A JavaScript Library for Rapidly Developing SAS Viya Applications Based on SAS REST APIs | Deva Kumar | SAS Viya is like a Swiss Army knife when it comes to allowing all types of clients to talk to it -Java, Python, Lua, R, and, of course, SAS. In addition to using these language-based APIs, SAS also publishes APIs that conform to REST best practices. The well-designed APIs in SAS Viya make it relatively easy to build complex web and non-web applications with SAS Viya as the engine of your application. The applications can be solely based on SAS Viya capabilities or users can do a mashup of the advanced SAS Viya analytics with their other products and capabilities. restAF is a light-weight, UI- framework agnostic, JavaScript library designed to be easy-to-use for app developers. restAF takes advantage of the consistency of the REST APIs to reduce the responses from th e server into a standard form, and it also manages the data on the client side. This reduction enables developers to interact with the server in a repeating pattern regardless of which service the application is using | sas viya, rest api, sample, cas action | source | 19 |
| 316 | Results of the Application of SAS for Fraud Identification in the Public Administration of Brazil | Leonardo de Lima Aguirre | Presentation | source | 17 | |
| 317 | Retail Product Bundling: A New Approach | Bruno Nogueira | Affinity analysis is referred to as Market Basket Analysis in retail and e-commerce outlets application. It determines how often items are purchased together and the best possible groupings of products which are regularly bought by the customers in order to understand their behavior and preferences. This analysis allows to design effective marketing campaign and is appropriate for several kinds of decisions, such as cross-selling products, bundle of products, product placement and guide websites and loyalty program designs. Product bundling strategy can help either to sell two or more products or services together, or to optimize marketing content, such as product catalogues, including social media and internet advertising. The output of SAS Enterprise Miner Market Basket Node generates vast numbers of rules which allow us to analyze discovered patterns that are often used as business guidelines for marketing strategies | data mining, proc freq, market basket analysis, nominal variable, response variable, transaction data, sample, dataset, training set, proc means, recommendation engine | source | 18 |
| 318 | Ridge Regression and Multicollinearity: An In-Depth Review | Deanna Naomi Schreiber-Gregory | Presentation | ridge regression, regression analysis | source | 21 |
| 319 | Risk Pathways: Using Machine Learning Techniques of SAS Viya to Understand Customer Risk Drivers | Leigh Ann Herhold | With the new SAS Visual Analytics and SAS Visual Data Mining and Machine Learning capabilities on SAS Viya, institutions can more quickly and easily understand drivers of customer risk in a visually appealing way. Analysts can identify new opportunities for stakeholders to identify potential gains or losses by understanding drivers of risk and detecting new risk factors as they emerge over time. Using a combination of supervised and unsupervised machine learning methods, businesses can further understand and improve their own definition of customer risk as an organization. In this paper, Zencos shares its expertise in identifying high-risk attributes, understanding the relative importance of each driver, and recognizing key combinations of factors associated with risky behavior through an anti-money laundering case study | proc hpsplit, machine learning, enhanced due diligence, sas visual analytics, sample, data cleaning, risk classification, sas viya, neural network, model risk management, logistic regression, proc freq, regression analysis, logistic distribution | source | 14 |
| 320 | Robust Principal Component Analysis: Two Analyses in One | Anthony Salis, Zohreh Asgharzadeh, Kyungduck Cha |
Home telematics is a new and growing field in the insurance industry. Whereas traditional rating variables, such as amount of insurance and type of home construction, describe a home by its structure, telematics variables describe how the home is used. Telematics variables are derived from sensors placed in the home, such as thermometers, motion detectors, and smoke detectors. The data from these sensors are summarized in many different ways in order to provide potential model variables. For insurance companies, the ultimate goal is to use these data to predict future insurance losses for a group of homeowner's policies. However, such high-frequency, high-dimensional data are usually full of outliers, so the data require preprocessing. Two objectives of this preprocessing are to determine which homes behave as outliers and to reduce the dimensionality of the predictor variables | proc princomp, outlier, standard deviation, validation set, proc step, data cleaning, loss ratio, predictor variable, training set, logistic regression, machine learning, proc freq, regression analysis, logistic distribution, proc varclus | source | 11 |
| 321 | Robust Tuning for Machine Learning | Alex Glushkovsky | Tuning of models has become a very important topic. Machine learning models, especially neural nets, have many hyperparameters that may impact the outcome of the modeling process dramatically. Today, some machine learning products, such as SAS Visual Data Mining and Machine Learning, include autotuning features. This paper discusses (1) different tuning criteria of the robust model, (2) possible approaches toward optimal tuning such as full factorial, screening, response surface methodology, Taguchi L-designs, and stochastic design of experiments, and (3) their practical implementation in SAS Enterprise Miner. Similar to the Nelder-Mead technique, optimal tuning may include iterative design of experiments extending the factor's levels that reach their limits at interim optimal points | data cleaning, seed, machine learning, data mining, response variable, batch processing, standard deviation, regression analysis, confidence interval, model training, main effect, sample, logistic distribution, randomization | source | 22 |
| 322 | Role and Value of Visual Analytical Insights for Decision-Making throughout the Global Supply Chain | Shantanu Samanta, Paul Reynolds |
Presentation | sas base, predictive analytics, quality assurance | source | 11 |
| 323 | Role of Preventive Care and Lifestyle Changes in Chronic Diseases | Asish Satpathy, Satyajit Behari |
Chronic diseases such as diabetes and hypertension are widely prevalent in the world and are responsible for a significant number of deaths each year. In addition, treatments for such chronic diseases account for an extremely high healthcare cost. Research has shown that these diseases can be proactively managed and prevented while lowering the healthcare cost. Using SAS Analytics, we have mined a data sample of ten million consumers with over 1000 demographic and lifestyle attributes who have self-reported their chronic disease condition. We developed a model from a subset of demographic and lifestyle factors to demonstrate the accuracy of prediction of chronic diseases. The objective of this study is to understand if the currently accessible large volume of consumer data is useful in identifying top social factors which are associated with such diseases | sample, predictor variable, logistic regression, logistic distribution, regression analysis, data sampling, likelihood function, data cleaning, gain chart | source | 13 |
| 324 | Run-Time Validation of SAS Jobs: A Threesome of Error-Throwing Macros | Quentin McMullen | SAS programming can feel fraught with danger. Each time a program is executed, there is a risk that an error in the source data or the code will lead to undetected erroneous results. Runtime validation is a n offensive programming technique designed to decrease that risk by adding code to detect and r eport errors as a program runs. This paper presents three utility macros for runtime validation of SAS jobs: %DupCk detects duplicate key values in a dataset. %Assert detects values in a dataset that are invalid or unexpected. * %CheckRecordCounts detects the accidental deletion of records. By increasing the detection rates of common errors, these utilities increase the programmer's confidence in their results. Principles of runtime validation are discussed, as are principles of macro design encountered during the development of the macros | error message, record count, macro parameter, %macro parameters, macro variable, sysrc, sas programmer, data cleaning, by group processing, macro facility, proc sort, sas log, macro definition, if-then-else, keyword parameter, raw data, options statement, proc step, missing value, macro call, out=, variable list, merge statement | source | 18 |
| 325 | Running Parts of a SAS Program while Preserving the Entire Program | Stephen B. Sloan | Presentation | source | 8 | |
| 326 | Running SAS Viya on Oracle Cloud without Sacrificing Performance | Dan Grant | Presentation | sas viya | source | 12 |
| 327 | RWI not REI a Robust report writing tool for your toughest mountaineering challenges | Robert T. Durie | The degree of customization required for different kinds of reports and analyses used in presentations, documents, and spreadsheets varies from one organization or department to another. Often the results needed can be achieved by using a point-and-click tool. If that is not possible, then a coded approach is required. The amount of syntax required increases depending on the SAS procedure that you choose. However, to achieve complete flexibility, a new tool from SAS called the Report Writing Interface (RWI) is required. RWI, part of ODS, is new in SAS 9.4. Using DATA STEP statements, report structures, currently not achievable with other coded method can be generated. In addition, RWI enables you to incorporate style customization anywhere. The syntax uses a common dot notation that programmer s from multiple languages are familiar with. This seminar examines capabilities, examples, advantages and disadvantages of this latest reporting methodology |
data step, proc step, proc report, hash table, proc tabulate | source | 13 |
| 328 | SAS 9.4 on Microsoft Windows: Unleashing Kerberos on Apache Hadoop | Stuart J. Rogers | Do you maintain a Microsoft Windows server providing your organization's SAS 9.4 environment? Are you struggling to get Kerberos authentication out to Apache Hadoop working correctly for your SAS Enterprise Guide or SAS Studio users? This paper outlines the key steps for enabling Kerberos authentication between your users, your SAS 9.4 deployment on Microsoft Windows server, and your Hadoop data sources. Learn about the configuration changes you need to make, and learn about some effective trouble-shooting techniques to get your environment working and your users happy | sas studio, libname statement, sas enterprise guide, error message | source | 19 |
| 329 | SAS Analytics Innovation Lab at La Trobe University: Taking Machine Learning Innovation to Industry | Damminda Alahakoon | Presentation | machine learning | source | 23 |
| 330 | SAS Analytics Pro Running in a Docker Container in a Cloud Environment | Brian Bream, Frank Daniels |
Presentation | data science, customer satisfaction | source | 1 |
| 331 | SAS and Python: The Perfect Partners in Crime | Carrie Foreman | Python is often one of the first languages that any programmer will study. In 2017, Python was named as the world's most popular language by the IEEE Spectrum research group (Smit, 2017) and was named third in the list of popular programming languages taught within the UK (Murphy, Crick and Davenport 2017). In April 2017, SAS introduced the SASPy project, a package that can be installed on top of Python 3 to connect to a Python session to a SAS 9.4 or SAS Viya 3.1 workspace session (Hemedinger, 2017). This SAS workspace session connectivity allows any authenticated users to utilize many of the procedures available within the SAS environment, the majority of which are also available by using the Python programming language. This enables users to have the freedom to code in a language of their choice with the view of achieving the same results. SASPy also includes a method which can convert any compatible Python coding which is submitted, into the SAS coding equivalent | dataset, bar chart, proc sgplot, sas viya, numeric variable, proc print, proc step, sas programming, proc freq | source | 14 |
| 332 | SAS Arrays and Macros Make Processing Claims with Multiple Conditions Easier | Shavonne J. Standifer | The ability to translate medical claims data into actionable insights is an important step in deriving knowledge useful to make inferences about the healthcare system. A single patient can have numerous diagnosis codes per encounter. Developing methods to efficiently process this information is necessary in order to provide meaningful health statistics to providers that help them to better understand the needs of their populations with multiple conditions. SAS Array statements and the Macro facility help to streamline this process in an efficient way. This paper discusses, through examples, how Base SAS techniques, such as RETAIN, FIRST, LAST, DO LOOPS, and the LAG function can work together with SAS Arrays and Macros to transform a claims data file into population level summaries | insurance claims, data step, array, lag function, proc step, macro facility, sas base, sas programmer, proc sql | source | 10 |
| 333 | SAS Cloud Analytic Services Actions: A Holistic View | Mark Gass | SAS Cloud Analytic Services (CAS) provides the analytics that power SAS Viya. Just as SAS provides a wide range of capabilities through its extensive array of procedures, so CAS services are supplied by a wide (and growing) variety of action sets. And just as PROCs each define their own grammar and output tables, so CAS actions define their own programming interface in the form of input parameters and results. But the differences between conventional PROCs and CAS actions are also illuminating. Action execution occurs in parallel on the CAS cluster with reference to a server-side environment (a session, caslibs, tables). And when writing scripts or applications to invoke CAS actions, SAS and other popular industry programming languages are provided an equal footing-with each programming language client customized for its own ecosystem | sas viya, cas action, cas, proc step, mortgage, machine learning, default, support vector machine, sas programming | source | 20 |
