HOME
QUICK SEARCH
AUTHORS
KEYWORDS
HELP
BUGSSGF 2020 Papers - Index
372 papers. Source: SAS Global Forum conference proceedings.
| # | Title× | Authors× | Abstract× | Keywords× | Extras | Pages |
|---|---|---|---|---|---|---|
| 1 | %SURVEYCORRCOV Macro: Complex Survey Data Correlations for Multivariate Analysis and Model Building | David R. Nelson, Siew Wong-Jacobson |
SAS SURVEY procedures cover the main topics of descriptive statistics (MEANS, FREQ) and regression (REG, LOGISTIC, PHREG). But as the use of complex surveys evolve, particularly among students who often use this data due to its high quality and low price, adding even more analytics that are suitable for this data further opens its horizons. Twelve SAS/STAT procedures can use special SAS data sets with the CORR and COV options as input data for analyses such as PRINCOMP, FACTOR, and VARCLUS. Having this functionality as our motivation, we extended Jessica Hampton's "PROC SURVEYCORR" approach to create a %SURVEYCORRCOV macro to include features of the CORR procedure. For example, rather than a vector of correlations, %SURVEYCORRCOV provides a matrix of correlations and their p-values, for both the observed values and the within-domain ranks. In addition, %SURVEYCORRCOV generates standard deviations, which can be used to create covariance matrices |
correlation, proc step, regression analysis, predictor variable, proc reg, proc surveyreg, covariance, proc princomp, sample size, sample, proc cancorr, factor analysis, proc calis, design effect, multiple regression, proc corr, response variable, multivariate analysis, ridge regression, proc varclus, standard deviation, macro call, macro definition, random sampling, feature selection | source | 20 |
| 2 | 20 in 20: Quick Tips for SAS Enterprise Guide Users | Kelly Gray | There are many time-saving and headache-saving tips and tricks you can use to make working in SAS Enterprise Guide a breeze. Did you know that you can create a process flow from a program with the click of a button? You will learn 20 tips and tricks for working in SAS Enterprise Guide in 20 minutes - tips for both SAS Enterprise Guide 7.x and 8.x. One tip per minute, and out of the twenty, you are guaranteed to find at least one nugget that will make your life easier | sas enterprise guide, macro variable | source | 15 |
| 3 | A Beginner's Guide to Using ARRAYs and DO Loops | Jennifer L. Waller | If you are copying and pasting code over and over to perform the same operation on multiple variables in a SAS DATA STEP you need to learn about arrays and DO loops. Arrays and DO loops are efficient and powerful data manipulation tools that you should have in your programmer's tool box. Arrays list the variables that you want to perform the same operation on and can be specified with or without the number of elements/variables in the array. DO loops are used to specify the operation across the elements in the array. This workshop will show you how to create an array and utilize DO loops to perform operations on the elements in an array, create new variables, and change a short wide to a long and skinny data structure |
do loop, do over, end, array, time variable | source | 12 |
| 4 | A Beginners Guide to Consuming RESTful Web Services in SAS | Laurent de Walick | Web services are a method to exchange data between applications or systems using web technology like HTTP and machine-readable file formats like XML and JSON. Representational State Transfer (REST) is the most popular architecture used to implement web services. Web services using the REST architecture are called RESTful web services. In recent years SAS has included procedures and LIBNAME engines for all standards to support consuming RESTful web services. This paper presents how web services can be consumed in SAS. It will explore the PROC HTTP and discuss the different options that must be set correctly to consume a web service. It shows how parameters can be generated from existing SAS data using PROC STREAM and can be submitted when calling a web service. And finally, it describes how the output from a web service can be read into SAS using the JSON and XML LIBNAME engine |
web service, libname engine, proc http, proc step, filename statement, rest api | source | 12 |
| 5 | A Close Look at How DOSUBL Handles Macro Variable Scope | Quentin McMullen | The macro variable scoping rules of the SAS macro language are complex, and well- documented. The DOSUBL function, introduced in SAS 9.3M2, adds an additional layer of complexity to these scoping rules, as the macro programmer needs to understand how code executing in the DOSUBL side-session will create or update macro variables, and what impact this will have on macro variables stored in the main session symbol tables. Unfortunately, the current SAS documentation does not provide a clear definition of the DOSUBL scoping rules. This paper presents a series of test cases designed to illustrate DOSUBL's handling of macro variable scopes, and infers a set of DOSUBL macro variable scoping rules. The intended audience is experienced macro programmers interested in learning how DOSUBL manages macro variable scopes | macro variable, dosubl | source video |
19 |
| 6 | A Data-driven Approach to Understand Mild Cognitive Impairment from Imbalanced Data Using SAS | Liyuan Liu, Meng Han, Yiyun Zhou, Gita Taasoobshirazi |
Presentation: Cognitive decline has emerged as a significant threat to both public health and personal welfare, and mild cognitive decline/impairment (MCI) can further develop into Dementia/Alzheimer's disease. While treatment of Alzheimer's disease can be expensive and ineffective sometimes, the prevention of MCI by identifying modifiable risk factors is a complementary and effective strategy. Using a data-driven approach to understand the MCI factors become a crucial research question recently. However, there is a main problem that most healthcare datasets are imbalanced. Therefore, we employed multiple strategies to deal with imbalanced data, such as random oversampling, random under-sampling, SMOTE, SMOTEENN, etc. After that, to examine the effects of comparing multiple strategies and different machine learning algorithms, we use three machine learning algorithms: decision tree (DT), neural networks(NN), and Gradient Boosting (GB) | machine learning, proc hpsplit, gradient boosting | source | 5 |
| 7 | A Doctor's Dilemma: How Propensity Scores Can Help Control for Selection Bias in Medical Education | Deanna Naomi Schreiber-Gregory | An important strength of observational studies is the ability to estimate a key behavior's or treatment's effect on a specific health outcome. This is a crucial strength as most health outcomes research studies ar e unable to use experimental designs due to ethical and other constraints. With this in mind, one drawback of observational studies (that experimental studies naturally control for) is that they lack the ability to randomize their participants into treatment groups. This can result in the unwanted inclusion of a selection bias. One way to adjust for a selection bias is by using a propensity score analysis. In this paper, we explore an example of how to use these types of analyses. In order to demonstrate th is technique, we seek to explore whether clerkship order has an effect on National Board of Medical Examiners (NBME) and United States Medical Licensing Examination (USMLE) exam scores for 3rd year military medical students | propensity score, sample, regression analysis, logistic distribution, sample size, logistic regression, causal inference, treatment effect, proc step, potential outcomes, predictor variable, randomization, proc freq, likelihood function, response variable, histogram | source | 13 |
| 8 | A Hands-on Introduction to SAS Hash Programming Techniques | Kirk Paul Lafler | SAS users are always interested in learning techniques that will help them improve the performance of table lookup, search, and sort operations. SAS software supports a DATA STEP programming technique known as a hash object to associate a key with one or more values. This presentation introduces what a hash object is, how it works, the syntax required, and simple applications of it use. Essential programming techniques will be illustrated to sort data and search memory-resident data using a simple key to find a single value |
hash table, proc step, proc transpose, lookup, proc sql, sas log, composite key, dataset, proc sort, sas base | source | 13 |
| 9 | A Level Headed Approach to the METHOD=FASTQUAD Option in the GLIMMIX Procedure | Sidney J. Hann | The Gaussian quadrature algorithm approximates the likelihood of a generalized linear model with multiple nested levels using the Gauss-Hermite quadrature method of integration. While the approximations are close to exact, the calculation of these approximations in SAS are computationally demanding both in memory and time. To address these intense requirements, the SAS/STAT 14.1 update added the multilevel adaptive Gaussian quadrature algorithm of Pinheiro and Chao (2006) to PROC GLIMMIX using the METHOD = FASTQUAD option. Pinheiro and Chao's algorithm reduces the number of integrations required while SAS processes the estimation of the conditional log-likelihoods required for this modelling technique. The reduction of integrations reduces computation memory and time. Using a public dataset, this paper will examine the process of creating a multilevel model using adaptive Gaussian quadrature while comparing the run times of single level and multilevel adaptive Gaussian quadrature |
likelihood function, proc glimmix, proc mixed, sample, error message | source | 11 |
| 10 | A Program to Compare Two SAS Format Catalogs | Michael A. Raithel | SAS programming professionals are sometimes faced with the task of determining the differences between two SAS format catalogs. Perhaps they received an updated format catalog from a collaborating organization; or maybe a colleague updated a format catalog to reflect changes in the underlying data. Either way; how can programmers tell which catalog entries and value/label pairs have been modified? If the two catalogs being compared are relatively small, then the tried-and-true method of outputting each of them via the FMTLIB option of PROC CATALOG and then manually comparing the listings may suffice. But, this method is laborious and error-prone when there are a large number of formats and format value/label pairs. This paper presents a SAS program that compares two SAS format catalogs and reports the differences between them. It identifies mismatches in the format name, start value, end value, and label between the two catalogs being compared |
format statement, catalog entry, catalog, dataset, proc format, sas base, flag variable, proc step | source | 9 |
| 11 | A program to keep track of the number of records in a project | Gurpreet Pabla | Presentation: This paper introduces a macro (%case_control_check) that tracks the number of records/cases/controls in a dataset and how those change over the course of a project. What are the biggest issues programmer faces when writing scripts? Are there any dropped or added records, or are there any duplicates, especially after linking multiple datasets. The SAS log is available, but does not provide all the information. A programmer has to write additional code after each data or PROC STEP. This will be a cumbersome task for bigger projects. The purpose of this macro is to report on these issues with minimal amount of extra work. This macro was developed for epidemiological (case-control) studies but has been applied elsewhere. This paper explains macro parameters followed by an example to better understand the functionality of this macro. |
sas log, macro parameter, proc step | source | 8 |
| 12 | A SAS Macro for Calibration of Survey Weights | Anthony B. An | In survey sampling, calibration is commonly used for adjusting weights to ensure that estimates for covariates in the sample match known auxiliary information, such as marginal totals from census data. Calibration can also be used to adjust for unit nonresponse. This paper discusses a macro for calibration that was developed using SAS/STAT 15.1 and SAS/IML software. The macro enables you to input the design information, the controls for the auxiliary variables, and your preferred calibration method, including either a linear or exponential method. Because an unbounded calibration method can result in extreme calibration weights, this macro also supports bounded versions of both linear and exponential calibration methods. The macro creates calibration replication weights according to the sample design and the specified calibration method. Examples are given to illustrate how to use the macro |
bootstrap, sample, proc step, sample weight | source | 12 |
| 13 | A Survey of Methods in Variable Selection and Penalized Regression | Yingwei Wang | Statistical learning often deals with the problem of finding a best predictive model from a set of possible models on the basis of the observed data. "Best" often means most parsimonious; thus a sparse model that is composed of a subset of variables is usually preferable to a full model that uses all input variables because of its better interpretability and higher prediction accuracy. To this extent, systematic approaches such as variable selection methods for choosing good interpretable and predictive models have been developed. This paper reviews variable selection methods in linear regression, grouped into two categories: sequential methods, such as forward selection, backward elimination, and stepwise regression; and penalized methods, also called shrinkage or regularization methods, including the LASSO, elastic net, and so on. In addition to covering mathematical properties of the methods, the paper presents practical examples using SAS/STAT software and SAS Viya |
elastic net, regression analysis, sas viya, feature selection, data cleaning, backward selection, forward selection, correlation, proc reg | source | 17 |
| 14 | A Warning about Wald Tests | David A. Dickey | In mixed models, tests for variance components can be of interest. In some instances, several tests are available. For example, in standard balanced experiments like blocked designs, split plots and other nested designs, and random effect factorials, an F test for variance components is available along with the Wald test, Wald being a test based on large sample theory. In some cases, only the Wald test is available, so it is the default output when the COVTEST option is invoked in analyzing mixed models. However, one must be very careful in using this test. Because the Wald test is a large-sample test, it is important to know exactly what is meant by large sample. Does a field trial with 4 blocks and 80 entries (genetic crosses) in each block satisfy the "large sample" criterion? The answer is no, because, for testing the random block effects, it is the number of blocks (4) that needs to be large, not the overall sample size (320) | sample, f-test, restricted maximum likelihood, anova, degrees of freedom, likelihood function, proc mixed, fixed effects, random sampling, standard deviation, z-test, numeric functions, sample size, missing value, null hypothesis, proc glm, treatment effect, proc genmod, proc calis, covariance, residual, estimate, proc step | source | 20 |
| 15 | Accelerate DATA Step BY-Group Processing in SAS Viya | Jason Secosky, David Bultman |
BY-group processing is a method of processing observations from one or more data sets so that the observations are grouped by common variable values. SAS Cloud Analytic Services (CAS) on SAS Viya enables you to code your DATA steps such that BY groups are operated on in parallel. Parallel processing can improve performance. In this presentation, learn how to combine tables in CAS with the DATA STEP SET AND MERGE statements. Also, learn how to use FIRST. and LAST. variables in the context of CAS to perform operations at the start or end of a BY group. We also cover issues you might run into as you convert your SAS9 DATA steps to run in CAS. Come see how to accelerate your DATA steps in CAS |
by group processing, merge statement, cpu time, set statement, parallel processing, proc sort, proc step, dataset, benchmarking | source | 14 |
| 16 | Accelerating SAS Using High-Performing File Systems on Amazon Web Services | Darryl Osborne | When running efficient, high-performing SAS applications in the cloud is critical to your business, deploying the right storage architecture could be the difference between success and failure. However, running a high-performing parallel, distributed file system is difficult and often very expensive to setup, run, and maintain. What if you could offload this to someone else, someone with expertise and with virtually unlimited resources? You can! This paper guides you through selecting Amazon FSx for Lustre when running SAS Grid on AWS. It also discusses the different types of cloud servers available on AWS and which are best suited to access these high-performing file systems. This in-depth analysis considers the needs of the different SAS tiers and gives recommendations based on compute, memory, and network performance configurations, plus tips and tricks to help you get the most out of your investment | sas grid, output delivery system, amazon s3, cloud storage | source | 16 |
| 17 | Accessing Medicare Data at the Centers for Medicare and Medicaid Services using SAS | Richard Andrews | CMS is certainly no stranger to complex data. With billions of Medicare records loaded each year, Fee-for-Service (FFS) databases like the National Claims History (NCH), Standard Analytical Files (SAFs), Integrated Data Repository (IDR), and Chronic Conditions Warehouse (CCW) are getting more involved every day. This paper will discuss some of the nuances among the various systems and will offer tips for accessing each of these environments using SAS Access to Teradata, Oracle, and th e SAS Grid; utilizing tools such as SAS Enterprise Guide, SAS Studio, and the IBM mainframe | sas studio, sas enterprise guide, dataset, star schema, business intelligence, data warehouse, proc sql, calendar year, sas grid, input statement, insurance claims, oracle, macro definition | source | 24 |