| 334 | SAS Configuration Management with Ansible | Cameron Lawson | Presentation | source | 29 | |
| 335 | SAS Credit Scoring for Banking: An Integrated Solution from Data Capture to Insight | Ewa Nybakk | The banking sector experiences increased demands related to risk assessment because of the Basel Capital Requirements. Credit modeling and scoring is an important component of estimating the capital requirement, and banks face various challenges and needs related to this modeling. SAS Credit Scoring for Banking is an integrated solution that enables detailed analysis and improved prediction of credit risk with these challenges and needs in mind. This paper is based on experiences gained from implementing SAS Credit Scoring for Banking for a series of banks. We discuss the solution architecture in the context of challenges and needs related to credit modeling. The architecture overview highlights the SAS software included in SAS Credit Scoring for Banking and the stages at which the different software comes into play. We consider the benefits of SAS Credit Scoring for Banking and assess its scalability with respect to additional data sources and models | credit scoring, capital adequacy | source | 12 |
| 336 | SAS Enterprise Application Consolidation | Jan Bigalke, Anne Van Den Berge |
source | 14 | ||
| 337 | SAS Environment Manager: A SAS Viya Administrator's Swiss Army Knife | Trevor Nightingale, Michele Ryals |
The latest version of SAS Viya brings with it a wealth of new capabilities, and administrators are by no means left out of the party. The version of SAS Environment Manager that accompanies SAS Viya 3.3 significantly ratchets up what a SAS administrator can accomplish with this next generation HTML5- based web application. See new features of its top-level dashboard, including an at-a-glance widget that provides the health of all SAS Viya machines, services, and service instances in a hierarchical heat map. Witness the new Logged Issues widget highlighting recent errors with details to help diagnose and head off escalating problems | sas viya, sas 9, sas administrator | source | 14 |
| 338 | SAS Fraud Framework and MCMC in Government Estimation of Improper Payments of Social Benefits | Rodrigo Hildebrand, Aloisio Dourado Neto |
Brazil's Social Security monthly budget is about US$12 billion, paying benefits to 34 million people. In Brazil, the Federal Court of Accounts is responsible for auditing these expenses. In the last few years, the Tribunal de Contas da União (TCU) has found several improper payments, and has acted alongside the Social Security Agency to reduce such losses. In this paper, we describe how SAS Fraud Framework was used to estimate and monitor the evolution of the percentage of improper payments of social benefits granted. The TCU team conducted 685 interviews with experts from different areas (auditing or control). Based on the analysis of this data, they generated triangular probability distribution per respondent, applied Markov chain Monte Carlo (MCMC) simulations and fitted probability distributions (Kernel and Beta), aiming to estimate the incidence of irregularities | sample, mcmc, probability distribution, cluster analysis, by group processing, markov chain, monte carlo, internal audit, sas enterprise guide, histogram | source | 10 |
| 339 | SAS Grid Infrastructure on Amazon Web Services | Siddharthkumar Prasad | SAS Grid computing 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 paper emphasizes on deploying SAS Grid on cloud (AWS infrastructure) | sas grid, grid computing | source | 4 |
| 340 | SAS Grid Manager for High Availability: Implementation Best Practices | Edoardo Riva | SAS customers appreciate SAS Grid Manager for the many benefits that it provides to their SAS environment. Enhancing the availability of their critical services is certainly one of the most popular benefits. This paper presents the best practices for implementing highly available services and leveraging the new failover orchestration capabilities provided by the latest release of SAS Grid Manager | sas grid, audit trail | source | 13 |
| 341 | SAS in Style: Customizing Solutions and Reports with SAS Theme Designer | Ronald Page, Sherry Parisi |
SAS Theme Designer enables users to create their own custom application and report themes and to tailor the visual look of the applications they run and the reports they generate. Colors, fonts and images can be specified and the visual changes are presented within an embedded preview ap plication, or within an actual application when editing a theme within application context. Users can customize their applications and reports to use a unique organizational theme, or can customize their applications and reports based on an individual business requirement. This visual customization helps to provide for the customers' desire for colors, general branding, and logo integration | conditional do loop, bar chart | source | 19 |
| 342 | SAS Macros for Computing Causal Mediated Effects in Two- and Three-Wave Longitudinal Models | David P. MacKinnon, Matthew Valente |
Mediation analysis is a statistical technique for investigating the extent to which a mediating variable transmits the effect of an independent variable to a dependent variable. Because it is used in many fields, there have been rapid developments in statistical mediation. The most cutting-edge statistical mediation analysis focuses on the causal interpretation of mediated effects. Causal inference is particularly challenging in mediation analysis because of the difficulty of randomizing subjects to levels of the mediator. The focus of this paper is on updating three existing SAS macros (%TWOWAVEMED, %TWOWAVEMONTECARLO, and %TWOWAVEPOSTPOWER, presented at SAS Global Forum 2017) in two important ways. First, the macros are updated to incorporate new cutting-edge methods for estimating longitudinal mediated effects from the Potential Outcomes Framework for causal inference | effect size, sample, response variable, potential outcomes, confidence interval, propensity score, standard deviation, bootstrap, mediation analysis, ancova, power analysis, regression analysis, sample size, causal inference, covariance, predictor variable, proc calis, proc step, treatment effect | source | 18 |
| 343 | SAS Middle-Tier Performance Optimization | Glenn Horton | SAS middle-tier performance needs to be optimal to provide users with the best possible experience as well as to support growth in number of users. Optimal performance requires tuning. However, due to differences in the SAS web applications that are deployed as well as differences in the mix of applications in use, the tuning that is optimal for one deployment might not be optimal for another deployment. This session covers the tools that you can use to understand what's going on in a SAS middle tier, as well as the techniques you can use to tune the SAS middle tier once you understand what's going on | note | source | 7 |
| 344 | SAS ODS EXCEL Destination: Using the START_AT Suboption to Place Your Data Where You Want It | William E. Benjamin Jr. | The SAS ODS EXCEL destination statement option called "OPTIONS" has many sub-options. One of those sub- options is START_AT which allows you to place your output anywhere on the open output EXCEL worksheet. I will show you how the put your starting output data element into any row and column in the open worksheet |
excel, xls | source | 6 |
| 345 | SAS Studio: A New Way to Program in SAS | Lora D. Delwiche | SAS Studio is an important new interface for SAS, designed for both traditional SAS programmers and for point-and- click users. For SAS programmers, SAS Studio offers many useful features not found in the traditional Display Manager including integrated syntax help, one touch code formatting, and the ability to drag and drop variable and data set names into your code. SAS Studio runs in a web browser. You write programs in SAS Studio, submit the programs to a SAS server, and the results are returned to your SAS Studio session. SAS Studio is included in the license for Base SAS, is the interface for SAS University Edition, and is the default interface for SAS OnDemand for Academics. Both SAS University Edition and SAS OnDemand for Academics are free of charge for non- commercial use. With SAS Studio becoming so widely available, this is a good time to learn about it | sas studio, sas log, sas programmer, proc sql, sas library, sas enterprise guide, proc step, libname statement | source | 20 |
| 346 | SAS Visual Analytics 8.2: What's New in Reporting? | Riley Benson, Rajiv Ramarajan |
SAS Visual Analytics 8.2 extends the unified SAS user experience to data exploration and reporting , adds new features to report content , and introduces exciting new report object s. The user experience is built on a familiar and intuitive user interface at the report, page, and object levels. New features in report content include improvements in handling data, enhancements in graphs and tables, and more geographic capabilities . New report objects in the release are the Key Value object for infographic-like presentations, the Parallel Coordinates Plot object for more analytical visualization, and the Data -Driven Content object to manipulate externally created content within the point-and-click interface of the report | bar chart, proc freq | source | 17 |
| 347 | SAS Visual Analytics: Text Analytics Using Word Clouds | Jenine Milum | There exists a limit to employee skills and capacity to efficiently analyze volumes of textual unstructured data in a manner that provides actionable business insight. SAS provides several tools that provide the capacity to explore text; SAS Text Miner, SAS Visual Analytics , SAS Contextual Analysis, and SAS Visual Text Analysis. Utilizing Text Analytics & Sentiment Analysis within SAS Visual Analytics Word Clouds puts the availability and usefulness of these tools in the hands of a much broader audience. Visual Analytic Word Clouds deliver a point and click tool to explore volumes of unstructured data, identify patterns and create usable reports to aid in defining policies to improve business operations | unstructured data, sas visual analytics, dataset | source | 9 |
| 348 | SAS Visual Forecasting: A Cloud-Based Time Series Analysis and Forecasting System | Jerzy M. Brzezicki, Joseph H. Katz |
SAS Visual Forecasting, based on SAS Viya, is the next generation SAS product for forecasting. It provides a new resilient, distributed, scripting environment for cloud computing that provides time series analysis, automatic forecast model generation, automatic variable and event selection, and automatic model selection. SAS Visual Forecasting features a new graphical interface that is centered on the use of pipelines, a new microservices-based architecture, and a new fast, scalable, and elastic in-memory server environment based on SAS Cloud Analytic Services (CAS). It provides end-to-end capabilities to explore and prepare data, apply various modeling strategies, compare forecasts, override statistical forecasts, and visualize results. The workflow framework for model generation and forecasting is shared with SAS Visual Data Mining and Machine Learning and SAS Visual Text Analytics | time series analysis, rest api, dataset, data wrangling, sas viya, unstructured data, time series data, automatic variable, champion model, predictor variable, feature selection, proc freq | source | 21 |
| 349 | SAS Visual Investigator and SAS Visual Analytics: Bridging the Gap between Subject Matter Experts | Emily Chapman-Mcquiston | Health care payers constantly work in the world of big data. Because of the insights that big data can provide, payers are increasingly required to use it to improve outcomes for their patients, lower costs, and prevent fraud, waste, and abuse (FWA) in their networks. This enormous task requires contributors from diverse areas of the organization such as strategically minded executives, clinicians, auditors, and data scientists. Together, SAS Visual Investigator and SAS Visual Analytics provide a toolset that enables users to bridge the gaps between these different roles. This paper examines how advanced analytics, coupled with visualizations, make complex analytics readily available to subject matter experts across the health care industry | proc hpsplit, confidence interval, insurance risk score, outlier | source | 12 |
| 350 | SAS Viya : The Beauty of REST in Action | Sean Ankenbruck, Grace Heyne Lybrand |
The introduction of SAS Viya opened the SAS brand and its industry leading machine learning algorithms to the open source community. With new ability to connect open source interfaces such as Lua, Python, and R as well as REST APIs to CAS, this new platform becomes a powerful tool to include in any analytical toolbox. Using SAS Cloud Analytic Services (CAS), users can easily surface data and analytical model results to a WebApp through REST API connectivity. This capability is one key aspect Viya offers to provide real-time analytics. REST APIs provide a way to access SAS Viya over HTTP, and CAS capabilities are more efficient in communicating between front end and back end applications. Zencos will showcase SAS Viya's capabilities of leveraging the CAS server and connecting to REST APIs to surface data for real-time decision making using a case study where we score user data in real-time | sas viya, rest api, cas, proc hpsplit, cas action, machine learning, sample | source | 7 |
| 351 | SAS Viya Service Layer Architecture Overview | Eric Bourn | The SAS service layer has been completely re-architected from the ground up in SAS Viya. The monolithic web application server used in SAS9 has been replaced by modular microservices, which act as building blocks to help form a loosely coupled, scalable, and secure platform. This presentation focuses on the various technologies used within SAS Viya, from service registration to an event delivery architecture model that enables services to communicate with one another. Learn about a few of the key microservices included within SAS Viya and the benefits they provide | sas viya, rest api | source | 9 |
| 352 | SAS Viya: Architect for High Availability Now and Users Will Thank You Later | Jerry Read | You have SAS Viya installed and running for your business. Everyone loves it so much, they are using it more than you initially anticipated. Fortunately, you had the forethought to architect for high availability. Now you don't have to lose sleep as you keep your growing user community happy. This paper guides you through the process of creating high availability for the SAS Viya services, microservices, and web applications that support it | sas viya, machine learning | source | 6 |
| 353 | SAS/STAT 14.3 Round-Up: Modern Methods for the Modern Statistician | Maura E. Stokes | The latest release of SAS/STAT software has something for everyone. The new CAUSALMED procedure performs causal mediation analysis for observational data, enabling you to obtain unbiased estimates of the direct causal effect. Y ou can now fit compartment models for pharmacokinetic analysis with the NLMIXED and MCMC procedures. In addition, variance estimation by the bootstrap method is available in the survey data analysis procedures, and the PHREG procedure provides cause-specific proportional hazards analysis for competing-risk data. Several other procedures have been enhanced as well. Learn about the latest methods available in SAS/STAT software that can modernize your statistical practice |
bootstrap, mediation analysis, confidence interval | source | 9 |
| 354 | SASGF Template | Margaret A. Crevar | Any hardware infrastructure that is chosen by a SAS customer to run their SAS applications requires the following: a good understanding of all layers and components of the SAS infrastructure an administrator to configure and manage the infrastructure * the ability to meet SAS requirements-not to just run the software but to also allow it to perform as optimally as possible This paper will talk about important performance considerations for SAS 9 (both SAS Foundation and SAS Grid Manager) and SAS Viya when hosted in any of the available public clouds (Amazon AWS, Microsoft Azure, and Google Cloud, to name a few). It will also give guidance on how to configure the cloud infrastructure to get the best performance with SAS | sas viya, sas 9, sas grid, dataset | source | 9 |
| 355 | SASGF Template | Nick Welke | Scalability. Fault tolerance. Load balancing. High performance. High Availability. SAS Grid Manager. These are all phrases that commonly show up during analytics infrastructure conversations. However, maybe you are still confused about how all this relates to SAS Grid and what it would mean for your organization. When considering SAS Grid, many customers have similar points of confusion about how and if their SAS workflow would change, how do they connect to the Grid, how do you manage it, what do they ne ed to successfully implement it. In this paper, we cover questions we are asked most often about implementation, administration, and usage of a SAS Grid environment | sas grid, mp connect | source | 5 |
| 356 | SASLint: A SAS Program Checker | Igor Khorlo | Linters are programs that carry out static source code analysis, detecting certain bugs, coding rule deviations, and other issues. Linters also detect redundant or error-prone constructs that are nevertheless, strictly speaking, legal. Potential performance optimizations can also be checked by linters. This paper covers creating a modular linter for the SAS language consisting of a parser module, an analysis module that includes a list of rules, and a reporting module that displays issues found. It is possible to include and exclude rules, as well as develop your own rules, all of which makes the linter very flexible for any team with its own list of requirements regarding the source code and programming standards. The parser for SAS language grammar is based on the ANTLR Java parser. The tool is written in Java and SAS, which is why it can be integrated into any SAS environment | sas log, where statement, macro definition, sas expression, options statement, macro facility, proc sort, proc sql | source | 16 |
| 357 | Scalable Cloud-Based Time Series Analysis and Forecasting | Thiago Quirino, Michael J. Leonard, Ed Blair |
Many organizations need to process large numbers of time series for analysis, decomposition, forecasting, monitoring, and data mining. The TSMODEL procedure provides a resilient, distributed, op timized generic time series analysis scripting environment for cloud computing. It comes equipped with capabilities such as automatic forecast model generation, automatic variable and event selection, and automatic model selection. It also provides advanced support for time series analysis (in the time domain or in the frequency domain), time series decomposition, time series modeling, signal analysis and anomaly detection (for IoT), and temporal data mining. This paper describes the scripting language that supports cloud-based time series analysis. Examples that use SAS Visual Forecasting software demonstrate the use of this scripting language |
time series analysis, by group processing, class variable, proc step, big data, dataset, time series data, feature selection, data mining, seasonal decomposition, parallel processing, outlier, proc esm, cas, estimate, sysrc, proc freq, proc print, automatic variable | source | 22 |
| 358 | Seeing the Forest for the Trees: Part Deux of Defensive Coding by Example | Nancy Brucken, Donna E. Levy |
As statisticians and programmers, SAS is part of our daily life. Through assessing patterns, data quality, programming datasets, analysis displays or developing simulations, we need to determine the best ways to conduct our daily work, allowing us to see the forest for the trees. This paper provides guidance on quality defensive programming, efficient coding as well as good programming concepts. Programming no no's will also be discussed. The concepts discussed will allow us to navigate through the trees --- that is, seeing the trees for the forest. We may have been programming in SAS for weeks, months, years or decades. Regardless, we should continue to expand our skills and continue learning and updating our techniques. With this paper, we will provide reminders for paths lost in the past, as well as new tips to help us clear the brush from the trail | proc step, in=, keep statement, proc genmod, proc sql, out=, sas programmer, temporary dataset, cpu time, raw data | source | 10 |
| 359 | Sentiment Analysis of Netflix and Competitor Tweets to Classify Customer Opinions | Rucha Jadhavar, Agastya Komarraju |
With more than 310 million users worldwide, twitter is an important source of data for social media analytics. Each day, thousands of customers share their opinions and reviews about Netflix and its competitors in the video on-demand space. By analyzing the content of these tweets, companies can learn more about their customer preferences. This can help make businesses profitable by helping design effective social media campaigns and converting prospects into customers. It will also help companies make better -informed decisions to maintain their competitive edge. In this study, we analyze tweets for Netflix and its competitors such as Amazon Prime Video, Hulu, and HBO NOW. We demonstrate the use of multiple SAS tools to analyze these tweets, genera te quick summaries, identify different categories of tweets, and classify reviews | data mining, proc freq, parsing, data cleaning, histogram | source | 15 |
| 360 | Sentiment Analysis on YouTube Movie Trailer Comments to Determine the Impact on Box-Office Earnings | Rishanki Jain | The video-sharing website YouTube encourages interaction between its users via the provision of a user comments facility. This was originally envisaged as a way for viewers to provide information about and reactions to videos, but is employed for other communicative purposes including sharing ideas, paying tributes, social networking, and question answering. This study seeks to examine and categorize the types of comments made by YouTube users to understand how the sentiments of users can impact the first day revenue | data mining, parsing, data cleaning | source | 9 |
| 361 | Seven Agile Methods that Help Deliver Visualizations Agilely (and without Resorting to Being AdHoc!) | Shane Gibson | Presentation | source | 62 | |
| 362 | Shorter Waiting Time, Better Emergency Healthcare: Forecasting Stockholms' Emergency Room Visits | Martin Nordberg, Oskar Eriksson |
Emergency department (ED) overcrowding is a problem for EDs world-wide, but first and foremost it is a problem for the patients who are affected from it as ED overcrowding leads to higher morbidity and mortality for the patients. Forecasting of ED inflow for planning ED operations and steering patients streams in a region is an integral part of mitigating the overcrowding issue. In the study at hand, four different forecasting models for ED inflow are developed, validated and tested. Gradient boosting as modelling method yielded the best results for forecasting the coming 72 hours of ED inflow, mean absolute percentage error of 21 %. This is a sufficiently accurate model to implement in a new information system for planning ED operations in a health care region. | gradient boosting, neural network, response variable, data cleaning, proc means, time series analysis, machine learning, confusion matrix, datetime variable, series plot, validation set, raw data | source | 24 |
| 363 | Shredding Your Data with the New DS2 RegEx Packages | Will Eason | DS2's latest packages, PCRXFIND and PCRXREPLACE, wrap the functionality of previous PRX regular expression functions into sleek new packages. These just-in-time compiled regular expressions (or RegEx) can be used in multi-threaded environments to maximize throughput, while the object-oriented APIs simplify your connection to the powerful world of RegEx. Practical examples showcase using the new packages to execute RegEx on large data sets and include tips and techniques to get the most out of RegEx in distributed environments. We'll look at analyzing and filtering data sets using the new packages as well as using the fundamentals of text analytics to make processing large jobs faster. To top things off, we'll do the work with whichever smiling emoji best suits you, since the new packages are ready to handle your social media and international text processing needs | proc step, sysrc | source | 8 |
| 364 | Simple Methods for Repeatability and Comparability: Bland-Altman Plots, Bias, and Measurement Error | Maribeth H. Johnson, Jennifer L. Waller |
While a Pearson correlation coefficient may be a quick and easy measure to compare two different measurement methods or examine repeatability of a measure it is not the most appropriate nor does it give you insight into bias. Additionally, performing linear regression or tests for differences between the means is also not the best approach for determining whether two methods are comparable or a measurement is repeatable. Examining the difference between the measurements may not offer insight into the accuracy of the methods and a Pearson product moment correlation coefficient is not a measure of agreement but a measure of association. Altman and Bland (1983) suggested a graphical method and two other statistical tests to examine repeatability of measurement or whether two methods of measurement produced similar results | correlation, regression analysis, kendall correlation, proc step, missing value, proc corr, paired t-test | source | 12 |
| 365 | So Many Date Formats: Which Should You Use? | Kamleshkumar Patel | Nearly all data is associated with some Date information. In many industries, a SAS programmer comes across various Date formats in data that can be of numeric or character type. An industry like pharmaceuticals requires that dates be shown in ISO 8601 formats due to industry standards. Many SAS programmers commonly use a limited number of Date formats (like YYMMDD10. or DATE9.) with workarounds using scans or substring SAS functions to process date-related data. Another challenge for a programmer can be the source data, which can include either Date-Time or only Date information. How should a programmer convert these dates to the target format? There are many existing Date formats (like E8601 series, B8601 series, and so on) and informants available to convert the source Date to the required Date efficiently. In addition, there are some useful functions that we can explore for deriving timing-related variables | raw data, date variable, sas programmer, sysdate9, format statement | source | 6 |
| 366 | Some Tricks When Plotting Graphic Images Using PROC TEMPLATE in SAS Enterprise Guide , Part III | Kaiqing Fan | Presentation | sas enterprise guide, proc template, proc step | source | 30 |
| 367 | Square Peg, Square Hole-Getting Tables to Fit on Slides in the ODS Destination for PowerPoint | Jane Eslinger | An output table is a square. A slide in Microsoft PowerPoint is a square. The table, being the smaller square, should fit in the bigger square slide. Right? Well, not always. Despite the programmer's expectations, some tables will not fit on the slide created by the ODS destination for PowerPoint. It depends on the table. For instance, tables with, say, more than 10 rows or more than 6 columns might end up spanning multiple slides. But, just as with the popular children's toy, by twisting, turning, or approaching the hole from a different angle, you can get the peg in the hole |
proc step, proc report, proc freq, proc tabulate, proc genmod, proc glm, class variable, proc print | source | 18 |
| 368 | Streaming ETL of High-Velocity Big Data Using SAS Event Stream Processing and SAS Viya | Joydeep Bhattacharya, Manish Jhunjhunwala |
A typical ETL happens once in a day. How can you handle those use cases when ETL needs to happen in minutes? This paper presents a design using SAS Viya and SAS Event Stream Processing to fetch large volumes of data from a data source, process that data in the flow itself, and load it into Hadoop files (SASHDAT format) at a frequency that can be as high as every 15 minutes. The data source used here is Amazon Elastic Compute Cloud (Amazon EC2) performance data, which is fetched hourly, and Amazon EC2 metadata, which is fetched every 15 minutes. The amount of data generated every hour by a medium to large Amazon Web Services (AWS) consumer environment can push the traditional ETL process to its limit. In this design, data is fetched by a distributed data collector that is running on SAS Viya microservices | stream processing, real-time processing, staging area, sas viya, cas action | source | 6 |
| 369 | Supercharge Your Dashboards with Infographic Concepts Using SAS Visual Analytics | Travis Murphy, Falko Schulz |