| 18 | Achieving Net-Zero: Forecasting Power Usage to Improve Sustainability on Clemson University's Campus | David L. White | Clemson Energy Visualization and Analytics Center (CEVAC) is helping lead Clemson University toward a more sustainable future by using analytics and modeling of campus utility data. Leveraging access to multiple data stores and web-based data streams, the CEVAC team is deploying visualization and analytic solutions at a building and campus level. CEVAC is working with an initial ten buildings across campus, using SAS Visual Analytics 9.4 (VA) reports for each building. The CEVAC student team developed data pipelines and databases for a diverse data environment that ingests data from campus facilities databases, daily and hourly email attachments, and third-party APIs. Metrics include power, temperature, water, indoor air quality, and occupancy, which are available in each VA building report. SAS LASR (LSR) Server tables are updated and are subsequently available in VA building reports. A python middleware solution pushes data in LSR tables at regular and irregular intervals | data pipeline, proc sql, deep learning, neural network | source | 10 |
| 19 | Achieving Optimal Performance with the SAS Data Connect Accelerator for Hadoop | David Ghazaleh | The SAS Data Connect Accelerator for Hadoop uses the SAS Embedded Process to improve performance when moving data between Apache Hadoop sources and SAS Cloud Analytic Services. Achieving optimal performance during data movement can be challenging. Some of the variables to consider include cluster size, number of cores available, size of the data, and number of splits in a file. This paper explains how to optimize the SAS Embedded Process and how to take advantage of the new Apache Spark Continuous Processing mode available in the SAS Embedded Process | hive, cas, proc step, parallel processing, sas viya, spark, cas action, resource allocation, proc sql | source | 17 |
| 20 | Add SGSCATTER, SGPLOT, SGPANEL and SGRENDER Procedures to Your SAS Toolbelt | Alex Chaplin | SGSCATTER, SGPLOT, SGPANEL and SGRENDER procedures provide a range of graphical data visualization options to help any data analyst tell the story of the data. If you are a SAS Enterprise Guide user be aware that SAS Enterprise Guide generates graphs using the GPLOT procedure which is the predecessor to PROC SGPLOT. To really bring your data alive you will need to be familiar with the expanded capabilities in PROC SGPLOT and introduce yourself to PROC SGPANEL |
proc step, proc reg, regression analysis, proc sgplot, scatter plot, macro variable, simple linear regression, proc sgpanel, proc freq, where statement, confidence interval, frequency analysis, predictor variable, null hypothesis, proc sgscatter, line chart, sas enterprise guide, sample, proc sql, histogram | source | 16 |
| 21 | Adopting SAS Viya into Your Business: Data Success Tales from a Central Bank's Perspective | Ben Zenick | Data maturity is an essential element of a successful business. To support business growth and capacity, companies, like Eastern Caribbean Central Bank (ECCB) seek to adopt high performance, user-friendly data and analytical solutions as they outgrow tools such as Microsoft EXCEL. This paper shows how ECCB matured from a legacy system primarily using Microsoft EXCEL for analytics and reporting purposes to a customized, self-service, robust system powered by SAS Viya. SAS Viya's ability to connect Python - an open-source interface - and REST APIs to the Cloud Analytics Services (CAS), along with web-based analytics and reporting capabilities, was an optimal analytical solution that supported ECCB's vision for growth and complex business process. In this paper, we will walk you through the business issues that inspired the change, the specific techniques we used to solve the data issues, and how they integrated the solution into their processes |
sas viya, excel, data governance, proc freq, data cleaning, rest api | source | 20 |
| 22 | Advertising Effectiveness - Do Super Bowl Ads Impact Stock Prices? | Jaya Bhatia | Presentation: The Super Bowl has been a great platform for companies to market their products or services through 30-60 second commercials. These commercials are not only famous for their entertainment factor, but also their high costs. A significant question raised is whether these multimillion-dollar investments substantially impact the growth of a company? Since, the focus of these ads is both increase in sales and brand awareness, the return of investment of these ads can be inferred using its impact on the stock price of companies. The goal of this research is to understand the impact of various factors related to advertisements on the stock price of the companies soon after the event and one week later. | source | 3 | |
| 23 | An Astounding Lack of Data | Christopher Hooks | In today's world, where there are quintillions of bytes of information generated every day, we still suffer from a lack of data. Data is driving the decision-making in business, but what about governments? Why are cities behind when it comes to data-driven decision-making? This paper looks at the availability of city-level data and how it can be used in decision- making at the local level. The topic is further extended with a discussion of existing barriers to using data for informed decision-making in local governments | credit card, balance | source | 11 |
| 24 | An Efficient Way to Create Descriptive Tables with Pairwise Comparison | Ang Gao, Chul W. Ahn |
This paper illustrates a SAS macro for descriptive tables, which provides Chi-square and Fisher Exact tests for categorical variables, and parametric and nonparametric statistical tests for continuous variables. A formatted output table includes Mean ± Standard deviation, Median (25th percentile, 75th percentile), non-missing N, count (%), and statistical test for each p-value. It also provides pairwise p-values if the comparison is among more than two groups. A permutation method has been used in the macro to allow huge amount of variables to be processed automatically. This approach is designed to create and update descriptive tables in an efficient way | macro variable, proc step, nominal variable, interval variable, class variable, anova, sample, proc freq, proc ttest | source | 10 |
| 25 | An Insider's Guide to SAS/ACCESS Interface to Snowflake | Jeffrey Bailey | Snowflake is an exciting, new data warehouse built for the cloud. SAS/ACCESS Interface to Snowflake allows SAS to take advantage of this exciting technology. This paper describes Snowflake and details how it differs from other databases that you might have used in the past. Using examples, we discuss the following topics: the differences between using SAS/ACCESS Interface to Snowflake and SAS/ACCESS Interface to ODBC how to configure your SAS environment for SAS/ACCESS Interface to Snowflake tricks that you can use to discover what the SAS/ACCESS product is doing how to effectively move data into Snowflake using SAS/ACCESS Interface to Snowflake * performance tuning your SAS and Snowflake environment The paper uses an example-driven approach to explore these topics. Using the examples provided, you can apply what you learn from this paper to your environment |
proc sql, libname statement, dataset, cloud storage, error message, proc step | source | 17 |
| 26 | An Introduction to Multiple Time Series Analysis and the VARMAX Procedure | Xilong Chen | To understand the past, update the present, and forecast the future of a time series, you must often use information from other time series. This is why simultaneously modeling multiple time series plays a critical role in many fields. This paper shows how easy it is to use the VARMAX procedure to estimate and interpret several popular and powerful multivariate time series models, including the vector autoregressive (VAR) model, the vector error correction model (VECM), and the multivariate GARCH model. Simple examples illustrate Granger causality tests for identifying predictive causality, impulse response analysis for finding the effect of shocks, cointegration and its importance in forecasting, model selection for dealing with the trade-off between bias and variance, and volatility forecasting for risk management and portfolio optimization |
time series analysis, sample, vecm, feature selection, proc varmax | source | 19 |
| 27 | Analyzing Non-normal Data: Application to Missing Data Problems | Niloofar Ramezani | In many applications, the response variable is neither continuous nor normally distributed. If the outcome variable is binary, count, multinomial, or ordinal, the relationship between predictors and response variable is non-linear and using ordinary linear models developed for continuous data is inappropriate. Therefore, more advanced models adopting classification algorithms should be applied. Binomial, multinomial, and ordinal logistic models, as well as Poisson regression, for low-dimensional data and classification random forest for high-dimensional data are among robust predictive methods discussed for such scenarios. Starting with the simplest case of binary outcomes, through count, multinomial, and ordinal response variables, this study discusses various modeling options for low- and high-dimensional data while handling non-normal responses and missing data issues in | logistic distribution, proc step, logistic regression, regression analysis, response variable, proc hpforest, imputation, missing value, proc logistic, proc genmod, ordinal logistic regression, multiple imputation, poisson regression, poisson distribution, mean imputation, binomial distribution, multinomial logistic regression, proc mi, proc hpsplit, proc mianalyze, likelihood function, nominal variable, maximum likelihood estimation, sample, proc standard, predictor variable, proc princomp | source | 10 |
| 28 | Analyzing the Factors Impacting Suicidal Behavior in American Youth | Ashlesha Sampathi | Suicide is the 2nd leading cause of death in youth aged 12-19. Estimates from the data indicate that nearly 7.4% of the American youth attempted suicide in 2017 alone, while 17.2% of teens considered attempting suicide. The last decade has seen an increase in the number of suicide attempts in teenagers. Given the seriousness of this issue, it is important to identify the factors that are leading to this increase in suicide attempts. This paper closely examines the 2017 Youth Risk Behavior Survey (YRBS), in order to understand characteristics of teenagers who attempted suicide. Latent Class Analysis using SAS Enterprise Guide found that among teens who attempted suicide, three distinct classes were evident which were characterized mainly by sexual assault, bullying, and depression respectively | logistic regression, predictor variable, interaction effect, logistic distribution, regression analysis, missing value, sample, response variable, trend analysis, proc step | source | 7 |
| 29 | Any Time Zone to Any Time Zone: A Macro to Convert Anything | Joe DeShon | This paper describes a flexible and fully configurable macro that converts a DATETIME stamp from any time zone to any other time zone. The macro can be configured to operate with any and all of the more than 24 time zones in the world |
datepart, sas base | source | 6 |
| 30 | Application of Google Distance Matrix API to investigate healthcare access: a study on Hokkaido, Japan | Anna Tsutsui, Yuko Ohno |
Recently, a web service called web Application Programming Interface (API) has attracted the attention of academic researchers for evaluating healthcare access by estimating travel times and distances. Particularly, Google Maps API is now being adopted as a new and simple tool for spatial analyses. Google Maps API requires information on the search criteria but provides the calculation results based on their own data and calculation algorithms. However, most researchers have used R or Python for this purpose. In this paper, we introduce the key steps for conducting similar studies using SAS by providing an overview of Google Maps API (especially Google Distance Matrix API), Uniform Resource Identifier (URI), JavaScript Object Notation (JSON) format, and JSON map file. An analytical example is also provided to investigate healthcare access for children with cancer in Hokkaido, Japan | web service, libname statement, dataset, filename statement, macro variable, sas studio, proc step | source | 14 |
| 31 | Applications of DOW Loop: Extension to PROC REPORT | Neha Yadav, Pranathi Salla |
A double DOW-loop in conjunction with PROC REPORT can be used to present order variable information across pages when vertical space is needed to separate blocks of related rows. When creating reports in PDF ODS destination, SPANROWS option is used to repeat the values of GROUP or ORDER variable across the pages. However, sometimes the values are not repeated as expected, especially when a BREAK or LINE statement is used. The proposed solution uses SPANROWS and a compute block to change the height of the last row in a block of records. Preprocessing is needed since there is no LAST dot (LAST.variable) option in PROC REPORT. A double DOW-loop can be used for this processing. A double DOW-loop consists of two DO-UNITL statements. The first DO-UNTIL calculates the number of records per each break group (LAST.BREAK) and second DO-UNITL attaches the calculated number of records to each break group. When used with a compute block, the vertical spacing can be adjusted. We will present an example that demonstrates the problem and uses the proposed solution using Base SAS 9.4 in a windows environment. It is appropriate for programmers in any industry with a basic understanding of PROC REPORT. The discussion focus on the use of PDF, though limitations of RTF will be presented. |
proc report, proc step, adverse events, conditional do loop, shift table, laboratory shift table, binary variable | source | 11 |
| 32 | Apply the Key Ideas from Andrew Ng's Machine Learning Certification (Coursera) in SAS Viya | Petri Roine | Machine Learning Certification by Stanford University (Coursera) is the single highest rated course on machine learning on the entire internet. Created by artificial intelligence (AI) guru Andrew Ng, co-founder of Coursera and Professor at Stanford University, the program has been taken by more than two million persons globally, who have given it an average rating of a whopping 4.9 out of 5. The course covers how to apply the most advanced machine learning algorithms to common problems. This breakout session takes all the key ideas from Andrew Ng and explains step-by-step how to use those valuable nuggets in SAS Visual Data Mining and Machine Learning. You will become better at debugging and figuring out how to improve algorithm's performance. We go through supervised and unsupervised learning models and pinpoint what to consider when trying to optimize the performance of the model. You do not have to have taken the actual course-the key ideas are explained at an appropriate level | machine learning, dimensionality reduction, feature extraction, proc hpsplit, training set, neural network, feature selection, proc princomp, regression analysis, data science, feature engineering, confusion matrix, data cleaning | source | 9 |
| 33 | Architecting SAS Analytics Edge-to-Cloud Solutions on Hewlett Packard Enterprise Infrastructure | Mark Barnum, Kannan Mani |
We live in a world where everything computes. Where technology, apps, and data are driving digital transformation, reshaping markets, and disrupting every industry. IT needs to reach beyond the traditional data center and the public cloud to form and manage a hybrid connected system stretching from the edge to the cloud. Listen to experts on how Hewlett Packard Enterprise (HPE) and SAS help organizations to rethink and modernize their infrastructure more comprehensively to deploy their edge-to-cloud architecture and analyze data at the edge quickly. Join us, in this session for a deep dive and demo on: HPE Elastic Platform for Analytics (EPA) architecture for SAS and use cases SAS Event Stream Processing (ESP) and SAS Visual Analytics (VA) deployment at edge Cloudera Data Platform and NiFi | stream processing, real-time processing, hive, sas viya, machine learning, network utilization, data pipeline, deep learning, proc freq, proc step, edge computing | source | 24 |
| 34 | Automate Patient Safety Survey PDF Report Production | Lan Luong | The Agency for Healthcare Research and Quality (AHRQ) Surveys on Patient Safety Culture (SOPS) assess staff's views about their organizational culture for patient safety every two years. The results of these surveys are used by BJC HealthCare leadership to determine areas of improvement and monitor the organization's safety culture. At BJC, the survey results are summarized for 15+ provider organizations, at many human resources (HR) hierarchical levels, and distributed to leadership throughout the system. Therefore, the reports require complex formatting, customization, and data validation. During 2016, about 500 PDF reports were developed using a manual process that was error prone and time consuming. In 2018, based on lessons learned from 2016, our team created SAS Enterprise Guide programs to automatically produce 855 PDF reports. This paper presents techniques used for the report production process including macros, ODS PDF, ODS LAYOUT, PROC REPORT, DO loop, and more |
proc step, proc report, data cleaning, macro variable, do loop, data step, data governance, sas enterprise guide, title statement, %if-%then-%else, macro loop, proc sql, ods pdf, macro function | source | 14 |
| 35 | Automatically Loading CAS Tables from SAS Data Integration Studio Using SAS Viya REST APIs | Oscar Simionati, Ervin Wilneder |
Your SAS Viya installation may have many SAS Visual Analytics reports in several folders that also need many source data tables. Assuring that the information for each of those reports is loaded at the moment the users want to use them is not an easy task. We developed a comprehensive SAS Data Integration Studio process that automatically finds the data source needed for each SAS Visual Analytics report, extracts the tables from the database management system (DBMS), loads them as SAS Cloud Analytic Services (CAS) tables, and checks that everything is correct for the reports' execution. This is possible thanks to the use of the SAS Viya REST API, integrating http requests that refer to the folders on which we want to automate the reports and the relationships between these and their CAS tables, along with the corresponding authorization to access the data | rest api, raw data | source | 9 |