A human's attention span is shorter than that of a gold fish-about eight seconds is all you have to capture their attention and create a reason for a viewer to stay on your dashboard. Therefore, a dashboard's visual appeal is even more important today than ever before, and this is where infographic concepts make a difference. Infographics deliver information with clarity and simplicity. Data is everywhere, and more report designers are using infographic elements to better communicate insight from the data. The boardroom can now benefit from what has become mainstream on popular news sites and social networks online. This paper shows you how to create infographic -inspired dashboards and reports that can be shared and dynamically explored by your teams using SAS Visual Analytics on SAS Viya. Supercharge your existing dashboards and reports with easy drag-and-drop wizards, while still providing the performance, repeatability, and scalability on massive data that your enterprise demands | data visualization, bar chart, sample, excel | source | 29 |
| 370 | Supercharging Data Subsets in SAS Viya 3.3 | Brian Bowman | SAS Viya 3.3 provides a powerful new table indexing capability that has the potential to significantly improve performance for data handling and analytics actions submitted to SAS Cloud Analytic Services (CAS). Effective use of table indexes can simplify big data scenarios, improve usability, and reduce computing resource requirements. Programming examples using the CAS procedure are presented using SAS Viya capabilities integrated into the latest release of SAS9. This information is beneficial to SAS Programmers, data scientists, data engineers, and others involved in configuring and deploying big data applications within SAS9 and SAS Viya |
elapsed time, sas viya, cpu time, proc freq, cas action, cas, big data, sample | source | 20 |
| 371 | Swimming Lessons for the Data Lake: Becoming a Marketing Analyst for the Next Ten Years! | Emma Warrillow | Presentation | data lake | source | 42 |
| 372 | Table Look-Up Techniques: Is the FORMAT Procedure the Best Tool for the Job? | Andrew T. Kuligowski | SAS programmers have employed user-written formats via PROC FORMAT to perform table look -ups for as long as PROC FORMAT has been available. There is no question that it is a viable technique - but is it the best way to attack the problem? This paper and associated presentation will look at how PROC FORMAT can be used to facilitate table look -ups. User gener ated Formats can be employed to assign descriptive labels to data values, create new variables, and identify unexpected values. PROC FORMAT can also be used to generate data extracts and to merge data sets. The computer resources necessary to execute this technique will be examined, and contrasted with alternate approaches such as the DATA STEP MERGE statement and SQL JOIN |
proc format, proc step, lookup, sas base | source | 9 |
| 373 | Take a Dive into HTML5 | Nathan Bernicchi | The SAS Output Delivery System (ODS) provides several options to save and display SAS graphs and tables. You are likely familiar with some of these ODS statements already, such as ODS RTF for rich text format, ODS PDF for portable document format, and ODS HTML for hypertext markup language. New to SAS 9.4, users have several additional ODS statements to choose from; among them is ODS HTML5. The ODS HTML5 statement differs from ODS HTML in that it uses HTML version 5.0 instead of HTML version 4.01. As such, the user can now take advantage of several new features introduced to the language. ODS HTML5 also uses a different default graphics output device, trading out the old standby portable network graphics (PNG) for the up-and-coming scalable vector graphics format (SVG). Throughout the paper, we will look at some of the differences between ODS HTML and ODS HTML5, with a focus on computer graphics |
ods html, output delivery system, sas programmer, proc sgplot, proc step | source | 19 |
| 374 | Taming Change: Bulk Upgrading SAS 9.4 Environments to a New Maintenance Release | Javor Evstatiev, Andrey Turlov |
The deployment options for the SAS 9 Platform have changed dramatically in the last years. Cloud based deployments are becoming a real alternative. On premise deployments are facing pressure to reduce operational costs. Downtimes are expected to be short to allow end users an almost seamless usage of the platform. Some organizations maintain several SAS installations (deployments) in multiple stages for separate business entities because of different regulatory, technical and business requirements. A widely used approach is to hotfix SAS environments only for "alert" level issues and introduce new functionality with maintenance level upgrades. Maintenance level upgrades are far more complex than deploying a hotfix, but they are smaller in scope, visibility, and complexity than a release upgrade. In the following paper we discuss strategies for maintenance level upgrades and how to successfully apply them to multiple SAS deployments | data cleaning, sas enterprise guide | source | 10 |
| 375 | Test-Driven Data Science: Writing Unit Tests for SASPy Python Data Processes | Stephen Siegert | Code needs tested == True. We've all been there-tweak this parameter, comment another line out-but how do you maintain the expected function of your programs as they change? Unit tests! SASPy is a Python interface module to SAS. It enables developers and data scientists alike to leverage SAS and Python by using both APIs to build robust systems. Oh yeah, you can submit SAS code, too! This paper walks through a test-driven development (TDD) approach to leveraging a data pipeline powered by SAS with Python. The pytest framework and unittest library are covered | proc sort, dataset, in=, proc step | source | 16 |
| 376 | Text Analysis Accuracy and Ease in SAS Text Miner versus the Python NLTK Sentiment Analysis Package | Jacob Braswell | Presentation | source | 10 | |
| 377 | Text Analysis and Cluster Analysis of Airplane Crashes from 1908 to 2009 | Ritesh Kumar Vangapalli | Presentation | cluster analysis | source | 12 |
| 378 | Text Analytics Lessons Learned: When MAUDE Doesn't Talk to You | Grace Heyne Lybrand, Reid Baughman, Ingrid Lundberg, Michael Swanson |
Data scientists are trained in analyzing data, specifically unstructured text data. Often, they need to mine insights from unstructured text data in unfamiliar industries. This paper will articulate the lessons we learned as we analyzed data and successfully partnered with subject matter experts to inform and guide our dive into the medical device industry. Using SAS Viya, we text mine the narratives submitted with medical device failure reports to the FDA. As you embark on your next text analytics project use our lessons learned to strengthen your partnerships and insights | proc freq, data science | source | 9 |
| 379 | The Anatomy of Clinical Trials Data: A Beginner's Guide | Venky Chakravarthy | An audience that wants to learn more about clinical trials will appreciate this comprehensive introduction to the role of a SAS programmer in Clinical Trials Phases I-III. The presentation will cover the role of Clinical Data Interchange Standards Consortium (CDISC) and the implications for capturing and reporting clinical trials data. Those who have just begun their careers in the Pharmaceutical industry will also benefit from attending this presentation. If you have been working in the Pharma industry for some time this will be a refresher and could lead you to discover hidden gems. The presentation will begin with an introduction to Human Clinical Trials. A short history of the evolution of standards in clinical trials will be provided. The construction of datasets based on current CDISC data standards will be the next step. The talk will be a microcosm of a clinical trial study. It will cover the study protocol, eCRFs (capture of data) and SAP (plan to analyze data) | sample, clinical research, sas programmer, phase iii, crf, data science, clinical trial, adam dataset, data standard, phase i, phase ii, adverse events, dataset, baseline, clinical database, laboratory data, ctcae grade, proc step, pharmaceutical industry, cdisc | source | 14 |
| 380 | The Art of Accurate Reports (with Examples from SAS Enterprise Guide) | Victoria Garcia | Presentation | sas enterprise guide | source | 13 |
| 381 | The Art of Defensive Programming | Philip R. Holland | txt, missing value, select, proc step, numeric variable, proc sql, macro variable, unique key | source | 9 | |
| 382 | The Baker Street Irregulars Investigate: Discoveries Using Perl Regular Expressions and SAS | Lisa A. Mendez, Peter W. Eberhardt |
A true detective needs the help of a small army of assistants to track down and apprehend the bad guys. Likewise, a good SAS programmer will use a small army of functions to find and fix bad data. In this paper we will show how the small army of regular expressions in SAS can help you. The paper will first explain how regular expressions work, then show how they can be used with CDSIC | character variable, data cleaning | source | 33 |
| 383 | The Cox Hazard Model for Claims Data | Tanya Kolosova | Claim management requires applying statistical techniques in the analysis and interpretation of the claims data. The central piece of claim management is claims modeling and prediction. Two strategies are com- monly used by insurers to analyze claims: the two-part approach that decomposes claims cost into fre- quency and severity components, and the pure premium approach that uses the Tweedie distribution. In this article, we evaluate an additional approach: time-to-event modeling. We provide a general frame- work to look into the process of modeling and prediction of claims using Cox hazard model. The Cox haz- ard model is a standard tool in survival analysis for studying the dependence of a hazard rate on covari- ates and time. Although the Cox hazard model is very popular in statistics, in practice data to be analyzed often fails to hold assumptions underlying the Cox model | hazard function, survival function, proc phreg, proportional hazards assumption, loss event, insurance claims, workers compensation, time-dependent covariate, hazard ratio, survival analysis, sample, markov chain, prior probability, posterior probability | source | 16 |
| 384 | The DO's of Fantasy Football | Alexandria Mccall | "Should I change my starting lineup?" This is the dreaded question all 40 million fantasy football participants have asked at one time or another. In particular, a player's injury report can make or break a fantasy football participant's lineup for the week. The unlikely combination of fantasy football with DO loops can assist in alleviating this concern. Often overlooked due to their simplicity, DO loops are a vital key to your SAS programming. When used correctly, they are a main driver for data or code productivity. In this paper, we discuss the use of DO loops with SAS features such as macro variables and text notifications. Each of these features plays a role in fulfilling your business needs at a faster and more efficient rate | macro variable, do loop, sas programming | source | 12 |
| 385 | The Ensemble of Neural Network and Gradient Boosting for the Prediction of Customer Profitability | Sunny Lam | This paper illustrates a two -stage approach for predicting customer profitability. The first stage is to build a dichotomous model to predict the customer's likelihood of future purchase. The second stage is to build a model, with continuous target variable, to predict the conditional future profit generated by the customer given he would make a purchase. Both stages involve the utilization of the gradient boosting and neural network data- mining techniques. In each stage, various ensemble combinations are tried and the one resulting in the lowest validation average squared error is chosen to be the stage model winner. The two model winners are subsequently used jointly for the prediction of future profit. In this analysis, Base SAS is used for data manipulation and SAS Enterprise Miner 13.2 is used for predictive modeling. It is evident that this two-stage modeling approach is robust in predicting customer profitability . Managerial and research implications will be highlighted | neural network, gradient boosting, data cleaning, sample, customer lifetime value, likelihood function, customer profitability, data mining, proc step | source | 15 |
| 386 | The Function Selection Procedure | Bruce S. Lund | The function selection procedure (FSP) finds a very good transformation of a continuous predictor to use in binary logistic regression. The FSP was developed in the 1990's for applications in biostatistics. The methodology is fully presented in the book by Royston and Sauerbrei (2008). In connection with their book Royston and Sauerbrei provided a SAS macro to implement FSP. This SAS macro has many advanced features but it is designed for the analysis of one variable at a time. A more efficient approach is needed for large scale applications in marketing and credit risk. This paper presents a n alternative macro %FSP_8LR which efficiently processes multiple predictor variables (for example, 50) with minimal passing of the data. Additionally, the methodology of FSP is extended in %FSP_8LR to the cumulative logit model | logistic regression, logistic distribution, proc logistic, degrees of freedom, macro variable, null hypothesis, hypothesis testing, proc step, likelihood function, residual, macro definition, regression analysis, alternative hypothesis, macro call, sample, proc freq | source | 11 |
| 387 | The Game Is Played Away from the Ball | Lee Manzer | Presentation | source | 23 | |
| 388 | The Hunt for Gravitational Waves and How to Visualize What No Person has Seen Before | Hans-Peter Bischof | Presentation | source | 29 | |
| 389 | The Impact of Positive School Experiences and School SES on Depressive Symptoms in Chinese Children | Yang Yue | Presentation | source | 3 | |
| 390 | The Ins and Outs of Internal and External Host Names with SAS Grid Manager | Paula Kavanagh | It is common to have network topologies introduce an external host name known only outside the firewall, while having a different host name that is used only internally or behind the firewall. Often, third-party network products are added after the deployment of SAS that require internal and external URLs, such as load balancers and firewalls. The SAS web applications and SAS Environment Manager components that are needed for SAS Grid Manager administration require additional configuration when different internal and external host names are used. It is important to understand the components of SAS Grid monitoring and management in SAS Environment Manager and where you should use each host name. This paper clarifies the configuration for successful communication between the components for a SAS Grid in SAS Environment Manager and external URLs | cas, sas grid, data cleaning | source | 12 |