| 36 | Automating Your Multi-Environment Hotfix Management | Pierre Dupuis | Managing SAS hot fixes on a SAS 9.4 multi servers environment can be a bit of a challenge for SAS administrators. It becomes even more of a struggle when checking for available hot fixes on multiple SAS environments with different SAS versions or even worst if you don't have access to SAS web site from your SAS servers. The SAS Hot Fix Analysis, Download and Deployment Tool (SASHFADD) creates a customized report that lists available hot fixes for the installed SAS products. It also generates scripts that automate hot fixes downloads. A script is generated to install SAS hot fixes then SAS admins use SAS Deployment Manager to install SAS hot fixes. The goal of this paper is to provide helpful information on how to use SASHFADD to automate the SAS 9.4 hot fixes download and deployment process when you are dealing with multiple SAS environments. It also provides a workaround solution for SAS environments that can't connect to SAS update servers | sas administrator, sas output | source | 8 |
| 37 | Automation in SAS Visual Data Mining and Machine Learning | Wendy Czika | Automated machine learning can help every data scientist, from the novice to the most experienced practitioner. This paper demonstrates the different levels of automation available in the Model Studio environment of SAS Visual Data Mining and Machine Learning software. You can choose to have features automatically constructed or to automate the process of algorithm selection and hyperparameter tuning by using dedicated Model Studio nodes in the pipeline that represents your machine learning process. You can build on or edit a pipeline that includes these nodes, inserting your domain expertise into the process. Alternatively, you can ask the software to automatically build an entire pipeline that includes various feature engineering steps and predictive models, optimized for your specific data according to the assessment criterion of your choice. The included models are determined using hyperparameter tuning across multiple modeling algorithms | machine learning, feature engineering, proc hpsplit, regression analysis, feature selection, imputation, hyperparameter tuning, rest api, target encoding, supervised learning, correlation, outlier, champion model, data science, response variable, cv, logistic distribution, data wrangling, neural network, pearson correlation, credit card, interval variable, ks statistic, median imputation, nominal variable, univariate analysis, data mining, proc princomp | source | 26 |
| 38 | Bayesian Sequential Monitoring of Clinical Trials Using SAS | Matthew A. Psioda | In this paper, we provide an overview of Bayesian sequential monitoring for clinical trials. In such trials, patients are continually enrolled and their data are analyzed as often as is desired or feasible until a hypothesis has been proven or disproven, or until the allocated resources for the trial have been exhausted (that is, the maximum sample size or study duration has been reached). Such an approach is particularly appealing in the case of difficult-to-enroll populations such as pediatric populations or for rare disease populations. A Bayesian sequentially monitored trial does not require a pre-specified sample size or number of analyses. For proving efficacy in a sequentially monitored trial, the Bayesian collects data until the evidence in favor of the investigational treatment is substantial from the perspective of an a priori skeptical judge who doubts the possibility of large treatment effects | sample size, sample, posterior probability, treatment effect, null hypothesis, interim analysis, significance level, type i error, binomial distribution, proc iml | source | 13 |
| 39 | Behind the Front Door: Authentication Options with SAS Viya | Mike Roda | If you are tasked with deploying and administering SAS Viya, one of your top concerns is how users will be authenticated. Will you use an LDAP directory, or is there a requirement to implement single sign-on with your company's existing security infrastructure? Learn what the authentication options are in SAS Viya 3.5 and what information you need to obtain from your IT security department to configure them. Learn how to limit concurrent logins and see examples of how to customize the login page. If you are administering an existing deployment, find out what new authentication options have been added since SAS Viya 3.4 | sas viya, sas administrator, service provider, cas | source | 10 |
| 40 | Best Practices for Converting SAS Code to Leverage CAS | Steven Sober, Brian Kinnebrew |
This is an introductory paper to help you understand how to convert SAS code to leverage the distributed computing engine in SAS Cloud Analytic Services (CAS). As customers adopt SAS Viya, they have access to many new visual interfaces, procedures, and CAS action sets that execute code in CAS. But what about existing SAS routines? How does one convert these routines to leverage CAS? In this session, we will cover terminology, the sweet spot in CAS, the biggest SAS9 gotchas in the conversion of SAS code to SAS Viya and CAS, best practices in doing the conversion, workarounds, case studies, coding examples, and reading material. Additionally, you will learn how to engage with SAS to run your SAS code through the SAS Viya readiness assessment utility, which produces reports to assist you in your conversion of SAS code to leverage CAS | sas viya, proc step, by group processing, sas log, proc sql, proc logistic, proc sort, logistic distribution, cas action, sas enterprise guide, monte carlo, sas base, proc freq | source | 11 |
| 41 | Best Practices for Effective Infographics in SAS Visual Analytics | Falko Schulz | SAS Visual Analytics enables you to build impressive infographics into your report. Learn how to effectively convey your message using the out-of-the box visualizations and layouts delivered with SAS Visual Analytics. In this paper, the author will analyze Earth's surface temperatures using daily and monthly NASA/GISS data for over 25,000 stations across the globe. We will learn how temperatures changed over the years and detect trends and hotspots. You will learn how to import and prepare data in SAS Viya, as well as discover the latest visualization capabilities. We will discuss best practices for designing and developing simple but effective data visualizations targeted for specific audiences | bar chart, sas viya, data visualization, create table, line chart, proc sql | source | 26 |
| 42 | Best Practices for Enabling SAS Analytics in the Cloud - at Scale | Heather Burnette | We know cloud is the future, but it is quickly becoming our present! An increasing number of companies, large and small, are turning to the cloud to deliver advanced analytic solutions that will scale. In fact, the cloud can be the perfect environment for evolving your SAS analytics to solve real-world problems quickly, easily, and affordably. But to do that, you need to understand how to make it work for your business. Whether you're an administrator, business user, architect, or engineer, this presentation has something for you. We discuss everything from broad best practices to specific technical tips and tricks to show how to optimize your cloud solution. We illustrate how companies are using SAS9, SAS Viya and Teradata Vantage in public and private clouds to achieve the following: reduce and avoid cost; better manage and prepare data; increase operational efficiency; leverage cloud marketplaces to streamline setup and configuration; and quickly scale SAS solutions to production | cloud storage, proc step, proc sql, teradata access, sas viya, sas log, proof of concept, libname statement, azure blob storage | source | 20 |
| 43 | Best Practices for Scheduling in SAS Viya | Ursula H. Polo | To help you harness the power of the SAS Platform, SAS Viya contains numerous scheduling capabilities. The SAS Viya documentation provides detailed instructions about how to schedule a job or flow. However, to give you a better understanding about which method might be best for you, this paper goes into depth about the five scheduling methods available in SAS Viya. This paper discusses the advantages, disadvantages and alternate approaches, and best practices for each scheduling procedure. This paper details the scheduling types available and helps you take full advantage of the SAS Viya environment | sas viya, sample, sas studio | source | 13 |
| 44 | Best Practices in SAS Programming to Support Ever-Increasing Data Loads. | John Schmitz | In most large organizations, SAS serves a pivotal role in data processing for warehousing, reporting, analytics and more. The SAS language provides multiple tools and options to streamline these data processing needs that may be unfamiliar to many developers. This paper will present some well-known and lesser-known SAS methods for efficient data handling. The focal point of the paper is more geared towards what is available and the use cases for each technique, rather than a detailed how-to on a specific solution | proc sql, proc step, hash table, where statement, implicit sql pass-through, user-defined format, by group processing, set statement, proc means, proc sort, lookup, sas programmer, libname statement, create table, data wrangling, dataset, sql pass-through, group by, sas viya, proc format, data merging | source | 15 |
| 45 | Better Student Application Reporting: A Slowly Changing Dimension and SAS Data Integration Studio | Lauren E. Schoenheit, Alexander K. Fantroy |
For a selective university, shaping the incoming freshman class requires current operational data on the student applications that have been received, the admissions decisions that have been made, and the students that have committed in response throughout the admissions cycle. A point-in-time comparison with historical data serves as a benchmark to better understand and anticipate the makeup of the incoming students. To meet these demands, we shifted student application reporting from a Base SAS program (run on an ad hoc basis about twice a week that created a data set of application information for that day, saved to a drive), to a scheduled SAS Data Integration Studio job that created a slowly changing dimension of year-over-year daily application information stored as an oracle table in the university's enterprise database | slowly changing dimension, sas data integration studio, natural key, staging area, unique key | source | 11 |
| 46 | Bits and Bytes - A Mix for High Volumes of Data | Karine Désilets | Building a system is like building a house - both relate to the construction realm and have a myriad of similarities. Handling high volumes of data is like building a tower - or a castle. Every step is critical in order to have a great building! Big towers take sound planning, big cranks and; specific foundations, materials and supplies. Some buildings are trendier than others; some are copied from others; however, we all are familiar of the main plans with so many buildings built in the world. This paper reviews the roadmap of high volumes of data processing - in a funny fashion! The first part shows how to tackle big data efficiently and presents techniques to reduce the amount of data processed; the second part explains how to reduce the amount of data stored on disks; and the final part demonstrates techniques to efficiently process large datasets. Throughout this article, we expose one principle: Keep it Simple Stupid (KISS) - no need to be complicated. À la mode towers can be down-to-earth! | unique key, proc sql, proc step | source | 19 |
| 47 | Bringing Computer Vision to the Edge: An Overview of Real-Time Image Analysis with SAS | Maggie Du | In the Internet of Things (IoT) era, when large amounts of streaming data are generated continuously, it is extremely impractical and inefficient to store all these data in a data center. Furthermore, the majority of these data are irrelevant; for example, only streaming data that contain anomaly events are worth storing or transmitting for further investigation. For real-time image or video processing, moving the analytics to edge devices not only saves a device-to-cloud data round trip but also improves data privacy and governance. This paper presents the real-time image analytics solutions offered in SAS software for image processing, image classification, object detection, and segmentation. It also describes the general workflow of real-time image analytics, from preprocessing images, to training deep learning models by using SAS Viya, to deploying an image analytics pipeline on edge devices by using SAS Event Stream Processing | stream processing, real-time processing, edge computing, neural network, deep learning, machine learning, sample, sas viya, histogram, data cleaning, training set | source | 15 |
| 48 | Bringing SURVEYMEANS Up to Speed: Improving This Procedure for Estimating Domain Level Ratios | Brian Simonson | Domain-level estimation plays an important role in the field of survey sampling statistics. The implementation of domain estimation for means, totals, and ratios that SAS has developed is available in the SURVEYMEANS procedure. Through The Lewin Group's work on statistical audits of Medicare and Medicaid claims data, two weaknesses of PROC SURVEYMEANS have been identified. First, there is no calibration weight adjustment using auxiliary variables beyond rudimentary post-stratification. Second, the algorithm for estimating domains iterates through one domain level at a time, which can be time and resource intensive. To overcome these limitations, The Lewin Group developed the Hybrid Ratio Estimator (HRE) for use in statistical audits. The HRE is a hybrid of two well-known survey estimators: the Separate Ratio Estimator (SRE) and the Combined Ratio Estimator (CRE) |
proc surveymeans, proc calis, proc step, sample | source | 6 |
| 49 | Build an HTML5 Web App Using SAS | Allan Bowe | With SAS you have the Power to Know, HTML5 provides the "Power to Show"! A workshop to kickstart the development of new web apps on SAS Platforms | sas 9, sas viya, macro definition | source | 9 |
| 50 | Building an Expert's Toolbox: Essential Tools for Generating the Perfect Microsoft Excel Worksheet | Chevell T. Parker | When you have a home building or renovation project to accomplish, you need expert tools for the job. The same is true when you want to build (create) or modify (renovate) Microsoft EXCEL worksheets. You need a variety of expert tools in your SAS software toolbox to accomplish these tasks. You have a choice of many tools that enable you to create and fully customize your worksheets. For example, you can use the SAS Output Delivery System (ODS) EXCEL destination and the SAS EXPORT procedure. But you can also complement the standard tools with more specialized ones (for example SAS macros and the Python open- source language) to further extend the capabilities of your worksheets. This paper explains how to use all of these tools to create fully functional Microsoft EXCEL worksheets. The discussion is divided into two main sections. The first section explains how to generate EXCEL worksheets and perform various tasks in SAS and SAS Viya |
excel, proc step, proc export, sas viya, proc report, cas, proc fcmp, bar chart, output delivery system, ods excel | source | 21 |
| 51 | Building an Internal Application with the SAS Stored Process Web Application | Mara Werner | Using the SAS Stored Process Web Application and SAS prompts you can take existing SAS code and modify it to accommodate different variations of your most common requests. You can use a custom HTML front end to provide an intuitive, modern-looking user interface, ODS PDF to present a summary of your results, ODS EXCEL for supporting data, and you can deliver it all by email. Your customers will love getting results on-demand and you'll love that they can fish for themselves, freeing you from repetitive queries and allowing you to focus on more interesting and productive work |
macro variable, excel, sas enterprise guide, ods pdf, xls, proc export, proc step, proc sql | source | 18 |
| 52 | Building Two Regression Lines Is Better Than Building One | G Liu | Presentation: In a time series, there can be a change in parameter coefficient or error variance at a time point k. Traditional simple regression modeling can limit the accuracy and predictability of this time series if this change is not accounted for. Utilizing PROC UCM in SAS/ETS, the structural break at k can be identified so that two separate regression lines can be modeled to better represent the data, and to provide us with better predictions for future time periods. |
time series analysis, regression analysis, proc ucm | source | 8 |
| 53 | Building Web Applications with SAS Has Never Been So Easy | Matthew J. Kastin, Nikola Markovic |
Boemska AppFactory helps you complete the "last mile of analytics" by empowering users to innovate with highly tailored productivity apps. When combined with the SAS Viya Platform you can create sophisticated apps from your data and increase access to analytics by enabling more of your organization to harness the power of SAS by deploying modern, intuitive, and relevant apps | sas programmer, sas viya | source | 16 |
| 54 | Calculating Exposure Hours for Contact Investigations | Cecine Nguyen | Presentation | source | 3 | |
| 55 | CAS Sessions: Understanding Connection Options to Ensure Data Access Security | Philip Hopkins | In SAS Viya, there are a variety of ways to connect to the SAS Cloud Analytic Services (CAS) analytics engine, or CAS server. The resulting CAS sessions might run under the CAS instance's service account or the end user's identity. This is further complicated by whether the CAS client is using the Kerberos authentication protocol, which results in differences, depending on whether CAS is running on Linux or Windows. The various SAS Viya applications default to certain connection methods, which drive their CAS session's process owner and, finally, the resulting physical security contexts. Understanding the default CAS session behavior for each application and how those defaults can be overridden and engineered to support data access policy is the focus of this paper | sas viya, sas studio, cas, data science | source | 19 |
| 56 | CASL, a Language Specifically Designed for Interacting with SAS VIYA | Jerry C. Pendergrass | CASL is a new language designed to run SAS Cloud Analytic Services (CAS) actions and process responses to generate a report. To make it easy for SAS users, the language syntax mimics the syntax of the DATA STEP. CASL is not just a language, but a programming environment that is embeddable into any program. CASL is available through the CAS procedure or as the action runCasl in the CASL Server action set. Learn how to use the CASL language to pipeline actions one after another to produce a report. You will learn how to use CASL to create your own result tables from action results. Allow me to introduce you to a very powerful language that is simple to use |
proc step, where statement, output delivery system, put statement, cas action | source | 10 |
| 57 | Causal Effect Estimands: Interpretation, Identification, and Computation | Clay Thompson | In modern statistics and data science, there is growing attention on estimating causal effects by using data from nonrandomized or imperfectly randomized studies. This task arises in applications such as post-approval analysis of medical treatments, evaluation of public policies, and assessment of marketing campaign efficacy. One challenge of these applications is the variety of causal effects that you can estimate. For example, you might need to determine whether to estimate the average treatment effect (ATE), the average treatment effect for the treated (ATT), a mediated effect, or other conditional effects. Identifying the causal effect most relevant to your application can have important implications for determining what approach to causal inference is most appropriate | proc step, treatment effect, potential outcomes, causal inference, response variable, mediation analysis | source | 20 |
| 58 | CECL/IFRS9 Implementation: What Is Driving My Quarter-Over-Quarter Change in Allowance? | Usha Srinivasan, Gaurav Singh, Danny Noal |
Ford Motor Credit Company collaborated with SAS Institute Inc. to develop a quantitative process to explain the drivers in variability of issue-to-issue change in Expected Credit Loss (ECL), based on the Current Expected Credit Loss (CECL) / International Financial Reporting Standards 9 (IFRS9) framework. The underlying methodology performs attribution of the ECL change at the contract level. Results from this framework are compared to other ECL attribution approaches. This paper provides an analysis of the findings and compares the benefits and downsides of each of the approaches investigated | source | 8 | |
| 59 | Child Maltreatment Report Outcomes: Do Reports from Mandated and Permissive Reporters Differ? | Maria Gandarilla Ocampo, Brett Drake, Melissa Jonson-Reid |
Presentation | source | 3 | |
| 60 | Choose Your Own Adventure: Manage Model Development Via a Python Integrated Development Environment | Jon Walker | Data scientists often need to work with multiple languages and in multiple analytic environments to solve a problem. SAS provides a complete end-to-end environment, but it has traditionally been accessible to users only through GUIs and SAS languages. This paper introduces a new tool enabling data scientists to manage components of the analytics life cycle from within any Python environment. We first demonstrate how to register a model developed with Python using SAS Model Manager, before exploring methods for managing, deploying, and tracking the model. In addition, we show how to accomplish supporting tasks such as rendering visualizations and extending the existing functionality | sas model manager, sas viya, data science | source | 17 |
| 61 | Classification of regional characteristics using population composition data and POS data | Kazuma Bannai | Presentation | source | 7 | |
| 62 | Cloud, On-Premises, and DevOps: Implementing SAS Customer Intelligence 360 in a Banking Environment | Andrea Defronzo, Roel Van Assche |
SAS Customer Intelligence 360 is a SaaS platform that integrates with your on-premises customer data and marketing processes. This hybrid setup might raise a few questions when applied in a corporate environment, especially in a tightly regulated one such as a bank: How do we authenticate users? How do we make the system secure? * How do cloud and on-premises components communicate? Which data is persisted in the cloud? On the other hand, keeping your data in-house while benefiting from the advantages that cloud-based software offers, like continuous development of new features, elastic scalability and reduced maintenance, is wonderful. This paper answers those questions with a real-world example: the implementation of SAS 360 Engage: Direct at ING BE and ING DBNL | data cleaning, sas enterprise guide, macro variable | source | 17 |
| 63 | Coding in SAS Viya | Charu Shankar | This hands-on workshop is for users who want to take advantage of the boost in processing speed for Base SAS programs executing in SAS Viya. This paper covers using the power of SAS Cloud Analytic Services (CAS) to access, manage, and manipulate in-memory tables. The purpose of this paper is to support users in their ability to get started with coding in SAS Viya as they transition from coding in Base SAS programs. Users learn to perform three simple yet important tasks to get comfortable with the language of SAS Viya: connect to the CAS LIBNAME engine for data transfer between SAS and CAS; load data to a caslib and process data in CAS; and modify SAS programs to run in SAS Viya |
sas viya, sas base, sas studio, proc step, by group processing, dataset, sas library, raw data | source | 18 |
| 64 | Combination Weighted Log-rank Tests for Survival Analysis with Non-proportional Hazards | Andrea Knezevic, Sujata Patil |
The statistical methods most commonly used to test the equality of survival curves in time-to-event analysis rely on the assumption of proportional hazards. In oncology drug development, non-proportional hazards between investigational treatments are often observed but statistical methods that properly account for these situations are rarely used in practice. The use of combinations of Fleming-Harrington weighted log-rank statistics is one relatively straightforward way to perform hypothesis testing in the presence of non - proportional hazards. In this approach, the maximum test statistic of several weighted log- rank statistics (Zmax) is calculated from Z-statistics obtained using the G(ρ,γ) family. A combination test can simultaneously detect equally weighted, early, late or middle departures from the null hypothesis and can thus robustly handle several non-proportional hazard types with no a priori knowledge of the hazard functions. Although the LIFETEST procedure allows for testing with Fleming-Harrington weighted log-rank statistics, there is no built-in functionality in SAS to test combinations of weighted tests. We discuss the development of a SAS macro that implements combination testing, including estimation of the variance-covariance matrix of the joint distribution of the Z-statistics and calculation of the p-value for Zmax |
rank test, log-rank test, hazard function, sample, clinical trial, covariance, proc phreg, hazard ratio, null hypothesis, sample size, regression analysis, survival analysis | source | 10 |
| 65 | Common Points of Compromise Detection Using Network Analysis | Prathaban Mookiah | Common points of compromise (POC) are entities such as merchants and websites that suffer from a security breach that results in the compromise of a multitude of CARDS, online credentials, and so on. The form of such breaches can range from sophisticated security attacks on large merchants that are well publicized to an opportunistic staff member harvesting details on a regular basis. Such forms of fraud continue to thrive, and therefore POC detection continues to be a key tool in combating various forms of banking fraud such as card fraud and online banking fraud. In this paper, we introduce a process that is designed to identify POCs by combining techniques from network analysis and machine learning, engineered completely within the SAS ecosystem |
machine learning, card fraud | source | 7 |
| 66 | Common Tasks Done with CASL | Jane Eslinger | Some tasks are so common they are used in almost every program; tasks such as loading data tables and then either deleting or saving those tables. Other tasks include looping through the data, summarizing the data, and sorting the data. As a SAS programmer, you know the code that you need to accomplish these common tasks. CASL, the language created to communicate with the SAS Cloud Analytic Services (CAS) server, might be new to you. This paper demonstrates how to perform those common tasks with CASL. Understanding how to perform common tasks and having the code to do them empowers you to develop CASL programs and take full advantage of the CAS server | proc step, cas, sas 9, cas action, sas viya, excel, dataset | source | 15 |
| 67 | Computer-Aided Diagnosis System for Breast Ultrasound Images Using Deep Learning | Hiroki Tanaka | Presentation | deep learning | source | 6 |
| 68 | Create Impactful Data Journeys for "Informavores" with SAS Visual Analytics on SAS Viya | Travis Murphy | Have you ever thought about what makes one dashboard or infographic more impactful than another? This paper provides all you need to take your current information products from "huh?" to "aha!" Creating data applications with SAS Visual Analytics is important to ensure that the audience gets immediate and long-term value from your output. Competing for audience attention today is harder than ever, and the term informavore refers to the insatiable need for information today-we are all informavores. Humans need answers or we start filling the answers in without the correct context or data. Our job as data and business analysts is to create data applications that provide a journey from one infofragment to another. This paper steps you through creating impactful output that resonates with your audience, no matter how data literate they are. SAS Visual Analytics on SAS Viya provides so many options to craft the precise journey for all users in an organization. You will gain insight about how to leverage these features in SAS Visual Analytics and how to modernize the way you communicate data stories to your audience. The journey you design for your audience is important for how long they stay and whether they keep coming back to your dashboards and reports in the future. This paper provides you with the ideas and skills needed to create and refine your information products to keep Informavores hooked and keep them wanting more tomorrow | analytics platform, sas viya | source | 22 |
| 69 | Creating a SAS Technical Platform Standard for IFRS 17 | Sumit Kumar | By 2022, IFRS 17 from the International Financial Reporting Standards Foundation has the potential to unsettle the entire insurance industry. It not only brings new regulatory requirements, but the technical capability to support this new regulation can potentia lly introduce new costs to insurance provide rs. As an insurance company, are you ready to support the technical requirements of an end -to-end IFRS 17 platform - including architecture, installation, verification, and validation? Does your current IT organization have the capacity to scale to support this new solution? How ready are you to support an IFRS 17 platform? Is your IT team prepared to implement and support a highly available IFRS 17 platform? If the answer is "NO," then this session is for you! This session touches on the following topics: architectural decisions , security, and best practices (such as why it's important to have a platform standard and how to leverage DevOps tools like Puppet). Also included in this session are specific tips and tricks for preparing your institution to use SAS software to implement your IFRS 17 platform | ifrs 17, sas base, data cleaning | source | 9 |
| 70 | Creating Custom Microsoft Excel Workbooks Using the SAS Output Delivery System, Part 1 | Vincent DelGobbo | This paper explains how to use Base SAS software to create custom multi-sheet Microsoft EXCEL workbooks. You learn step-by-step techniques for quickly and easily creating attractive multi-sheet EXCEL workbooks that contain your SAS output by using the SAS Output Delivery System (ODS) Report Writing Interface (RWI) and the ODS destination for EXCEL. The techniques can be used regardless of the platform on which SAS software is installed. You can even use them on a mainframe! Creating and delivering your workbooks on demand and in real time using SAS server technology is discussed. Although the title is similar to previous presentations by this author, this presentation contains new and revised material not previously presented |
excel, proc report, xls, sample, proc step, ods excel, sas output, output delivery system, sas viya, missing value, character functions, macro variable | source | 33 |
| 71 | Creating Custom Web Applications with SAS Viya Jobs | Robert L. Anderson II | In prior versions of SAS , an oft-overlooked capability was using SAS Stored Processes to stream HTML content and essentially create full -blown web applications that take advantage of the SAS Platform. Using a combination of HTML, CSS, JavaScript, and SAS code, the possibilities were endless. From workflow, to data entry, to geographic mapping applications- if you could envision it , you could probably build it with SAS Stored Processes. With the introduction of SAS Viya, the concept of SAS Stored Processes was initially sunset. Thankfully, the SAS Job Execution Web Applic ation enables much of the same custom application development functionality that SAS Stored Processes did, while enabling you to leverage the full power of SAS Viya. Whether you're familiar with the old approach based on SAS Stored Processes or not, this b reakout session shows you the easiest ways to leverage SAS and HTML with SAS Job Execution Web Application | proc step, sas viya, proc print, macro variable, sas studio, put statement, sas output, data step, ods html | source | 21 |
| 72 | Creating Episodes of Care and Finding Coverage Gaps with Administrative Medical Records | Stephen Lein | Presentation | source | 13 | |
| 73 | Creating Reports that Comply with Section 508 Using SAS 9.4: Planning Is the Most Important Step | Greg Kraus | When reports need to meet Section 508 and other accessibility compliance requirements, those needs are often only addressed in the last stages of creating and designing reports. At that point in the process, decisions have already been made that either limit the ability to make reports accessible or require significant revisions to output that has already been designed and generated. The result is a report that either does not fully meet accessibility requirements or that requires a significant investment in additional resources to fix the problem. This session details the issues report authors need to consider before they write their first line of code when planning for how they will create and deliver their report. This includes the technologies they choose to use (for example, HTML vs. PDF vs | structured data, excel, proc step, title statement, proc sgplot | source | 8 |
| 74 | Customize Your Table of Contents with the ODS Destination for Word | David W. Kelley | The Base SAS Output Delivery System (ODS) destination for Word enables customers to deliver SAS reports as native Microsoft Word documents. ODS WORD generates reports in the Office Open XML Document (.docx) format, which has been standard in Microsoft Word since 2007. The .docx format uses ZIP compression, which makes for a smaller storage footprint and speedier downloading. ODS WORD is preproduction in the sixth maintenance release of SAS 9.4. This paper shows you how to make a custom table of contents (TOC) with ODS WORD. You will learn how to control the placement, text, and style of your TOC. Place your TOC anywhere in the body of your document. Make your TOC title and entry text anything you want it to be. Assign custom colors or fonts to your TOC text. Ev en put a stylish border around your TOC if you like! Adding a TOC to your document makes it more navigable, whether it is in digital or hard - copy format |
proc step, proc template, output delivery system | source | 17 |
| 75 | Customized Output with the SAS Config File: Always Useful but Seldom Used | Zeke Torres | The SAS config file is powerful and useful. In this example, we show how to customize the names of the Log and List files from the code we run, and obtain a useful new name as the | macro facility, macro definition, proc printto, proc step, proc sql | source | 14 |
| 76 | Data Breaches Are Deadly - Let's Move Our Data with Cloud Data Exchange | Ivor G. Moan | Data preparation involves collecting, processing, and cleansing data for use in analytics and business intelligence. These tasks include accessing, loading, structuring, purging, unifying (joining), adjusting data types, verifying that only valid values are present, and checking for duplicates and uniform data (for example, two birthdates for one person). When we look specifically at accessing data, we bump into the first real issue: dealing with sensitive and personal data. Many industries (for example, banks) have very strict rules concerning the movement, handling, and storage of data . Data preparation is playing the role of self- service data management. Traditional data management processes can produce data up to a point, but dynamic fine-tuning and last-minute work is being done in a self-service way, using data preparation tools | sas viya, fedsql, data wrangling, cloud storage | source | 15 |
| 77 | Data Catalog: The Binding Governance Tool for a Successful Digital Transformation | Vincent Rejany | No one today challenges the power of data as a driver for growth and innovation. One after another, companies are starting their digital transformation to add value to their data and to build a data-driven strategy. Unfortunately, most organizations govern their data in an ad hoc or firefighting manner across different parts of the business, and most of the time only within IT. Mapping data by building a data catalog is one of the first steps toward more governance and sustainability. A data catalog is a repository of metadata, centralizing information about data sources, schemas, tables, and columns extended. In this paper, we discuss about the importance of data catalogs how to build a data catalog over the SAS 9 Platform using open-source technologies | data dictionary, data governance, metadata management, proc sql, excel, proc step, rest api, data profiling, sas studio, sas management console, proc contents, data cleaning, ods json, sas library, proc append | source | 29 |