| 391 | The Life Expectancy of Phone Numbers in Escort Ads | James Van Scotter, Joni Shreve, Denise J. McManus, Miriam McGaugh |
Most people would be surprised by the extent to which "the oldest profession" depends on the newest technology. Websites that sell classified ads for escort services enable human traffickers to reach customers while shielding them from law enforcement. The phone numbers that connect potential customers to the victims of human trafficking are an important clue for finding ties to the traffickers and pimps who profit from these activities. Our paper describes how we used SAS to take the first steps toward using phone numbers to uncover the traffickers. We used a custom web-scraping program to capture the text of nearly 700,000 classified ads from backpage.com. Then we examined the length of time that phone numbers appeared in ads, whether one phone number was used in different locations, and whether ad categories are associated with the amount of time a phone number remains active | proc reg, regression analysis | source | 7 |
| 392 | The New Era of Credit Risk Modeling and Validation | Boaz Galinson | Presentation | credit risk analyst, credit risk, data cleaning | source | 22 |
| 393 | The Power of Testing! An Automated SAS Enterprise Guide System for Testing | Karine Désilets | Over the years, at Statistics Canada, the testing tasks have been distributed between the employees: unit tests are done by IT, the certification of the calculations by methodologists and subject-matter analysts approve the process flow and data quality. Most of those tasks can be, and should be, automated; especially the tasks related to unit tests and regression tests. With the help of the Base SAS , SAS GRID and SAS/EG flow and a bit of EXCEL, this article presents an innovative way to automate testing. The main goal of this article is to use the power of SAS to avoid manual steps, maximise test coverage and minimi se the amount of tests. The article discusses the naïve heuristic-based methodology to automatically generate test cases for an application where XML files are used as input metadata. In large applications, it is impossible to cover the quality assurance of each path |
regression analysis, dataset, excel, sysrc, sas enterprise guide, proc compare, put statement, quality assurance, sas grid, macro definition, proc step | source | 23 |
| 394 | The Ultimate Data-Driven Guide to U.S. Canned Craft Beers Using SAS Enterprise Miner 14.2 | Linyi Tang | Presentation | data mining | source | 3 |
| 395 | There's No Reason to Hide! Discovering the Benefits of Using Hidden Columns in SAS Visual Analytics | Michael Drutar | SAS Visual Analytics 8.2 includes many features that report designers can leverage to create dynamic, interactive reports. One of the most powerful new features is the hidden data role that is available for many reporting objects. While initially the concept of hidden columns or data might be a bit foreign, just a little instruction can empower report designers to leverage it and create their most dynamic SAS Visual Analytics reports. This paper demonstrates the various methods in which one can use hidden data in report design, and the benefits each of these methods provides to report consumers. Examples include robust mapping between multiple data sources, creating improved color-mapped display rules, and, most importantly, dynamic linking to external web resources based on the values in one or more hidden columns in a list table | bar chart, data mapping | source | 12 |
| 396 | Time Series Analysis of Hate Speech in Social Media | David J. Corliss | Mining and analysis of social media text is a powerful tool for the analysis of thoughts, preferences, and actions of individuals and populations. While commonly used today in marketing and other business applications, Data for Good researchers have begun to apply these methods to the analysis of hate speech in social media. Different channels, including Twitter, Facebook, and Google searches, are found to have distinctive characteristics that affect the types of models and analyses each can support. This paper provides a step-by-step description of how to create and deploy a Twitter API and mine the data to extract tweets with user-selected search terms, including keywords and user name of the person or organization sending the tweet. This methodology is used to investigate hate speech, modeling the time series patterns with the aim of estimating the risk of subsequent acts of violence against persons targeted by the speech | time series analysis, exploratory data analysis, proc http, data science, proc step | source | 12 |
| 397 | Time Series Analysis: Did Dropping the Atomic Bomb Save Lives? | Rachael Becker | Presentation | source | 23 | |
| 398 | Time Series Feature Extraction | Michele A. Trovero, Michael J. Leonard |
Feature extraction is the practice of enhancing machine learning by finding characteristics in the data that help solve a particular problem. For time series data, feature extraction can be performed using various time series analysis and decomposition techniques. In addition, features can be obtained by sequence comparison techniques such as dynamic time warping and by subsequence discovery techniques such as motif analysis. This paper surveys some of the time series feature extraction methods and demonstrates them through examples that use SAS/ETS and SAS Visual Forecasting software |
time series analysis, time series data, feature extraction, proc esm, seasonality, dataset, proc princomp, data mining, machine learning, proc step, seasonal decomposition, proc freq | source | 18 |
| 399 | Tips and Techniques for Designing the Perfect Layout with SAS Visual Analytics | Ryan Norris, Brian Young |
Do you want to create better reports but find it challenging to design the best layout to get your idea across to your consumers? Building the perfect layout does not have to be a rocky experience. SAS Visual Analytics provides a rich set of containers, layout types, object sizing enhancements, and options that enable you to quickly and easily build beautiful reports. Furthermore, you can design reports that work across different device sizes or that are specific to a particular device size. This paper explores how to use the layout system and demonstrates what you can accomplish | bar chart, histogram | source | 22 |
| 400 | Tips and Techniques for Using the Random-Number Generators in SAS | Rick Wicklin, Warren Sarle |
SAS 9.4M5 introduces new random-number generators (RNGs) and new subroutines that enable you to initialize, rewind, and use multiple random-number streams. This paper describes the new RNGs and provides tips and techniques for using random numbers effectively and efficiently in SAS. Applications of these techniques include statistical sampling, data simulation, Monte Carlo estimation, and random numbers for parallel computation | sample, macro loop, monte carlo, proc step, bootstrap | source | 14 |
| 401 | Tips and Tricks Using the SAS Windowing Environment | Raviteja Mudunuru Venkata | If you work with SAS, then you probably find yourself repeating steps and tasks when developing a program. SAS windowing environment provides customizable toolbar and other options to help reduce time taken to perform few repetitive tasks. In this paper, we will discuss how to utilize the SAS windowi ng environment to perform a recurring task that's a click or a word away and will provide few tips and tricks to help increase productivity. This paper is targeted at new SAS users who might not be aware of all the wonderful options that SAS windowing environment options available to help increase productivity | macro variable, sas log, proc step, sas enterprise guide | source | 15 |
| 402 | To Catch a Thief: The Use of Analytics for Employee Fraud Detection | Bruce Bedford | Presentation | outlier | source | 21 |
| 403 | To Show or Not to Show? Using SAS Solutions to Maximize Patient Scheduling In Medical Clinics | Mia Lyst, Yijie Li |
At busy medical clinics, it is important to maximize the number of patients seen each day. Scheduled patients that fail to show are a common problem in medical clinics today that cause a decrease in revenue and disruption in daily operations. In order to combat this issue at several ambulatory clinics in the Southwest, machine learning algorithms were used to help the staff make informed decisions when scheduling patients. Using historical data, predictive models were built to identify patients that are not likely to show up to an appointment. By identifying these patients with high confidence, the staff can quickly fill these open slots to help the clinics serve more people at an efficient pace | champion model, proc hpsplit, data mining, sas visual analytics, regression analysis, sas enterprise guide | source | 8 |
| 404 | Tools Designed to Aid in Quality Control of Administrative Data Research Projects | Matthew Keller, Katelin B. Nickel |
Using administrative healthcare data for research, including health services, outcomes and comparative effectiveness research, is challenging for researchers because the data sources are not designed for research purposes. These data take different forms (e.g., hospital discharge data from individual hospitals, insurance claims data from health insurers) and are often reformatted by a secondary entity (e.g., individual state, government or commercial data warehouse) before being available for research. Together, these factors create substantial variation in the structure and content of the datasets. Therefore, when using administrative health databases for research purposes, the need for q uality control becomes an important focus for data scientists. This paper will discuss examples of quality control measures utilized by programmers at our center | quality control, create table, group by, proc step, macro definition, inner join, proc sql, insurance claims | source | 10 |
| 405 | Top 10 Tips for SAS Enterprise Miner Based on 20 Years' Experience | Melodie Rush | Over the past 20 years that I have been using SAS Enterprise Miner and helping analysts with it, I have learned and developed many tips and tricks for ease of use, productivity, and just plain clever implementation. In this presentation, I cover the evolution of SAS Enterprise Miner from the original SAS/AF software application to the current version that integrates with both open-source software and with SAS Viya. I share my top 10 tips for getting the most from using SAS Enterprise Miner, including sharing my favorite node that no one seems to know about and how to implement more complex modeling techniques |
data mining, feature selection, proc hpsplit, neural network, machine learning, sas viya, sas enterprise guide, score code, macro variable, time series analysis, sample, error message, regression analysis | source | 21 |
| 406 | Tracking Down the Culprit of a SAS Workspace Server Initialization Delay | Jessica Franklin | When a SAS Workspace Server session is launched, many components are activated and many actions are set in motion. Any number of components, for any number of reasons, can cause a delay in the workspace-server initialization step. Identifying the problem can be time-consuming. Meanwhile, users who are affected by the delays are left waiting. When users first report the warning signs of a sneaky culprit that is causing the delay, their administrators must then be swift and agile in their efforts to eliminate it. Tracking down the cause and making the necessary changes to resolve the delay is an important mission that can be extremely challenging. This paper is intended for SAS administrators, system administrators, and network administrators, and the goal is to help you work together to investigate the warning signs in order to determine whether there is an initialization delay and, if so, what is causing it | sas management console, packet loss, connection string | source | 23 |
| 407 | Troublemaker Records | Flora Fang Liu | Presentation | source | 7 | |
| 408 | Troubleshooting Your SAS Grid Environment | Jason Hawkins | A SAS Grid environment provides a highly available and resilient environment for your business. The challenge is that the more complex these environments become, the harder it can be to troubleshoot incidents and proactively optimize the configuration. Efficient troubleshooting of the environment builds user confidence and enables organizations to get the most value out of their investment. This paper focuses on the strategies that should be taken to identify and resolve issues within a SAS Grid environment. It provides a best-practice approach for making modifications to the default configuration files to understand what parts of the environment are in effect. The interpretation of this output can then be used to optimize or troubleshoot the environment. This paper is for SAS administrators who wish to learn more about troubleshooting a SAS Grid environment. It covers both Microsoft Windows and UNIX topologies using both shared and individual configuration directories | sas grid, sas enterprise guide, sas log, sas administrator | source | 19 |
| 409 | TV versus Internet: How Do Americans Consume News? | Ethan Stager | Twenty years ago, only 12% of Americans consumed their news through the Internet. Since then, innovations in the use of information and communication technologies have made news accessible in different formats to millions of people all over the world. Businesses involved in content creation and diffusion, such as media organizations and marketing firms, need to be aware of the constant change of consumer preferences to remain competitive. To this end, our goal was to examine demographic variables such as age, race, income, gender, and education, and their effect on the way Americans consume their news, more specifically through the Internet or television. We performed a logistic regression analysis on a dataset from the general social survey. Our analysis indicates that, over the last decade, age has become less impactful in predicting the source of news of Americans | proc step, proc sql, logistic distribution | source | 5 |
| 410 | Tweaking Your Tables: Suppressing Superfluous Subtotals in the TABULATE Procedure | Steve Cavill | Presentation | proc tabulate, proc step | source | 4 |
| 411 | Uncovering Big Opportunities in Big Data | Dmitriy Khots, Jeremy Wortz |
Presentation | big data | source | 19 |
| 412 | Understanding SAS K Functions | Leo Liu | SAS offers a variety of string functions that help you get the most out of your character data. However, the traditional string functions, such as SUBSTR and INDEX, assume that the length of a string in a SAS character column is always one byte. The K functions offer the power of SAS string function handling for all of your character data, no matter how long a character might be. This paper demonstrates the intrinsic characteristics of K functions by comparing them with the traditional SAS string functions. Your understanding of K functions will be further improved by looking at K functions from different perspectives. Based on a full exploration of the SAS K functions, you will be able to write SAS programs that can be run in any SAS environment using data from any language-"Write it once, run it anywhere!" | string functions, character variable, character functions, normalization, ebcdic, sas programming | source | 23 |
| 413 | Understanding Security for SAS Visual Analytics 8.2 on SAS Viya | Antonio Gianni, Faisal Qamar |
Have you been using SAS for more than a decade? Did you start using Base SAS, then progress to more advanced tools like SAS Enterprise Guide and SAS Data Integration Studio? Well, get ready for SAS Viya-the new cloud-enabled, in-memory analytics engine developed by SAS. SAS Viya is the new engine with which the next generation of SAS products are being delivered, with SAS Visual Analytics being one of many exciting new offerings. This paper broadly discusses several security topics of SAS Visual Analytics and SAS Viya with emphasis on application, data, and content authorization. The target audience for this paper are SAS Administrators implementing SAS Viya security, but anyone looking to understand these concepts can benefit | sas viya, access control, cas, workforce analytics, dataset | source | 29 |
| 414 | Understanding the Factors that Affect Customers' Choice of Sunscreen | Zihan Fan, Xiaojing Zhao |
Presentation | source | 26 | |
| 415 | Understanding The Influence Of Day Of The Week On Reviews Written | Sujal Reddy Alugubelli | Presentation | source | 14 | |
| 416 | University CSU: A Web-Based Space Utilization Tool for Investigating Trends in University Room Use | Hilary Melroy | Understanding utilization of existing space resources across a university campus has applications to both immediate an d long-term needs. This can be used to support plans for upgrades and expansions of the physical plant of the institution. Historical data can help to understand trends in space usage, provided it can b e collected and organized uniformly. Additionally, fundamental characteristics that define the potential us e of the elements must be included to understand trends in use of specialized spaces within the physical plant. With such information in hand, an interactive, web-based dashboar d for assessing campus space utilization (CSU) called University CSU has been developed as a tool for visualizing and optimizing room use across a university | proc step, data cleaning | source | 5 |
| 417 | Unleash the Power of the REPORT Procedure with the ODS EXCEL Destination | Devi Sekar | A new ODS destination for creating Microsoft EXCEL workbooks is available with SAS 9.4M3. This destination is an extremely easy and handy tool for producing ad-hoc as well as production EXCEL reports. The ODS EXCEL destination has several advantages over ODS ExcelXP tagset. With the ODS EXCEL destination, you can bring all those powerful features available with the REPORT procedure such as predefined styles, traffic-lighting, custom formatting, and compute block flexibility straight into your EXCEL reports. Once you start using the ODS EXCEL destination, you will quickly realize that the PRINT procedure is not sufficient to meet all the formatting demands for your EXCEL Reports. This paper covers various techniques that you can use with PROC REPORT and the ODS EXCEL destination, to make your EXCEL reports pretty and publication-ready! |
excel, proc report, proc step, ods excel, output delivery system, macro variable, options statement, xls, sas base | source | 25 |
| 418 | Unleashing the Power of SAS Visual Analytics: Warranty Analytics in the Automobile Industry | Prateek Kumar Singh, Saket Shubham, Shiv Kumar, Paresh Peter Rodrigues |
Performing Warranty Analytics in the Automobile industry can be a cumbersome task as we need a combination of warranty events data along with the production and sales data to understand the rates at which warranty events are received or the payments are made toward those events. However, the data needed to carry out this type of analysis typically comes from different transaction systems. One system records the production and sales of the vehicles and another system contains information about failures and warranty events for these vehicles. By using a combination of SAS products, we integrated the data from these two systems enabling our users to perform the desired warranty analytics. We exploited various unconventional programming techniques to combine and manipulate the data into the desired form for use with SAS Visual Analytics | sas visual analytics, missing value, create table | source | 16 |
| 419 | Using Arrays to Quickly Perform Fuzzy Merge Look-ups: Case Studies in Efficiency | Arthur L. Carpenter | Merging two data sets when a primary key is not available can be difficult. The MERGE statement cannot be used when BY values do not align, and data set expansion to force BY value alignment can be resource intensive. The use of DATA STEP arrays, as well as other techniques such as hash tables, can greatly simplify the code, reduce or eliminate the need to sort the data, and significantly improve performance. This paper walks through two different types of examples where these techniques were successfully employed. The advantages will be discussed as will the syntax and techniques that were applied. The discussion will allow the reader to further extrapolate to other applications |
hash table, conditional do loop, binary variable, do loop, set statement, key, length statement | source | 14 |
| 420 | Using Cluster Analysis to Maximize Workplace Design Effectiveness | Renae Rich | While it is generally accepted in the design industry that work spaces should be thoughtfully planned around the workers that occupy them and the types of work they do, available space, required design standards, and budget limit the number of unique workspace options that are feasible. By applying a cluster analysis method to survey data related to the type of work individuals do, job roles are categorized into work style groups with distinct workplace environmental needs and characteristics. These work style profiles are then used to inform and develop design strategies to best support the various types of workers in an organization, within spatial and budgetary parameters. This presentation outlines the considerations for selecting and combining variables for analysis, exploration and creation of the clustering pattern, investigation of unique cluster characteristics, as well as visualization techniques related to this method | cluster analysis, proc step, proc fastclus, proc sgplot, box plot, anova, proc glm, proc genmod, proc candisc | source | 10 |
| 421 | Using Cox Proportional Hazard Model to Predict Failure: Practical Applications in Multiple Scenarios | Sudeep Kunhikrishnan | Presentation | proc phreg | source | 4 |
| 422 | Using Customer Lifetime Value Models in SAS | Edward Malthouse | Presentation | customer lifetime value | source | 33 |
| 423 | Using Graph Template Language and R for High-Quality Publication Plots | Huei-Ling Chen, Jeff Cheng |
Presentation | graph template language | source | 14 |
| 424 | Using Hash Tables to Manage Your Macro Language Control Files | Arthur L. Carpenter | When dynamically controlling an application or process through the use of SAS macros, it is often advantageous to utilize a combination of metadata control files and list processing techniques. Typically the control file is read once into a macro variable list. But what if your information source is a large table and instead of accessing it sequentially you need to first look up specific information based on some supplied criteria? It would be very useful to be able to load the table into memory once and then only access those portions needed at a given point in time, and do this without creating any macro variable lists. Learn how this can be done using a combination of hash tables and FCMP functions and routines along with the macro language to create memory resident lookup tables | hash table, macro facility, proc step, macro variable, proc fcmp, macro call, lookup, call execute, dosubl, macro function | source | 9 |
| 425 | Using Information Value, Information Gain, and Gain Ratio for Detecting Two-Way Interaction Effect | Alec Zhixiao Lin | An interaction effect occurs when the impact of one attribute on a dependent variable depends on the value of another attribute. The presence of an interaction effect between two attributes often weakens, dissolves, or even distorts the predictive power of either or both attributes if used alone in predicting the outcome. Consequently, some valuable information is discarded or ignored. Examination of a limited number of attributes for an interaction effect can be done manually, but a similar task involving numerous attributes can be challenging. By employing information theory, this paper suggests a combinational use of Information Value, Information Gain, and Gain Ratio to detect a two-way interaction effect at the preliminary stage of variable screening and selection. We also expand the use of the methodology to continuous dependent variables. A SAS process is introduced that automatically screens all attributes in pair with minimal manual handling by users | interaction effect, proc step, interval variable, create table, log, proc sort | source | 19 |
| 426 | Using Maps with the JSON LIBNAME Engine in SAS | Andrew Gannon | This paper serves as an introduction to reading JSON data via the JSON LIBNAME engine in SAS. The engine includes an automap that dynamically reads the data into a generic data set. This paper goes into detail about specifying and creating your own map-effectively how to build custom data sets from the JSON data. It covers the input options that can be stated in the JSON map, as well as the subsequent effects those changes produce on the generated SAS data set |
libname engine, libname statement, dataset, character variable | source | 6 |
| 427 | Using Market Basket Analysis in SAS Enterprise Miner to Make Course Enrollment Recommendations | Shawn Hall | Market basket analysis, an example of association rule mining or affinity analysis, is used most widely in marketing to target customers by identifying the products they purchase in combination. Discovery of existing purchase patterns allows for better product placement, targeted marketing and product recommendations. This data mining technique has also been applied to the analysis of credit card and other service purchases in fraud detection, medical insurance claims and event promoti on. In the fall of 2013, the University of Oklahoma implemented flat rate tuition, in which students enrolled in 12 or more credit hours, pay a flat rate amounting to 15 credit hours of tuition and mandatory fees. Students paying flat rate, but enrolling in fewer than 15 credit hours, may bank those unused hours for enrollment in the following summer term. Here we consider the application of market basket analysis to student course enrollment | transaction data, market basket analysis, association rules, insurance claims | source | 8 |
| 428 | 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 |
| 429 | Using ODS TAGSETS.RTF to Fit Text and Multiple Graphs on One Page | Teresa Wilson | A common challenge is to fit a lot of information on a single page of an RTF document. When the usual solutions with the Output Delivery System (ODS) RTF statement don't work, the ODS TAGSETS.RTF statement offers an alternative solution for managing space on the page efficiently. For example, it is possible to fit two graphs on one page, along with a title and subtitle for the page and captions for each graph. This can be done by using the options that come with ODS TAGSETS.RTF and by creating a style template with the TEMPLATE procedure to decrease th e space between elements on the page |
output delivery system, proc template | source | 11 |
| 430 | Using Predictive Analytics to Improve a Student Advising System | Thanuja Sakruti, Youyou Zheng |
Student success is one of the most important topics for any institutions. In this paper, the institutional researchers discussed the predictive modeling process that could identify student at risk for a major STEM course at a top public university. SAS Enterprise Miner and Visual Analytics were applied to predict and visualize the student performance at individual level. This study allows student advisors and administrators to use predictive analytics and provide interventions for at-risk students in a timely manner | neural network, data mining, regression analysis, proc hpsplit, predictive analytics, logistic regression, logistic distribution, data cleaning | source | 13 |
| 431 | Using SAS 9.4M5 and the Varchar Data Type to Manage Text Strings Exceeding 32 kb | John Schmitz | Database systems support text fields much longer than the 32kb limit traditionally supported by SAS. These fields can be captured as substrings using SAS and explicit pass-through logic. However, managing the resulting substrings is often challenging. This paper will show how the varchar data type, introduced in 9.4M5, can provide improved functionality. The paper solves common management and storage issues associated with the extraction of longer strings, using varchar field type and the V9 engine | proc sql, sql pass-through, proc print, string functions, character functions | source | 8 |