| 78 | Data Entry in SAS Visual Analytics: Is It Possible? | Xavier Bizoux | Achieving data entry within SAS Visual Analytics can be a challenge for many customers. SAS Visual Analytics offers a way to achieve it using the data-driven content object and SAS Viya jobs. This session presents the available techniques that can be used to integrate a data-entry form into a SAS Visual Analytics report. It covers the basic principles of data-driven content objects and the different approaches to storing the data. At the end of the session, you should be able to create a simple data-entry form, insert it in a SAS Visual Analytics report, and store the data in SAS Cloud Analytic Services (CAS) for later use | sas viya, cas, bootstrap, sample, macro variable | source | 20 |
| 79 | Data Preparation Techniques for Cloud-Based Data Sources | Jennifer L. Nenadic | Traditional data warehousing continues to evolve as the industry continues to embrace more and more cloud-based transactional systems. The days of accessing on-premises source system databases to extract data for your data warehouse, report, or analytics model are becoming more and more rare. In order to be ready to successfully navigate this new world, this session focuses on techniques and alterations needed to update your data preparation and data warehousing strategies. These strategic changes address the many differences that are presented by cloud-based source systems | extract, web service, data warehouse, raw data, sas engineer, data mart, data wrangling, data governance, data standard, proc sql | source | 11 |
| 80 | Data Standardization Using SAS Health Data Mapper | Eric Bolender | Businesses across all sectors and domains are combining their vast holdings of big data with high-powered analytics to find solutions to the complex and critical business problems in their industries. A challenge in moving to this data-driven paradigm is first incorporating the wide variety of data sources that companies have available. The health and life sciences industry is no different, as it encompasses everything from clinical trials, which include patient demographics and lab results, to insurance claims | data standard, clinical trial, raw data, dataset, life sciences | source | 14 |
| 81 | Data-Driven Robotics - Leveraging SAS and Python Software to Virtually Build LEGO MINDSTORMS Gear Trains for the EV3 Brick | Troy Martin Hughes | LEGO MINDSTORMS Evolution 3 (EV3) represents the third -generation programmable "Brick," a hand - held computer developed by the LEGO Group that intelligently drives and forms the cornerstone of LEGO robotics. Released in 2013, EV3 leverages LEGO Group -built sensors (including haptic, gyroscopic, ultrasonic, infrared, and others) and servomotors -rotary motors that track speed, degrees, and angle of rotation-to interpret, interact with, and respond to environmental and user stimuli. Although EV3 robotics locomotion begins with large and medium LEGO servomotors, gears and gear trains facilitate complex actions, movements, and the increase of speed or torque. To this end, this paper introduces LEGO gears and simple gear trains, and includes SAS code that programmatically identifies how (and how well) LEGO gears mesh with each other in a two-dimensional (2D) plane. Data-driven software evaluates a table of 41 LEGO gears and programmatically determines where on a virtual 9x9 LEGO stud plane the gears can be placed to mesh. Moreover, by modifying additional tables, the 9x9 stud plane can be replaced with other LEGO Technic beams (or other bricks) to demonstrate where gears can be placed. Additionally, a FUZZ parameter enables the user to specify the number of millimeters of gap or overlap permitted between gears. This data-driven design maximizes software flexibility and configurability, providing dynamic output to meet the needs of different users by modifying tables and parameters only-not code. Finally, the interoperability of data-driven design is showcased in that equivalent SAS and Python code are included, both of which rely on the same parameters and control tables. For more information, please consult the unabridged 69- page text (https://communities.sas.com/t5/SAS-Communities-Library/Data-Driven-Robotics-Leveraging- SAS-and-Python-Software-to/ta-p/641990) and the 30-minute 4K video (https://youtu.be/rvFS0rj6ml4) | goodness of fit, proc report | source | 29 |
| 82 | Dealing with Missing Data in Epidemiological and Clinical Research | Andrew T. Kuligowski, Lida Gharibvand |
Missing values can have a surprising impact on the way data is analyzed and processed. Epidemiological and clinical research typically involve complex data and large databases that frequently contain missing data. The impact of missing data on data analysis and research findings can be significant, so it is important to develop a sound methodology to deal with it. Fortunately, there are powerf ul tools to represent and reference the missing data in SAS analytics. There are a number of SAS functions and procedures that enable differentiated approaches for handling missing data. However, dealing with missing data can still be a bit of a minefield. This paper presents an introduction to categories of missing data and demonstrates some techniques that researchers can use to deal with missing data. KEYWORDS: Missing values, MCAR, MAR, MNAR, Complete case analysis L istwise deletion, Pairwise deletion, Mean Imputation, Regression Imputation, Stochastic Imputation | missing value, imputation, regression analysis, missing completely at random, missing not at random, sample, missing at random, proc calis, mean imputation, correlation, estimate, sample size, proc freq | source | 6 |
| 83 | Delay Analysis in the SAS Middle Tier | Bob Celestino | Investigation of performance delays in large distributed software environments like SAS 9.4 can be quite complex. This paper reviews the aspects of the SAS environment that make it so challenging to analyze. The paper goes on to explain some of the techniques that SAS Technical Support uses to diagnose and solve these issues. For example, the discussion demonstrates how to use log analysis to rule in, or rule out, contributors to the source of the problem. In addition, you will discover how to use high-frequency thread dumps to identify specific problematic components. The discussion also covers how you can use network analysis to measure and identify specific inter-process delays in specific components. Case studies illustrate all the techniques that are presented. This paper is aimed at system administrators, network engineers, and others who might be involved in the investigation of performance issues on the SAS middle tier | proc freq, network data, traffic data, sas administrator | source | 19 |
| 84 | Deploying Computer Vision by Combining Deep Learning Action Sets with Open-Source Technology | Jonny McElhinney, Duncan Bain, Haidar Altaie |
Whilst early computer vision dates back as far as 1927, it has gained momentum in the last years due to the developments in the fields of deep learning and artificial intelligence. With the desire for applying computer vision in ever more general and flexible contexts, the challenge arises how we can push image processing models to production in a robust way. This paper focuses on doing Automatic Meter Reading (AMR) using customer-submitted photos of their meters. The challenges in this context are two-fold. First, we need to localise the box containing the digits, and then we must classify each digit with the correct label, 0-9. Many approaches are available but both of these challenges can be addressed by combining the Deep Learning action set in SAS Visual Data Mining and Machine Learning (VDMML) with DLPy and Keras | deep learning, sas viya, cas action, neural network, machine learning, model training, training set | source | 20 |
| 85 | Deploying Machine Learning Models in an Anti-Money Laundering (AML) Program | Beth Herron, Saurabh Duggal |
As expectations of artificial intelligence (AI) and deep learning have peaked in the financial services industry, anti-money laundering (AML) professionals are exploring advanced methods to more accurately identify suspicious activities that impact their institutions. Fueled by regulatory guidance in the 2018 Joint Statement on Innovative Efforts to Combat Money Laundering and Terrorist Financing, many have set up pilot programs, but few have moved into production. Creating a modern AML platform that can support rapid and automated deployment of models and traditional rule-based scenarios ensures that banks will evolve to keep pace with sophisticated financial crimes. This paper explores use cases for machine learning models in AML and provides examples of how clients are promoting advanced analytics from the sandbox into their production SAS Anti- Money Laundering software environment | machine learning, aml, transaction monitoring, financial crime, champion model, type i error, suspicious activity | source | 11 |
| 86 | Design a High-Performance SAS Grid Infrastructure | Sandeep K. Paul | Presentation: Considering the fact that SAS Grid has become more and more popular among financial institute to run their regulatory runs, it is important to have a GRID which can meet the needs. This E-poster focuses on key Design requirements a High-Performance and reliable SAS GRID Platform. Here we will focus on few key factors which will impact the performance as well as the resiliency of system. | sas grid, high-performance sas | source | 2 |
| 87 | Designing Dashboards for Multiple Target Audiences with SAS Visual Analytics | Kunal Shah | There is no doubt that organizations have a vast amount of data available at their disposal. Yet, they often fall into the trap of creating a single dashboard for a wide variety of audiences to derive meaningful insights from the available data. Different audiences expect different insights from the data. Even the dashboard design thinking differs based on the target audience. This presentation focuses on three key audiences (executives, middle management, and individual contributors) and how to use SAS Visual Analytics to create dashboards that generate powerful insights for each of these. The presentation also covers dashboard design best practices and recommendations from field consultants to help you weave a compelling story from your organizational data | data wrangling, business intelligence | source | 12 |
| 88 | Determining College Student Success | Carl Palombaro | Student loan debt is an extremely hot topic in the media and is continuously on the rise, totaling over 1.5 trillion dollars in the United States alone. The overall goal of this project was to determine a student's best financial option based on the degree they intend to seek, the university they attend, and the average salary per degree after graduation. In order to narrow the scope, this idea was applied to schools in North Carolina within the UNC public school system. The choice to exclude private universities in North Carolina was because many tuition rates at private institutions exceed the amount of funding typically available to a student through federal loans | tranwrd function, proc sql | source | 14 |
| 89 | Developing an Automotive Safety Ontology through Concept Map and Text Analytics | Sadikshya Basnet | Vehicle safety is an important area in the automotive sector; however, there is no standard terminology that all the stakeholders can use. Ontologies have long been argued as one approach for capturing and representing domain knowledge. Ontologies define the terminology of a domain by specifying the relevant hierarchical concepts and their relationships. Ontology development is an expensive and time-consuming process. This paper proposes a concept map-based approach for automotive safety ontology development by first semi-automatically creating a detailed level entities/concepts as a keyword list by applying natural language processing, including word dependency and POS tagging. Specifically, SAS Text Miner 15.1 will be used for analyzing the customer complaint dataset published by NHTSA | source | 7 | |
| 90 | Developing Credit Scores with Telco Data Using Machine Learning and Agile Methodology | Luciano Diettrich | The mobile phones market in Brazil is one of the largest in the world and this huge volume is concentrated in prepaid lines. This distribution reflects the population composed of lower income, lower credit profile and lower bankarization. However, in recent years the prepaid market has been falling, while the postpaid market has grown rapidly, due to the increase in the customer usage of data. This scenario makes selecting customers for upgrade a key point in the strategy of telecommunications companies in Brazil. In this context, Claro Brazil decided to invest more analytical intelligence in credit check for upgrades, which used to consider exclusively external credit score bureau verification during the sale process. Thus, the challenge is to select costumers for postpaid by considering the future propensity pay, optimizing sales channel campaigns, which can lead to increased conversions, improved payment performance and reducing costs with credit bureaus | credit score, proc hpsplit, logistic regression, proc hpforest, default rate, sample, logistic distribution, regression analysis, machine learning, correlation, response variable, data cleaning | source | 14 |
| 91 | Diagnosing the Most Common SAS Viya Performance Problems | Jim Kuell | Applications and the scalable computing environments in which they run have grown in complexity with more advanced technologies. With the mixture of virtual machine, cloud, and emerging container environments, diagnosing the causes of performance issues can be difficult. Relying on significant experience from the SAS Performance Lab, this paper presents the most common SAS Viya performance problems and methods for diagnosing and correcting them | sas viya, sas log, dataset | source | 8 |
| 92 | Digital Trust Index | Andre L. Miceli | Every six months, the Digital Confidence Indicator (DCI) measures the intention of use of technological devices. The current measurement brings a consolidation of views already perceived in previous ones, pointing out that, apparently, the better people's understanding of the digital environment, the greater is their perception that digital devices are both good and evil. There is a perception, for example, that at the same time as new technologies bring anguish, they also create countless facilities, becoming indispensable in everyday life. The last version pointed to a general decrease of the optimism regarding technology-the smallest number ever seen on the DCI. People are optimistic, but less so than they were before. Further evaluation shows that this sentiment is possibly a reflection of political polarization and of discussions that took place through digital devices. Hacking, social engineering, and lack of privacy in general also contributed to the decrease in optimism. The younger members of the population have shown a greater understanding of what the use of such devices means and what they represent. This audience has the lowest DCI. Yet again, people above 65 years old continue are the most optimistic group, crediting technology with minimizing the limitations that age imposes, in addition to recognizing the facilitation of relationships with groups of friends and family through social networks. All of these insights were gained by using SAS. | source | 2 | |
| 93 | Do More with Less: Programming Efficiently with Keyboard Macros | Breanne Young | Time spent searching through previous programs for recycled code can be repurposed through the utilization of keyboard abbreviations. Keyboard abbreviations are a means of creating keystroke shortcuts or abbreviations that tie directly to a curated library of code. This library can then be distributed through the use of keyboard macro files, enabling an increase in coding efficiency among teams | sas enterprise guide, proc sort | source | 7 |
| 94 | Don't Just Survive, Thrive with This Multi-Stratified Cox Proportional Hazards Model Macro | Katelyn J. Ware, Paul Egeler |
Survival analysis is a commonly used set of techniques for applied data analysis where the outcome variable is the time until an event. One of the most frequently used techniques for modeling this type of data is the Cox proportional hazards model, which can be implemented in SAS with the PHREG procedure. This model assumes that the ratio of hazards for any two individuals is constant over time, that is, they are proportional. If this assumption is not met for any particular covariate, stratification is one method to still include and control for the effects in the model. It then must be decided if there is an interaction involved between the stratum levels and predictors in the model. However, it can be cumbersome to manually code the interactions with all levels of stratum in SAS when there are multiple stratum variables involved. The new and improved lrt_strat_cox_ph macro simplifies this procedure by allowing for multiple stratum variables in its strata_vars parameter. The new feature makes the macro a powerful tool in applied survival analysis. This paper discusses the use of multiple stratifying variables, how they are implemented in SAS, and includes a practical example using the macro to tie it all together |
interaction effect, likelihood function, survival analysis, proc phreg, hazard function, binary variable, macro variable, maximum likelihood estimation, proportional hazards assumption, degrees of freedom, proc step, censoring indicator, time variable, regression analysis, macro definition | source | 11 |
| 95 | Don't Overwrite Me! A SAS Macro to Identify Variables That Exist in More Than One Data Set | Andrea Barbo | Presentation | macro definition | source | 7 |