| 432 | Using SAS Cloud Analytic Services with Raspberry Pi Time-Lapse Photography | Matthew Parmelee | While time-lapse image collection is a trivial problem to solve, it presents us with yet another data stream of IoT-type data that can be easily integrated and built out using the SAS Cloud Analytic Services (CAS) Image Action Set. Beginning with the release of the first wave of Raspberry Pi computers in 2012 (even earlier if we include the Arduino), the surge in popularity of low -cost, single-board computers has continued at breakneck pace over the past several years. While initially intended to be used in K- 12 computer science education, the Raspberry Pi has been featured prominently as the core component of a massive number of electronics projects. SAS Pittsburgh, currently located on the 29th floor of PPG Place, provides an excellent vantage point for time-lapse photography of the confluen ce of the Allegheny and Monongahela Rivers. In this session, we'll walk through how to set up and integrate these two technologies | machine learning, cas action | source | 6 |
| 433 | Using SAS Customer Intelligence 360 to Improve and Optimize the SAS Web Experience | Scott Calderwood | Discover how SAS uses SAS Customer Intelligence 360 to improve and optimize the SAS web experience for prospects and customers through data collection and analysis, content targeting and personalization, and A/B testing. These activities are designed to help strategically optimize the SAS web experience for end users by delivering more relevant, targeted experiences that result in significant business impacts for SAS | a/b testing, customer experience | source | 13 |
| 434 | Using SAS Enterprise Guide Projects in SAS Studio | Amy Peters | You've started learning the SAS Studio interface since it seems to be a good addition to how you already work with SAS-being able to access your stuff via your browser from other machines is useful. Opening your SAS program files (.SAS) is easy, but did you know you can also open your existing SAS Enterprise Guide projects (.EGP files) in SAS Studio? When you open a SAS Enterprise Guide project in SAS Studio, the process flows in the project are extracted and converted to process flows in SAS Studio. Any elements from the SAS Enterprise Guide project that are not supported by SAS Studio are either converted to different node types or are omitted from the process flow, and you can see the status in the conversion report that is created. This feature was experimental in SAS Studio 3.6 and is production in SAS Studio 3.7 | sas enterprise guide | source | 14 |
| 435 | Using SAS Enterprise Miner for Categorization of Customer Comments to Improve Services at USPS | Olayemi Olatunji | Delivering high-quality service and providing excellent customer experiences are performance outcome goals the U.S. Postal Service has established to measure corporate strategy success and continuous improvement efforts. Social media has opened the door for customer engagement and decision making. With the help of Twitter, Facebook, and Yelp, government agencies are more informed about how customers feel about their service and experience. Using Yelp data we will text mine comments about U.S. Postal Service customer service, retail service, mail delivery, and facility services using SAS Text Miner of SAS Enterprise Miner 7.1. The aim of this paper is to provide ways to categorize consumer comments regarding U.S. Postal Service services to improve the customer experiences at stations | parsing, data mining | source | 7 |
| 436 | Using SAS for Multiple Imputation and Analysis of Longitudinal Data | Patricia A. Berglund | "Using SAS for Multiple Imputation and Analysis of Data" presents use of SAS to address missing data issues and analysis of longitudinal data. Appropriate multiple imputation and analytic methods are evaluated and demonstrated through an analysis application using longitudinal survey data with missing data issues. The analysis application demonstrates detailed data management steps required for imputation and analysis, multiple imputation of missing data values, subsequent analysis of imputed data, and finally, interpretation of longitudinal data analysis results. Key SAS tools including DATA STEP operations to produce needed data structures and use of PROC MI, PROC MIANALYZE, PROC MIXED, and PROC SGPLOT are highlighted |
imputation, proc step, multiple imputation, missing value, sample, proc mianalyze, proc mixed, longitudinal data, proc mi, proc sgplot, random intercept, estimate, covariance, regression analysis, proc means, covariance structure, missing at random, fixed effects, proc calis, model training, p-value, proc transpose, do loop | source | 24 |
| 437 | Using SAS Fraud Framework for Government to Identify Fraud in Brazil's Federal Capital | Romulo Alvim | Presentation | source | 12 | |
| 438 | Using SAS OnDemand for Academics: Ten Tips for Success | Randy Mullis | SAS OnDemand for Academics is a free resource that is offered to students, professors and teachers, and independent learners. This online delivery model provides the opportunity to learn valuable skills in SAS programming as well as tools for developing knowledge of analytics. Because SAS OnDemand for Academics is cloud-hosted, not much setup is required. This paper shares tips to maximize the chance of success with using SAS OnDemand for Academics and to minimize inconvenience. These tips minimize inconveniences such as problems logging on, running programs that use Java, and handling issues with instructor data. Drawn from years of experience helping customers, the topics include categories for account setup, dealing with data, and differences among the products that are offered. The discussion also covers what you need to know about how SAS OnDemand for Academics is distributed geographically | sas studio, sas enterprise guide, data mining, libname statement, sas library, sas programming | source | 11 |
| 439 | Using SAS Text Analytics to Assess International Human Trafficking Patterns | Tom Sabo, Adam Pilz |
The US Department of State (DOS) and other humanitarian agencies have a vested interest in assessing and preventing human trafficking in its many forms. A subdivision within the DOS releases publicly facing Trafficking in Persons (TIP) reports for approximately 200 countries annually. These reports are entirely freeform text, though there is a richness of structure hidden within the text. How can decision -makers quickly tap this information for patterns in international human trafficking? This paper showcases a strategy of applying SAS Text Analytics to explore the TIP reports and apply new layers of structured information. Specifically, we identify common themes across the reports, use topic analysis to identify a structural similarity across reports, identifying source and destination countries involved in trafficking, and use a rule-building approach to extract these relationships from freeform text | sas viya, machine learning | source | 16 |
| 440 | Using SAS to Estimate Lagged Coefficients with the %partitionedGMM Macro | Kyle Irimata, Jeffrey Wilson |
Longitudinal data often includes time-dependent covariates, which must be accounted for with an appropriate model. Although a number of models have been proposed to analyze time -dependent covariates, most approaches constrain the effect of each covariate on the outcome to be constant across time. Irimata, Broatch, and Wilson (2018) introduced a partitioned generalized method of moments (GMM) model that used only valid moment conditions to estimate the diffe ring relationships within longitudinal data. This model provides insight into potential lagged effects of a given covariate on the response in a later time period. Each regression coefficient is estimated using moment conditions corresponding to the respective time period. Irimata and Wilson (2017) presented a SAS macro for fitting this partitioned GMM model for binary outcomes using SAS/IML software. We extended the %partitionedGMM macro to allow for either continuous or binary outcomes |
time-dependent covariate, estimate, correlation, regression analysis, macro call, longitudinal data | source | 8 |
| 441 | Using SAS to Fit AmeriFlux Data to Ecosystem Seasonality Models | Tracy Song-Brink | In ecosystem science research, we have several models to define season transitions in ecosystem gross productivity (GEP) and respiration (ER). These models were built with ecosystem data collected years ago. Thanks to the AmeriFlux ecosystem data community, now we have access to ecosystem data from more than 110 sites located across the Americas, compared with 15 sites in 1997. The purpose of this project is to fit the large volume of data that was not available to previous research to our existing models for model evaluation. We used the NLIN procedure for model fitting for each variable of one year at one specific flux data source. For each model fitting process, we used SAS macros to perform Grubbs' test for outlier detection and removal, and the GPLOT procedure for data visualization. SAS macros were written to automate the process of all input files, variables, and data years |
model training, proc nlin, proc gplot | source | 6 |
| 442 | Using SAS to Teach Credit Risk: From Undergraduate Studies to Professional Education | Cristian Bravo | Presentation | credit risk, credit scoring, sas base, sas enterprise guide, data mining, business intelligence, data science | source | 25 |
| 443 | Using SAS Visual Analytics to Explore the Western Kentucky University Twittersphere | Taylor Blaetz | Presentation | source | 10 | |
| 444 | Using SAS Visual Analytics to Solve Problems with Reporting Based in Microsoft Excel | Amanda Pasch | Using EXCEL as an analytics tool is pervasive and nearly inescapable in many corporate environments. While it can be an ideal tool for many basic ad hoc analyses, it can easily morph into an unscalable, error- prone, and undocumented reporting solution with significant limitations on end-user functionality. When this occurs, SAS Visual Analytics can provide a new and improved reporting solution that addresses many of EXCEL's pitfalls. This paper uses a real-world example to illustrate the advantages of moving EXCEL-based reporting to SAS Visual Analytics. Kaiser Permanente's Data and Information Management Enhancement (DIME) team replaced a highly manual EXCEL-based reporting solution that supported the needs our regional call center business partners with a SAS Visual Analytics solution |
excel, data validation rules | source | 7 |
| 445 | Using SAS Visual Investigator to Enforce Model Tuning Best Practices in a Regulatory Environment | Scott Wood | A common process of model tuning in the Financial Crimes space is sampling data to be submitted to subject matter experts for dispositioning to determine whether activities are suspicious. Establishing these target values is a key component to tuning models and being able to produce more efficient and effective models for catching misuse of the US financial system, as well as identifying terrorist financing activity. In addition, providing traceability of the process to establish thresholds and wei ghts for models is critical for regulated aspects of detecting suspicious activity. SAS Visual Investigator not only provides a toolkit for data exploration and analysis, it is a great tool for implementing analytics with a workflow process attached to it | sample, sample size, hyperparameter tuning, suspicious activity, rest api, binomial distribution, exact test | source | 15 |
| 446 | Using SAS/OR to Optimize Scheduling and Routing of Service Vehicles | Robert C. Pratt | An oil company has a set of wells and a set of well operators. Each well has an established amount of time required for servicing. For a given planning horizon, the company wants to determine which operator should perform service on which wells, on which days, and in which order, with the goal of minimizing service time plus travel time. A frequency constraint for each well restricts the number of days between visits. The solution approach that is presented in this paper uses several features in the OPTMODEL procedure in SAS/OR software. A simple idea and a small change in the code reduced the run time from one hour to one minute |
proc step, odds ratio | source | 27 |
| 447 | Using the CALIS Procedure in SAS to Confirm Factors Load for Bullying Scale for LGBTQ Youth in SC | Laura C. Hein | Presentation | source | 15 | |
| 448 | Using the COPULA Procedure to Simulate Multivariate Data | Bill Qualls | Simulating data is a common task for data scientists. In our scenario, our client needed to simulate a large number of observations of multivariate data based on a small number of real observations. When faced with such a task, an analyst will typically take a univariate approach, perhaps using PROC UNIVARIATE to produce a histogram of the data to determine a candidate distribution, as well as to obtain the best available estimates of the underlying parameters of the distribution. Having done so, they will then use one of the many distribution specific random number generators to simulate more rows. Data generated in this way should reflect the original distribution well. But there is a serious shortcoming with this univariate approach: it ignores any correlations which existed between the columns. A better method of generating such data is by using the COPULA procedure |
proc step, correlation, proc corr, standard deviation | source | 8 |
| 449 | Using the FCMP Procedure to the Fullest: Getting Started and Doing More | Arthur L. Carpenter | The FCMP procedure is used to create user defined functions. Many users have yet to tackle this fairly new procedure, while others have only attempted to use only its simplest options. Like many tools within SAS, the true value of this procedure is only appreciated after the user has started to learn and use it. The basics can quickly be mastered and this allows the user to move forward to explore some of the more interesting and powerful aspects of the FCMP procedure. Starting with the basics of the FCMP procedure, this paper also discusses how to store, retrieve, and use user defined compiled functions. Included is the use of these functions with the macro language as well as with user defined formats. The use of PROC FCMP should not be limited to the advanced SAS user; even those fairly new to SAS should be able to appreciate the value of user defined functions |
macro facility, proc fcmp, proc step, macro function, macro variable | source | 11 |
| 450 | Using the IML Procedure to Examine the Efficacy of a New Control Charting Technique | Austin R. Brown | For many years, control charts have been utilized to monitor processes, improve quality, and increase profitability. However, the body of literature tilts overwhelmingly toward charts monitoring normally distributed processes. In practice, the underlying distribution of a process may not follow a normal distribution, and many of those techniques may not be most effective. Mukherjee, McCracken, and Chakraborti (2015) suggested three control charts for simultaneous monitoring of the location and scale parameters for processes following the shifted exponential distribution. This study examines their proposed, Shifted Exponential Maximum Likelihood Estimator-Max Chart, (SEMLE-max) and suggests utilization of penalized maximum likelihood estimators (MLE) instead of traditional MLEs because of unbiasedness and minimum variance among unbiased estimators | sample, exponential distribution, likelihood function, normal distribution, maximum likelihood estimation, proc iml, proc step | source | 8 |
| 451 | Using the LIFETEST Procedure to Calculate an Alternative to Strike Rate in Limited Overs Cricket | Keith G. Curtis | Presentation | outlier, censoring | source | 5 |
| 452 | Using the ODS EXCEL Destination with SAS University Edition to Send Graphs to Microsoft Excel | William E. Benjamin Jr. | Presentation | excel, cdisc | source | 19 |
| 453 | Using UNIX Shell Scripting to Enhance Your SAS Programming Experience | James Curley | Presentation | sas programming | source | 19 |
| 454 | V is for Venn Diagrams | Kriss Harris, Richann J. Watson |
Would you like to produce Venn diagrams easily? This paper shows how you can produce stunning two -, three-, and four-way Venn diagrams by using the Graph Template Language, in particular the DRAWOVAL and DRAWTEXT statements. From my experience, Venn diagrams have typically been created in the pharmaceutical industry by using Microsoft EXCEL and Microsoft PowerPoint. EXCEL is used to first count the numbers in each group, and PowerPoint is used to generate the two- or three-way Venn diagrams. The four-way Venn diagram is largely unheard of. When someone is brave enough to tackle it manually, working out the numbers that should go in each of the 16 groups and entering the r ight number into the right group is usually done nervously! |
create table, macro variable, full join, macro call, proc step, proc sql, graph template language, excel, macro definition | source | 22 |
| 455 | Validating User-Submitted Data Files with Base SAS | Michael A. Raithel | SAS programming professionals are often asked to receive data files from external sources and analyze them using SAS. Such data files could be received from a different group within one's own organization, from a client firm, from a Federal agency, or from some other collaborating establishment. Whatever the source, there is likely to be an agreement on the number of variables, variable types, variable lengths, range of values, and variable formats in the incoming data files. Information Technology best practices require that the receiving party perform quality checks (QC's) on the data to verify that they conform to the agreed-upon standards before the data are used in analysis. This paper presents a rigorous methodology for validating user-submitted data sets using Base SAS. Readers can use this methodology and the SAS code examples to set up their own data QC regimen | dataset, constraint, data cleaning, proc step, proc compare, audit trail, key, data validation rules, macro variable, missing value, proc append, proc datasets, sas log, interval variable, create table, sysrc, proc sql, nominal variable | source | 16 |
| 456 | Weather Data Cleansing for Energy Forecasting | Aubrey Condor, Rachael Becker |
Presentation: Energy forecasting has become widely applicable in the utility industry for system planning and operations. One of the main drivers of electricity demand is weather. Programs such as SAS Energy Forecasting use hourly temperature readings as an input to their forecasting models. A lack of quality temperature data would contribute to less accurate forecasts. Despite observable daily as well as yearly seasonal patterns in the data, temperature can be very volatile, making it difficult to accurately fill in missing data. In order to monitor and improve the quality of temperature data, we sought to create an algorithm in SAS Enterprise Guide to automate the process of identifying missing and bad data, as well as imputing cleansed data into a temperature series. We examined different methods for filling in short missing series and long missing series using 5 years of historical data from Virginia weather stations | source | 1 | |
| 457 | Web Metrics at Scale: Using Base SAS to Access Google Analytics APIs | Chris Hemedinger | With SAS 9.4M4 and later, it's finally easy (relatively speaking) to connect to complicated APIs like those supported by Google, and to gather information with an unattended batch process. The task is made possible by recent enhancements in the HTTP procedure and the new JSON library engine in SAS. The PROC HTTP enhancements make it easier to negotiate multi-step authentication schemes like OAuth 2. And the JSON engine makes it easier to parse JSON results into SAS data sets. Previous approaches relied on complicated techniques such as the GROOVY procedure or other tricks to call outside of SAS to drive these OAuth2 APIs and parse JSON. The recent enhancements make such tricks unnecessary and thus provide a cleaner approach with fewer moving parts. In this paper, I describe the four main steps needed to use Google APIs to gather web metrics data. The paper includes SAS code that you can adapt and use immediately with your own Google Analytics environment |
proc http, rest api, proc step | source | 11 |
| 458 | What's New at SAS for Health Care | Lina Clover | This paper highlights the SAS health care solutions SAS Real World Evidence and SAS Episode Analytics. While seamlessly integrated with the SAS Platform and SAS Visual Analytics, these solutions are designed to help a wide spectrum of health care organizations (providers, payers and insurance companies, pharma, and government agencies) to easily derive health care analytics and insights from administrative claims data and patients' electronic medical records. For SAS Real World Evidence, this paper covers built-in longitudinal patient views and the interactive cohort discovery tool, which can easily identify cohorts of patients with complex sequences of events and encounters (diagnoses, medical procedures, visits, lab test results, and drug exposures) from a pool of millions of patients with a query response time in seconds; pre-defined analytical models such as Readmission Analysis, Length of Stay Analysis, Risk Scores and Comorbidity Report , and Incidence and | exposure, risk adjustment, insurance risk score | source | 9 |
| 459 | What's New in SAS Data Connectors for SAS Viya | Salman Maher, Chris DeHart |
The latest release of SAS Viya provides an expanded set of features for accessing your data. This presentation provides an overview of the latest features available with SAS data connectors. During the presentation, we discuss and provide examples of the following new product capabilities: expanded support form more data sources pushing SQL queries from SAS Cloud Analytic Services (CAS) to databases * saving CAS tables to third-party data sources The examples are drawn from a wide variety of relational databases including, but not limited to, Microsoft SQL Server and IBM Db2 | sas viya, proc step, cas, cas action, proc sql, numeric variable, sysrc, where statement, cross join, sample | source | 25 |
| 460 | What's New in SAS Data Management | Nancy A. Rausch | The latest release of SAS Data Management provides a comprehensive and integrated set of capabilities for collecting, transforming, and managing your data. The latest features include capabilities for working with data from a wide variety of environments including Apache Hadoop, the cloud, a relational database management system (RDBMS), unstructured data, streaming, Apache Spark, and the SAS Cloud Analytic Services server. New in the release is support and integration into SAS Viya. This paper provides an overview of the latest features of SAS Data Management and includes use cases and examples for leveraging the latest product capabilities | sas viya, cas, data cleaning, macro variable, proc sql, numeric variable, data governance | source | 11 |
| 461 | Which Smart Phone to Choose? | Mohana Krishna Chaitanya Korlepara | Presentation | source | 5 | |
| 462 | Who Is Likely to Succeed: Predictive Modeling of the Journey from H-1B to Permanent US Work Visa | Shibbir Khan | Presentation | source | 5 | |
| 463 | Who's Your Daddy: Managing Parent-Child Hierarchies in SAS | Richard Collins | Reporting hierarchies underpin most, if not all, business reporting solutions. Accurate hierarchies enable reporting lines to quickly drill into and deal with issues, address key performance indicator (KPI) fluctuations, and of course, receive credit for effort expended. Without an accurate hierarchy, KPI reporting in large organizations would quickly become mistrusted and suffe r the dreaded loss of adoption and potentially become extinct. This paper shows you how to simply manage large, multi -level hierarchies, complete with change history, through a simple HTML5 interface built on top of the SAS Stored Process Web Application. Powered by the Boemska HTML5 Data Adapter for SAS (H54S) and the built-in hierarchical query functions of major database management systems (DBMSs) (Oracle and PostgreSQL), you can manage your business reporting hierarchy with just a few lines of Base SAS code | proc sql, dataset | source | 19 |
| 464 | Working Together for Public Health: Using SAS , Esri, and Tableau to Enhance Data Presentation | Jennifer L. Han, Miriam McGaugh |
Numerous clients receive clinical services at local public health departments every day. With the current climate of providing necessary services with fewer resources, data-driven decision-making is essential in public health. Many local public health data systems are antiquated and provide limited reports to public health employees. Innovative means of assessing and visualizing data to enable public health employees to quickly access timely information specific to their clients and community will enhance efforts at the local level. This presentation demonstrates how large volumes of county-level surveillance data and client data can be analyzed using SAS and meaningfully displayed in a dashboard using ESRI ArcGIS maps and Tableau software | health insurance, %if-%then-%else, inner join, macro loop, macro definition, regression analysis, sas enterprise guide, proc sql, create table, txt, proc step, stepwise selection, macro variable, excel | source | 135 |
| 465 | Working with Big Data in SAS | Mark L. Jordan | This paper demonstrates challenges and solutions when using SAS to process large data sets. Learn how to do the following: Use SAS system options to evaluate query efficiency Tune SAS programs to improve big data performance Modify SQL queries to maximize implicit pass-through Re-architect processes to improve performance Identify situations where data precision could be degraded Leverage FedSQL and DS2 for Parallel processing on the SAS platform for improved performance Full precision calculations with ANSI data types In-database processing with the SAS In-Database Code Accelerator Take advantage of SAS Viya and Cloud Analytic Services (CAS) for fast distributed processing | proc sql, libname engine, proc step, cpu time, parallel processing, in-database processing, sas log, sas base, drop statement, character functions, group by, if-then-else, sas library, options statement | source | 13 |
| 466 | Wow! You Did That Map With SAS?! Round II | Louise S. Hadden | This paper explores the creation of complex maps with SAS software. This presentation incorporates explores the wide range of possibilities provided by SAS/GRAPH and polygon plots in the SG procedures, as well as replays, overlays in both SAS/GRAPH and SG procedures, and annotations including Zip Code level processing. The more recent GfK maps now provided by SAS, that underlie newer SAS products such as Visual Analytics as well as traditional SAS products, will be discussed. The pre-production SGMAP procedure released with Version 9.4 Maintenance release 5 will be discussed in context |
proc step, proc gmap, proc sgplot, sample, sas base, dataset, macro variable, proc sql | source | 30 |
| 467 | Writing Code with Your Data: The Basics of Data-Driven Programming Techniques | Joe Matise | In this paper aimed at SAS programmers who have limited experience with DATA STEP programming, we discuss the basics of Data-Driven Programming, first by defining Data-Driven Programming, and then by showing several easy to learn techniques to get a novice or intermediate programmer started using Data- Driven Programming in their own work. We discuss using PROC SQL SELECT INTO to push information into macro variables; PROC CONTENTS and the dictionary tables to query metadata; using an external file to drive logic; and generating and applying formats and labels automatically. Prior to reading this paper, programmers should be familiar with the basics of the DATA STEP; should be able to import data from external files; basic understanding of formats and variable labels; and should be aware of both what a macro variable is and what a macro is. Knowledge of macro programming is not a prerequisite for understanding this paper's concepts |
proc step, macro variable, proc sql, sas programmer | source | 9 |
| 468 | Your Data Visualization Game Is Strong Take It to Level 8.2 | Brandon Kirk, Jason Shoffner |
Your organization already uses SAS Visual Analytics, and you have designed reports that show compelling data stories. The newest version of SAS Visual Analytics can give those stories a facelift through its clean, modern HTML5 interface and exciting new visualization features. Learn how to make the transition seamless while using the move as an opportunity to focus on the most compelling reports. We walk through the methodology and the automation techniques that we, SAS IT, used when we moved our own internal SAS Visual Analytics environment from 7.3 to 8.2 | cas, spk file, sas viya, sample, sas management console, data cleaning, user-defined format | source | 10 |
| 469 | ZIPpy Safe Harbor De-identification Macros | Abigail Chatfield | Presentation | create table, proc sql | source | 21 |
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