| 96 | Dynamic Programming With The SAS Hash Object | Paul M. Dorfman | The original impetus behind the development of the SAS hash object was to provide SAS programmers with a fast and simple-to-use memory-resident storage and retrieval medium that could be used for matching data sets via table lookup. However, the forward-looking SAS R&D team ended up with a product that goes far beyond this simple functionality by creating the first truly run-time dynamic SAS data structure. At the basic level, its dynamic nature is manifested by the ability of a hash object table to grow and shrink at run time, ridding the programmer of the need to know its size in advance. At a more advanced level, the very structure and behavior or a hash table can be affected at run time via argument tags that accept general SAS expressions. Finally, a hash object can be made store pointers to other hash objects and used to manipulate them as pieces of data. Combined, these features entail possibilities for dynamic programming unfathomed in the pre-hash SAS era. This paper is an attempt to illustrate, via a series of examples, at least some of these possibilities and hopefully sway some programmers away from hard-coding habits by showing what the SAS hash object can offer in terms of truly dynamic DATA STEP programming |
hash table, do loop, sas log, sas programmer, sample, hash iterator, sas expression, by group processing, conditional do loop | source | 23 |
| 97 | Dynamic Programming: Using Files to Change the Behavior of Your Program | Bryan K. Merrell | Dilemma: A company much larger than mine was providing information on how data was to be received. Their documentation was over a thousand pages of data transforms and usage and could change at any time. You have very little influence on this 3rd party Solution Become more pro-active. Create a file that lists out all field transforms to be able to change those values as they change from the provider | data transformation, macro definition | source | 4 |
| 98 | Economic Impacts of Sea Level Rise on Coastal Real Estate | Austin Willoughby | As a result of the industrialized global economy, an increase of carbon and methane gas concentrations in the atmosphere has contributed to an overall warming climate, and in turn, global sea level rise. Such a change has vast implications for coastal communities. This study models the potential loss of value coastal properties in New Hanover County, North Carolina will experience due to sea level rise. The methods used were linear regression to predict the future value of properties in addition to methods used in ArcGIS in order to find the parcels affected by different increments of sea level rise. This analysis could be used to inform coastal residents on the potential loss in value of their property and what to expect in future years | variance, regression analysis, proc reg, missing value, bar chart | source | 9 |
| 99 | Efficient Release Management with SAS | Lars Tynelius | Release management is a process for managing different software versions between environments. This paper examines how you can improve the quality, speed, and efficiency of building or updating software and have a reliable release management process. By creating a web application with SAS Stored Processes as the back end, we can easily create, export, import, and promote releases through the different environments in an automated and highly configurable dynamic way. Different content can be included in the release such as SAS metadata, configuration files and tables, SAS macros, migration jobs, and so on | sysrc, macro variable, proc step | source | 14 |
| 100 | Empower and Inspire-Designing Reports for Mobile Experiences.docx | Khaliah H. Cothran | What is the impact of mobile on business intelligence and reporting? According to Gartner, approximately one third of business intelligence users consume reports, dashboards, and KPIs on mobile devices. Since this content is being viewed on non-traditional devices with less screen real estate, dashboards and reports must be designed to deliver information in an easy and consumable manner. Users expect content to look great regardless of the mobile device screen size, orientation, or functionality. Hence, mobile reports need to be optimized to display data effectively so that users can easily access, interact with, and modify visualizations. SAS Visual Analytics Apps empower users with reports and dashboards anywhere and on any device. In this paper, you will learn design and usability best practices informed by user experience research and cognitive science to help create beautiful reports that guide interaction in SAS Visual Analytics Apps. This paper presents tips for how to reduce information density with space saving-features, as well as do's and don'ts for selecting objects, fonts, and colors that enable complex analytics to be processed quickly. Come learn how to create effective reports that scale from your desktop to your mobile device screen | bar chart, scatter plot, line chart, business intelligence, bubble plot, histogram | source | 18 |
| 101 | Enabling Real-Time Stability Monitoring using SAS Viya and SAS Event Stream Processing | Sanjeev Heda | Today with the Internet of Things (IoT), devices generate vast amounts of data that can be used to derive insights into the health and operation of an asset. To realize value from this data, analytics are needed in order to provide context for the state of the asset. In industrial applications, one such type of analytic is stability monitoring. These models provide an intelligent way to track the ongoing health of an asset where the metric of interest is checked against the bounds generated by the model. Automating such models to function in real time delivers immediate business value beyond detecting abnormal behavior because, in many cases, they provide the first step in identifying an invaluable window of opportunity to course- correct in mission-critical situations. To that end, these models need to be deployed against streaming data where they can continuously analyze the data | sas model manager, stream processing, real-time processing, alarm management, predictive maintenance, response variable, model inventory, sas viya, regression analysis, proc sql | source | 12 |
| 102 | Enhance Customer Risk Scoring by Quantifying the Opportunity to Commit Financial Crime | Steve Overton | Analytical scoring models help organizations quantify risk and make appropriate data-driven decisions across many areas. Fraud and financial crime related scoring models consist of complex formulas and mathematical equations designed to measure an outcome or target activity using multiple inputs and dimensions. Traditional scoring models measure commonly accepted dimensions across known activity, demographics and product behavior but often do not consider the means and opportunity for a criminal to transact through other entities or networks of bad actors. This paper will present a methodology and propensity modeling approach which enhances financial crime risk scoring models by analyzing the framework and infrastructure available to commit financial crime | financial crime, relational database, customer due diligence, kyc, network analytics, proc network, proc step | source | 14 |
| 103 | Enhancing Academic Teaching of Data Science with SAS Viya for Learners | Stefan Dimitrov Stoyanov | SAS Viya for Learners offers many advanced tools for studying data science. I found the fast cloud suite especially helpful to overcome obstacles in academic teaching that hinder novice learners. As a newcomer to data science lacking strong statistics knowledge, it was difficult to promptly read large textbooks or absorb in-depth knowledge from slides while in school, with limited hours for consultations with lecturers and complicated software. My paper discusses the features of SAS Viya for Learners that helped me to overcome these obstacles: interactive courses in statistics, programming, and advanced analytics, videos, summaries, examples, practices, case studies, papers, documentation, online chat with educational experts, webinars, and communities. The advantages of SAS Viya for Learners over IBM SPSS Modeler and R made it much easier to comprehend machine learning and appreciate the entire analytics life cycle thanks to Model Studio and other solutions. I found it enabled students' collaboration, combining well-structured visual analytics workflow with SAS and open-source code, parameters that are easy to set up, automated hyperparameter tuning, assessment visualizations, and best model selection. By including SAS Viya for Learners in academic curriculums, I strongly feel that educators could teach more creatively to speed-up students' deep data science comprehension, providing better- prepared professionals to bridge the gap in data science skills. Join me for my presentation to find out more | sas viya, data science, machine learning, data mining, sas model manager, champion model, hyperparameter tuning, response variable | source | 30 |
| 104 | Essential Performance Tips for SAS Visual Analytics | Meera Venkataramani | Troubleshooting performance-related issues across distributed systems is a challenging task, and it requires a step-by-step approach to identify the cause of the bottleneck. When it comes to performance, there are a myriad of factors involved, with each one contributing in its own way to the overall problem. Because SAS Viya is a broad system with many distributed layers, the variance and complexity is what makes the problem hard to solve, and often causes approaches to fail on given architectures. This paper takes a holistic look at the SAS Viya and explores the various processes and methodology involved in diagnosing performance problems with SAS Visual Analytics. Using a customer use case, we will demonstrate to you the diagnostic steps, forensic process, and the tools and methods involved in this process | sas viya, cas | source | 12 |
| 105 | Estimating Jump Diffusion and VaR Models of REITs Returns Using High-Frequency Data in SAS | Tunde Odusami | This paper examines the roles of jumps in the time series of Real Estate Investment Trust (REIT) returns. Using realized variance, bipower variation, and high-frequency REITs data, the paper shows how SAS programming can be used to estimate the intensity and magnitude of jumps in the returns and the volatility of the returns of a portfolio of REITs securities. The paper also shows how value at risk (VaR) can be estimated for a portfolio of REITs securities when jumps or discontinuity exist in the data . The forecasting accuracy of jump augmented VaR specifications is also compared with other VaR specifications, including those generated from some built-in procedures in SA S, such as the SEVERITY and SURVEYSELECT procedures | market risk, confidence level, tail var, normal distribution, proc freq, bootstrap, sample, proc step, likelihood function | source | 25 |
| 106 | Estimating Unknown Change Points and Variation Using SAS | Depeng Jiang, Lin Yan, Zhongyuan Zhang, Shaoping Jiang |
Change point (or knot, joint, turning point) was defined as "the time when development switches from one phase to another". Piecewise growth curve model (PGCM) is often used to estimate the underlying growth process. When fitting a PGCM, the conventional practice is to specify the change points a priori. However, the change points were often unknown and misspecifications of turning points could lead to bias of growth trait estimation. Also, there was individual variation in the change points. To estimate the individual specific change point, several different estimation methods (e.g., the profile likelihood, the first-order Taylor expansion and the Bayesian estimation methods) were proposed. Some R packages were developed to estimate the unknown change point. In SAS, the NLMIXED procedure was used to fit the nonlinear random effects models and could potentially be used to estimate the change points |
proc mixed, sample, longitudinal data, proc calis, covariance, regression analysis | source | 14 |
| 107 | Evolutionary Feature Selection for Machine Learning | Nandini Rakala | We propose an evolutionary feature selection technique for the machine learning predictive modeling task, involving two conflicting goals of minimizing the number of features and maximizing the prediction accuracy of the applied machine learning algorithm, in a multi- objective pareto-based dominance form. The evolutionary feature selection approach involves the steps of population initialization, crossover, mutation, and selection, based on a genetic algorithm mimicking the natural evolutionary process. The machine learning problem is thereby defined as a multi -objective optimization model involving the simultaneous optimization of the given objectives, producing a set of optimal solutions, called the Pareto set, where each solution of this set has a different trade-off between the two objectives. We compare the accuracy and run -times using different feature selection approaches and compare it on real-life datasets to show how the proposed evolutionary multi-objective feature selection approach outperf orms the rest, along with theoretical justification based on combinatorics and optimization | feature selection, machine learning, gradient boosting, regression analysis | source | 12 |
| 108 | Evolve your SAS Administration Skills with SAS Viya | Jack McGuire | Released in 2016, SAS Viya is the newest evolution in the SAS Platform, offering the latest high-performance analytics. SAS Viya brings a fundamental change to the SAS Platform, which has led to changes in how the platform is administered. As an experienced SAS9 administrator, the thought of having to deal with these changes seemed daunting, especially since learning that there is no metadata or SAS Management Console in SAS Viya. Fortunately, SAS Viya has been designed with the administrator in mind. A single web administration user interface, SAS Environment Manager, an HTML5 web application, centralizes the administration tasks. This paper provides examples of common administration tasks in SAS Viya by relating them to the corresponding tasks in SAS 9. By comparing these tasks, administrators familiar with SAS 9 will not only gain confidence with SAS Viya but will also see the benefits that SAS Viya can offer them | sas viya, sas 9, cas, sas administrator, sas studio | source | 14 |
| 109 | Examining the Impact of Discussion Activities on Student Understanding in Introductory Statistics | Rachael Becker | Presentation | source | 9 | |
| 110 | Expanding SAS Grid Manager for Platform: Lessons from the Field | Bill McMillan, Qingda Want |
SAS Grid Manager provides a powerful multi-tenant computing environment that enables high availability and accelerates processing for analytical workloads. "SAS Grid Manager for Platform" and "Platform Suite for SAS" are built upon IBM's LSF family of products. In addition to powering the vast majority of SAS Grid deployments, LSF manages the Summit (#1), Sierra (#2), Lassen (#10) and Pangea3 (#11) systems on the Top5001 list, and the compute grids of many of the world's largest semiconductor, automotive, aerospace and financial institutions. These large-scale deployments may seem worlds away from your typical SAS Grid, but they use the same underlying LSF technologies and capabilities which can be leveraged in your SAS Grid environment today to increase user productivity and deliver better business outcomes. This paper will examine four ke y areas where we have helped SA S Grid users and others to derive increased business value by leveraging additional LSF functionality | sas grid, sas viya, machine learning, access control, life sciences, rest api | source | 19 |
| 111 | Expedite Analytics Deliver and Empowerment with a Common Analytics Base Table | Jennifer L. Nenadic | One of the most time consuming and challenging aspects of an analytics project is the data preparation, and with each new project, that data preparation effort often starts at ground zero once again. It doesn't always have to be that way! Learn from how the data and analytics organization at SAS has taken the concept of an Analytical Base Table (ABT) to the next level by creating what we call a Common ABT. This Common ABT can be used to jump- start many different analytics projects, significantly reducing the time to delivery while also creating the foundation for analytics empowerment. This technique will help you shift out of the reactive approach to your data preparation and get more proactive and strategic. During this session, we talk about both the technical aspects of the Common ABT approach as well as process-related and people-related recommendations to set your organization up to be successful | data wrangling, data science, sample, champion model | source | 11 |
| 112 | Explanatory Machine Learning Model Plots for Epidemiological and Real-World Evidence Studies | David Olaleye | For real-world evidence (RWE) and epidemiological studies, comparative effectiveness research (CER) provides actionable information for drug regulatory and health care decision-making. CER relies on white box statistical and machine learning (ML) models for estimating treatment effect and drug safety evaluation. Black box ML models are also powerful for generating better predictions, but how to interpret the model results is not straightforward. How should ML model results be presented to regulators to assure them that the results are accurate, fair, and unbiased? How should ML model findings be presented to end users to overcome the stigma of black box model bias? We provide a standardized interpretability plots framework for evaluating and explaining patient -level ML models using observational health-care data | sample, machine learning, response variable, data mining, proc hpforest, boosting, logistic distribution, proc calis, regression analysis, logistic regression, partial dependence, raw data, data cleaning, gradient boosting, macro loop, champion model, model training, macro definition, %if-%then-%else, proc step, insurance claims, nominal variable | source | 22 |
| 113 | Exploring DataOps in the Brave New World of Agile and Cloud Delivery | Shane Gibson | DataOps is the new black, providing a combination of DevOps and agile practices to automate the creation and management of data, analytics, and visualization on cloud platforms. DataOps is revolutionizing the way we deploy and manage data platforms, and SAS Viya is embracing DataOps within it's core. In this session, I outline some of the key components required to adopt the new DataOps way of working and provide examples of the benefits each component will deliver. If you want to understand what DataOps is and how you should leverage it to build and manage better data platforms and processes, then this is the session for you | data pipeline, proc freq | source | 10 |
| 114 | Exploring Online Drug Reviews using Text Analytics, Sentiment Analysis, and Data Mining Models | Thu Dinh, Goutam Chakraborty |
Drug reviews play a very significant role in providing crucial medical care information for both healthcare professionals and consumers. Customers are utilizing online review sites to voice opinions and express sentiments about experienced drugs. However, a potential buyer typically finds it very hard to go through all comments before making a purchase decision. Another big challenge is the unstructured and textual nature of the reviews, which makes it difficult for readers to classify comments into meaningful insights. For these reasons, this paper primarily aims to classify the side effect level and effectiveness level of prescribed drugs by using text analytics and predictive models within SAS Enterprise Miner. Additionally, the paper explores specific effectiveness and potential side effects of each prescription drug through sentiment analysis and text mining within SAS Visual Text Analytics | data cleaning, response variable, proc freq, data mining, probability distribution, feature selection, parsing, proc hpsplit, neural network, histogram, validation set, regression analysis, character variable, training set | source | 23 |
| 115 | Expressions in Graph Template Language and Other Tips | Prashant Hebbar | This paper is a reprise of the SAS Global Forum 2013 paper entitled "Free Expressions and Other GTL Tips". SAS Graph Template Language (GTL) provides many powerful features for creating versatile graphs. The statistical graphics capability in GTL lets you use expressions directly in the templates in order to simplify data preparation. This presentation covers some ways of using DATA STEP functions to create grouped plots based on conditions and to select a subset of the observations. It also illustrates how to use the SAS Function Compiler (FCMP) procedure functions in GTL expressions. Tips on using non-breaking spaces for creating "chunked" graphs and indenting text are discussed. Workarounds for rendering Unicode characters using data column variables are also discussed |
scatter plot, graph template language, user-defined format, proc step, adverse events, sas base, macro variable, proc template, proc format, proc sgplot, proc fcmp | source | 16 |
| 116 | Face Recognition Using SAS Viya: Guess Who the Person Is! | Pratyush Dash, Anvesh Reddy Minukuri |
Presentation | sas viya | source | 8 |
| 117 | Fast Deployments and Cost Reductions: SAS in the Azure Cloud with HDInsight and the Azure Data Lake | Andrew Williams | Cost reduction is often a key objective of businesses but reducing costs without compromising on performance can be a challenge. The objective of this project was to reduce costs while improving performance. We found that combining SAS with Microsoft Azure Data Lake Gen2 could be part of the solution. It encompasses the power of a file system that is compatible with Apache Hadoop with integrated hierarchical namespace, along with the massive scale and economy of Microsoft Azure Blob storage. With the use o f SAS Grid Manager for Platform, our Kerberized platform talks to multiple Microsoft Azure HDInsight clusters, enabling us to distribute the different demands of users while reading from the same data lake. There are some key requirements that you need to be aware of prior to deployment but once they are in place, the deployment time for a new SAS and Hadoop cluster is hours, rather than weeks or months. The advantage of having the data physically stored separate from the Hadoop cluster is that once you have destroyed the cluster, the data is still there. In this paper, we discuss this further and explain the steps required to make the most out of SAS integrating with Azure HDInsight on the Azure Data Lake Gen2 | data lake, sas grid, azure blob storage, sas viya, proc sql | source | 13 |
| 118 | Finding Your Seat at the Table | William Coar | Have you ever found yourself wondering how to take that next step, wishing you had more of an impact, participating in discussions about how to move your organization's ideas forward? Do you think you warrant a seat at the strategic table? The answer is likely yes, but the opportunity and respect will need to be earned. The programming profession is dominated by technical expertise, but this alone will only advance you so far. Leadership skills must be learned and applied to make a broader impact within your organization. There are many definitions of leadership, but most are variations of a common theme. We view leadership as the ability to influence others to take specific action when they have the freedom to choose to do otherwise. This fundamentally differs from management. Many leadership principles are established and are not industry specific. Our technical training rarely emphasized or even taught these principles | sas programmer, sas programming, pharmaceutical industry, statistical programmer | source | 14 |
| 119 | Fitting Statistical Models with PROC MCMC | Mark Ghamsary | Bayesian inference, in particular Markov Chain Monte Carlo (MCMC), is one of the most important statistical tools for analyses. Although there is free access to many powerful statistical software tools for Bayesian analysis, still, it is challenging both t o learn and to apply to real life research. SAS has facilitated many procedures using Bayesian analysis which make it much easier to use, particularly for SAS users. This presentation demonstrates various examples such as 'one sample proportion', 'two sample proportion', and 'two sample t-test', to more advanced models via Bayesian analysis. The results will be compared with non-Bayesian models. Many real-life examples in medicine, clinical trials and meta-analysis will be given. Keywords Gibbs Sampling, Markov Chain Monte Carlo, Meta-Analysis, Binary Regression | sample, regression analysis, proc step, bayesian statistics, monte carlo, odds ratio, markov chain, proc genmod, mcmc, sample size, likelihood function, posterior probability, proc mixed, poisson distribution, relative risk, poisson regression, binomial distribution | source | 26 |
| 120 | Five Simple Ways to Know If Variables in a Table Are All Missing | Xia Ke Shan, Kurt Bremser |
Before doing data analysis, we sometimes need to know if certain variables in a table are all missing, and need to drop these all-missing variables. This paper is introducing five simple ways to achieve this goal. Unlike DATA STEP code which needs more code to accomplish it, these five simple ways only cost several SAS statements |
proc step, proc sql, character variable, numeric variable, proc freq | source | 6 |
| 121 | Forecasting Healthcare Statistics At the Cleveland Clinic | Michael Bromley | A thorough understanding of key volumes is of utmost importance to effectively manage a hospital system. As the Cleveland Clinic's analytical culture matures, we see a shift from reactive decision making to proactive decision making. This shift in emphasis calls for the increased application of statistical forecast models inserted into stakeholder work flows and at the fingertips of leaders and key decision makers. However, model development and deployment are not enough. Active end-user engagement and buy in are critical in order for forecasting methods to be accepted and used to make decisions. This requires consistent interaction between our data scientists and end users beyond just the modeling and validation process. This presentation covers Cleveland Clinic's complete journey from model inception through end-user acceptance and adoption for weekly business management | sample, time series analysis, monte carlo, standard deviation, feature selection, proc step, proc esm, forecast horizon, data warehouse, proc sql, data cleaning | source | 10 |
| 122 | Forecasting Hourly Electricity Prices | Joseph W. Perez | Forecasting power prices can be more of an art than it is a science. Locational Marginal Prices (LMPs) exhibit high volatility-a phenomenon known as volatility clustering as with many financial time series data. This paper will not discuss the mean reverting properties of power prices and assumes the reader has a basic understanding of the autoregressive nature of electricity prices. This method forecasts LMPs by preserving past volatility in the form of ratios and assigning them to corresponding load in the forecast. The purpose is to "shape" the forecasted series by utilizing historical profiles while holding to supply and demand fundamentals- high electricity usage tends to be associated with higher energy prices | sample, time series data | source | 3 |
| 123 | Forecasting Made Easy: A Macro to Select an Optimal Exponential Smoothing Model Using SAS Proc ESM | Bil Westerfield | Presentation: An accurate forecast is an invaluable tool for anticipating changes which may require a policy, budget, or other response. In health care, there are many potential applications including enrollment, utilization, cost, and operational processes. SAS PROC ESM generates forecasts with the option to use a variety of different exponential smoothing methods, however, deciding on which method to use is a challenge. Using publicly available Medicare Advantage (MA) enrollment data, we will demonstrate a macro that makes selecting the best performing forecast model easy and intuitive, so that users are able to create reliable forecasts to inform decision-making. |
proc esm, proc step | source | 8 |
| 124 | From AI to XI: It all starts with 'HI' | Tho H. Nguyen | "HI" is a universal way to greet family, friends, and acquaintances. It is a common word we say many times every day. "HI" can also be very powerful in the context of human intelligence. Humans have always been the core for delivering intelligence, and we as humans provide the intelligence that feeds many XI platforms such as business intelligence, data intelligence, customer intelligence, machine intelligence, artificial intelligence, and augmented intelligence. Humans have the brain power that is the platform for providing pervasive intelligence | business intelligence, artificial intelligence, machine learning, customer experience, relational database, data cleaning | source | 7 |
| 125 | From Device Text Data to a Quality Data Set | Laurie Smith | Data quality in research is important. It may be necessary for data from a device to be used in a research project. Often it is read manually from an external file and entered onto a CRF. Then the data is manually read from the CRF and entered it into a database. This process introduces many opportunities for data quality to be compromised. The quality of device data used in a study can be greatly improved if the data can be read directly from a device's output file directly into a dataset. If the device outputs results into a file that can be saved electronically, SAS can be used to read the data needed from the results and save the data directly into a dataset. Quite often, device data is saved in separate files per subject and it is often difficult to import each separate file into SAS withou t great effort | dataset, do loop, filename statement, txt, data step, macro variable | source | 11 |
| 126 | From Santiago to the Spirit Lake Nation: 30 Things I Learned in 30 Years as a Statistical Consultant | AnnMaria De Mars | All consultants face a few common problems; getting clients, getting the data, getting to know their data, fixing data problems, finally getting to the statistical analysis and communicating the results to an audience. Af ter getting paid, communication is the most important part of the process. If your client doesn't understand your analysis, the findings aren't going to be applied and you're not going to get repeat business. Fortunately, | proc step, proc freq, numeric variable, proc reg, logistic regression, standard deviation, regression analysis, data science, correlation, logistic distribution, odds ratio, factor analysis, missing value, proc glm, repeated measures, proc corr, multicollinearity, outlier, data cleaning, proc genmod, proc format, binary variable, quality control, sample size, repeated measures anova, sample, proc sgplot, anova, proc means | source | 20 |
| 127 | From Silos to Ecosystem - Evolution of Analytical Platforms at Elisa Corporation | Prasanna Pandian | SAS in Elisa was originally a standalone product where metadata, data and users were siloed in three separate environments for SAS Enterprise Guide, SAS Enterprise Miner and SAS Visual Analytics. Data tasks were manually executed, and the data produced by SAS was not available to other environments. Open source team relying on R and Python were not able to utilize the SAS datasets directly. In late 2018, as part of the enterprise BI architectural strategy, Elisa ventured to build a new SAS Analytics & Data Ecosystem - a collection of infrastructure, analytics and applications used to capture and analyze data. By deploying modern SAS Viya and SAS 9.4 platforms Elisa implemented standardized approaches, automation, tools, and processes. Current architecture enables them to effortlessly share data artifacts and collaborate on projects across corporate functions and platforms | sas viya, dataset, sas enterprise guide, data mining, machine learning | source | 7 |
| 128 | Getting from Governance Practice to Data Awareness | Chris Replogle | If you want to have a good practice of governance on assets with your organization's employees, then it is vital to put good policies, procedures, measures, and rules in place to manage those assets. Good management of assets alone is not enough in the connected and information-rich world we engage in; we must strive to have good practices for people and processes to follow in order to ensure compliance. These policies and practices need to be fed and managed by good information sources and tools. These assets are typically described or managed with a system such as a catalog. The catalog enables your systems and people to collect and manage information about your information or about the metadata of your systems. This catalog enables you to add information, relate assets, assign responsibilities, and find the information necessary to uphold and implement the policies you designate in your governance practice. The practice of cataloging assets such as data, processes, analytical models, digital locations, and intellectual property gives you the ability to not only inventory the assets, but, through their relationships, you get insight into how they are connected. This connection or relationship among assets can provide you with knowledge in how assets are created, consumed, produced, processed, classified, or are influential within your systems. This knowledge or awareness of your assets leads to more insight into how relevant information is- in other words, you have data awareness | data cleaning, data lake | source | 10 |
| 129 | Getting the Time Right Across Time Zones | Frank Poppe | Suppose you enter in your digital agenda the date and time for a presentation in, say, Washington, while living in another time zone. When in Washington you look at your agenda and wonder: in what time zone is the time displayed? The local time at home, or the current time? Those kind of problems occur also when computers in different time zones work together, or when daylight savings time (DST) is involved. A common practice to solve this is to store all time information in UTC-time: Universal Coordinated Time, commonly known as Greenwich Mean Time (GMT). SAS has several functions and formats that translate UTC time to local time and vice versa, but there is still room for misunderstandings. SAS has to know the local time zone, and there are two ways of defining them. One of those take DST into consideration, the other does not. The formats produce long strings, often too long to use as tick marks in graphs | proc step, proc format, date variable, format statement, sas function, options statement | source | 22 |
| 130 | Git for the SAS Programmer: Using Source Control to Organize Your Code and Collaborate with Others | Amy Peters | Are you tired of using elaborate comments in your code or saving multiple copies of your files to manage changes as you make them? Wish you could go back in time to that version of your program that worked? Do you live in terror of clobbering someone else's work? Version control can help, and the front runner in the version control world is Git. Git is a free and open source distributed version control system that you can use on your own or in collaboration with others. It can also be used with a central, shared repository such as GitHub or Bitbucket. Learn Git concepts such as clone, commit, and merge, and how to execute them using the Git interfaces in SAS Studio and SAS Enterprise Guide or in code using SAS functions | sas studio, sas enterprise guide, data step | source | 18 |
| 131 | Git it? Got it? Good: Migrating from SAS to SAS Viya Using Git | Cameron Estroff, Weston McManus |
You have established your workflows on SAS9 but are excited about the possibilities of moving to SAS Viya. What does it look like to do SAS in-memory, and in a clientless world? And what are you going to do with your current code? Let us help you get there with some ideas about how to migrate your code using the power of Git! | sas viya, sas enterprise guide, sas studio, sas base, sas programmer | source | 9 |
| 132 | Hate Speech Classification of social media posts using Text Analysis and Machine Learning | Venkateshwarlu Konduri | Hate crimes are on the rise in the United States and other parts of the world. Hate speech is one tool that a person or group uses to let out feelings of bias, hatred and prejudice towards a religion, race, ethnicity, ancestry, sexual orientation, gender or disability thereby spreading hatred. This paper focuses on how SAS Enterprise Miner's Text Analytics was used to develop a model that categorizes tweets based on their content, specifically hateful vs normal. After sampling and cleaning of the data and breaking the tweets down into quantifiable components, different models were built and compared. The best performing model was used to score unseen data, achieving reasonable accuracy in classification. This paper touches upon how text analytics could be harnessed by organizations like Twitter for encouraging civic responsibility in its users | data cleaning, data mining, champion model, proc freq, gradient boosting | source | 7 |
| 133 | History Carried Forward, Future Carried Back: Mixing Time Series of Differing Frequencies | Mark Keintz | Many programming tasks require merging time series of varying frequency. For instance, you might have three data sets (YEAR, QTR, and MONTH) of data, each with eponymous frequency and sorted by common ID and date variables. Producing a monthly file with the most recent quarterly and yearly data is a hierarchical last-observation-carried-forward (LOCF) task. Or, you might have three irregular times series (ADMISSIONS, SERVICES, TESTRESULTS), in which you want to capture the latest data from each source at every date encountered (event-based LOCF). These are tasks often left poorly optimized by most SQL-based languages, in which row order is ignored in the interests of optimizing table manipulation. This presentation shows how to use conditional SET statements in the SAS DATA STEP to update specific portions of the program data vector (that is, the YEAR variables or the QTR variables) to carry forward low frequency data to multiple subsequent high frequency records |
proc sql, set statement, time series analysis, data step, proc step, proc freq, sample, call missing, date variable, last observation carried forward | source | 14 |
| 134 | How a SAS Function and the ODS PACKAGE Statement Will Help You Save Money and Space | Ricardo Rosales | In these times when data storage is costly, organizations often keep their data in compressed files on their servers to save both money and space. It can be tedious and time consuming to take data out of compressed files, use the data, and then delete the extracted files. However, with the use of the GETOPTION(work) function, the contents of a dataset in a compressed file can be read and processed in the work library. This paper demonstrates how to unzip files into the work library and how to create new zip files using the ODS Package Destination. This will keep your network space in a good shape |
work library, output delivery system | source | 7 |
| 135 | How Many Shades of Guide: SAS Enterprise Guide to 8.1 and SAS Studio to 3.8 with SAS 9.4 | Philip R. Holland | I have been using SAS Enterprise Guide since version 1.1.1 in 2001, and SAS Studio since version 3.1 in 2013. SAS Enterprise Guide version 8.1 and SAS Studio version 3.8 have developed immensely from their original releases. This paper tracks their progress and convergence towards the perfect SAS development environment. If you are not yet using SAS Enterprise Guide or SAS Studio, then your IT department may be unnecessarily overworked, and you are missing out on some very useful programming features | sas enterprise guide, sas studio, dataset, sas programmer, sas log | source | 11 |
| 136 | How SAS Viya Provides a Way to Deliver Analytics to Everyone: Image Classification Examples | Pavel Rogatch | We are living in an era when machine learning (ML) helps to solve complex tasks for enterprises and improve productivity. ML techniques such as image recognition are continually improving, but they are of no use if an enterprise cannot easily implement the m. The scarcity of data science experts and the high costs associated with hiring them can be a significant barrier, especially for small and midsize enterprises. In this presentation, we argue that modern tools like SAS Viya provide an opportunity even for small enterprises to build complex models with high accuracy. For this purpose, we use MNIST and Fashion - MNIST data sets that have been used to test image classification methods. We show that using SAS Viya with default settings and without coding, you can get a model that misclassifies about 3.5-6.0% and 11.1-11.9% of images for the first and second data sets, respectively. Simple tuning with a graphical user interface enables you to achieve misclassification rates as low as 2.3-2.7% and 9.8-10.7%. In many cases, such rates are satisfactory for enterprises, and they can benefit from machine learning techniques even if for any reason they lack data scientists. SAS Viya makes the process of building complex models simple and time-efficient and opens opportunities for all kinds of people within enterprises to perform data analysis. This presentation is intended for a technical audience with a basic knowledge of SAS as well as for non-experts in data science who want to start using ML | machine learning, gradient boosting, data science, neural network, sample | source | 5 |
| 137 | How To Access and Manage Microsoft Azure Cloud Data using SAS | Kumar Thangamuthu | The popularity of cloud data storage has grown exponentially over the last decade as more and more organizations are transitioning from on-premises to cloud data storage and data management. Microsoft Azure is one of the big players accelerating the move to cloud. In this paper, we cover the following topics: Overview of SAS to access and manage data in Azure Storage. SAS best practices and options to work with big data and relational databases in Azure Storage. This paper contains data access examples and use cases to explore Azure cloud data using | proc sql, data lake, sas viya, sql server, dataset, cas action, proc step, mysql, sas library, cpu time, libname statement, relational database, sample | source | 14 |
| 138 | How to Become a Lazy Programmer | Balakumar Marappa Gounder Ponnusamy | As a programmer, how often do you see yourself writing the same skeleton of a code again and again? Quite often right? What if you can cut down that time just by using the necessary fillers? This paper will walk you through how to implement and share abbreviation macros using SAS Enhanced editor on Windows. This is nothing but an approach to become a lazy programmer in SAS Enterprise Guide | sas enterprise guide, proc sql | source | 6 |
| 139 | How to Build a Text Analytics Model in SAS Viya with Python | Nate Gilmore | Python is widely noted as one of the most important languages influencing the development of machine learning and artificial intelligence. SAS has made seamless integration with Python one of its recent focal points. With the introduction of the SAS Scripting Wrapper for Analytics Transfer (SWAT) package, Python users can now easily take advantage of the power of SAS Viya. This paper is designed for Python users who want to learn more about getting started with SAS Cloud Analytic Services (CAS) actions for text analytics. It walks them through the process of building a text analytics model from end to end by using a Jupyter Notebook as the Python client to connect to SAS Viya. Areas that are covered include loading data into CAS, manipulating CAS tables by using Python libraries, text parsing, converting unstructured text into input variables used in a predictive model, and scoring models | data cleaning, sas viya, cas action, auc, roc curve, training set, validation set, support vector machine, proc freq, machine learning, model training, predictor variable | source | 13 |
| 140 | How to Explain Your Black-Box Models in SAS Viya | Funda Güneş | SAS Visual Data Mining and Machine Learning in SAS Viya offers a number of algorithms for training powerful predictive models, such as gradient boosting, forest, and deep learning models. Although these models are powerful, they are often too complex for people to understand by directly inspecting the model parameters. The "black-box" nature of these models limits their use in highly regulated industries such as banking, insurance, and health care. This paper introduces various model-agnostic interpretability techniques available in SAS Viya that enable you to explain and understand machine learning models. Methods include partial dependency (PD) plots, independent conditional expectation (ICE) plots, local interpretable model-agnostic explanations (LIME), and Shapley values. This paper introduces these methods and demonstrates their use in two scenarios: a business-centered modeling task and a health-care modeling task | machine learning, partial dependence, sas viya, feature importance, regression analysis, gradient boosting, logistic regression, response variable, logistic distribution, training set, sample, proc hpsplit, boosting, cas action, macro variable, test set, feature engineering, likelihood function, proc hpforest, neural network, hyperparameter tuning, imputation | source | 26 |
| 141 | How to Maintain Happy SAS Viya Users | Margaret A. Crevar | Today's SAS Viya environments support many concurrent processes, using ever-growing data volumes. To help SAS Viya users remain productive, SAS administrators must ensure that the SAS Viya applications have both sufficient and properly configured compute resources that are continuously monitored. Understanding how all the SAS components work and how they are employed by your users is the first step. The guidance offered in this paper helps SAS administrators configure and tune SAS Viya hardware, operating systems, and infrastructure. This tuning and configuration will keep their SAS Viya applications running at optimal performance and keep their user community happy | sas viya, machine learning, dataset | source | 11 |
| 142 | How to Make Your First Impressive Web Application with Stored Processes and a Web Browser | Philip A. Mason | Many organizations are experiencing the value of applications that can be run from a web browser. We all know that virtually anything is possible using this technology, which will usually consist of some HTML, CSS, and JavaScript running in the browser, with various other software running on a server. SAS has provided the Store d Process Web Application that lets us connect the web browser to the SAS server, which opens up an enormous range of potential applications. In the simplest form, we can use a stored process to prompt the user for some info, run SAS code using that info, and return results to the web browser. From this simple starting point, this paper shows you how to make a more powerful and flexible web application. The emphasis is on showing those who know SAS (and nothing about web technologies) as simply as possible the steps to build a generic web application that they can use to start building their own web applications | proc sql, bootstrap, create table, proc step, proc json, sas library | source | 20 |
| 143 | How To Make Your Reports Fly! The Impact of Visualization Layer Calculations on Performance | Jessica Fraley | SAS Visual Analytics reports at the University of North Carolina Chapel Hill can be written against tables containing 500 GB of data. Reporting in an enterprise-sized environment has a specific set of requirements that were not present in the smaller environments of the past. Not paying attention to these differences can slow report visualization to an unusable crawl. However, there is one easily addressed methodology that gets the most out of your reporting performance. This paper examines the impact of visualization layer calculations (which is common in non-enterprise-sized reporting) on enterprise-sized reports. This paper also goes into the details of how some of our reports had performance increases from minutes to seconds when going against our largest tables | big data, data wrangling | source | 4 |
| 144 | How to Master a Risk Data Vault Using SAS Data Integration Studio | Linus Hjorth | When implementing a new Risk Analysis platform, the bank's choice was to use SAS for both ETL and Credit Scoring. A part of the solution is a Risk Operational Data Store, with the centrepiece of a Data Vault. We will go through the data architecture, data modelling issues, and sum up our ETL experiences when populating and querying the ODS using SAS Data Integration Studio and using SQL Server as the ODS database |
natural key, surrogate key, proc sql, credit scoring, data validation rules, sql server, data warehouse, credit card, raw data, data cleaning | source | 11 |
| 145 | Human Bias in Machine Learning: How Well Do You Really Know Your Model? | Jim Box | Artificial intelligence (AI) and machine learning are going to solve the world's problems. Many people believe that by letting an "objective algorithm" make decisions, bias in the results have been eliminated. Instead of ushering in a utopian era of fair decisions, AI , and machine learning have the potential to exacerbate the impact of human biases. As innovations help with everything from the identification of tumors in lungs to predicting who to hire, it is important to understand whether some groups are being left behind. This talk explores the history of bias in models, discusses how to use SAS Viya to check for bias, and examines different ways of eliminating bias from our models. Furthermore, we look at how advanced model interpretability available in SAS Viya can help end users to better understand model output. Ultimately, the promise of AI and machine learning is still a reality, but human oversight of the modeling process is vital | machine learning, training set, partial dependence, response variable, shap, feature importance, regression analysis | source | 14 |
| 146 | Hybrid Marketing, Campaign Management, and Analytics' Last Mile Using SAS Customer Intelligence 360 | Suneel Grover | The marketing industry has never had greater access to data than it does today. The more we know the customer, the more we understand the essence of customer experience. The more we understand customer experience, the more we can shape it, develop it, and better serve the customer. According to Daniel Newman (Futurum Research) and Wilson Raj (SAS Institute) in the October 2019 research study Experience 2030: "Brands must reinvent their operating models to act in the moment. They need a holistic data and technology strategy that they can individualize at scale, customer journey capabilities that can adapt in real time, and intelligent decisioning to automate the self- reinforcing cycle of tailored experiences. And that's just today. Tomorrow's customer journeys and personalization will be even smarter, more immersive, and more trust- enabling | machine learning, proc hpsplit, customer experience, cluster analysis | source | 32 |
| 147 | IBM Power Systems for SAS Empowers Advanced Analytics | Harry Seifert, Laurent Montaron |
For over 40+ years of partnership between IBM and SAS, clients have been benefiting from the added value brought by IBM's infrastructure platforms to deploy SAS analytics, and now SAS Viya's evolution of modern analytics. IBM Power Systems and IBM Storage empower SAS environments with infrastructure that does not make tradeoffs among performance, cost, and reliability. The unified solution stack, comprising server, storage, and services, reduces the compute time, controls costs, and maximizes resilience of SAS environment with ultra-high bandwidth and highest availability | sas viya, machine learning, sas grid, parallel processing, sas programmer | source | 12 |
| 148 | Identifying "One-Offs": Tuberculosis Genotype Cluster Detection Using the COMPGED Function | Edward Lan | In surveillance and epidemiologic functions of public health departments, SAS software programs are leveraged to identify clusters and outbreaks. Tuberculosis (TB) genotyping is a method used to identify TB patients with closely related strains, indicating potential recent transmission. The genotyping process produces a resulting GENType, comprised of the 15 length spoligotype string and a 24-length mycobacterial interspersed repetitive units (MIRU) string. Each week, the TB Control Program (TBCP) in Los Angeles County (LAC) receives a weekly update of genotyping results of TB isolates. TBCP monitors additions to high priority genotype clusters, i.e. matching GENTypes, and looks for patients with a result string that is one alphanumeric character different than one of the high priority GENTypes. Using a macro %DO loop, the COMPGED function quantifies differences between a patient's TB isolate and a reference library of genotypes for high priority clusters in the County. The quantifiable differences determine exact matches and results that are "one-off" are further investigated by the outbreak team to interrupt TB transmission | macro loop, do loop, excel, proc report, macro variable, proc step | source | 7 |
| 149 | If You Build it Well, They Will Come! Responding to a Growing Analytics Program with SAS Viya | Michael Gibbs, Ronald Freeze |
The objective of this breakout session shows strategies, tips, and lessons learned while implementing SAS Viya for use in a Business Analytics curriculum. The University of Arkansas Walton College of Business, located in Fayetteville, Arkansas, is accredited by the Association to Advance Collegiate Schools of Business (AACSB). SAS has been instrumental in helping to design our business analytics programs and has recognized the value of our program by presenting to graduate and undergraduate students an endorsed certificate upon completion. The initial portion of this session outlines the journey to our first iteration of SAS Viya on a single-node server running all our licensed SAS Viya components. The subsequent portion of this session discusses the growth in the analytics program, which created the need for a far more versatile solution. Details include the process of deploying a six-node distributed SAS Viya solution, including planning, documentation, unforeseen issues and how they were overcome, and other steps that have led to confidence by faculty in teaching SAS Viya as well as a solid learning experience for students | sas viya, analytics platform, sql server | source | 6 |
| 150 | If You Need These OBS and These VARS, Then Drop IF and Keep WHERE | Jayanth Iyengar | Presentation | source | 8 | |
| 151 | Important Performance Considerations when Moving SAS to the Public Cloud | Margaret A. Crevar | When choosing a hardware infrastructure for your SAS applications, you need a solid understanding of all the layers and components of the SAS infrastructure. You also need to not just successfully run the software but to optimize its performance. Finally, you need an administrator to configure and manage the infrastructure. This paper discusses important performance considerations for SAS9 (both SAS Foundation and SAS Grid Manager) when hosted in any of the available public clouds-Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform, and so on. It also provides guidance on how to configure the cloud infrastructure to get the best performance with SAS. Disclaimer: We strongly encourage you to take the advice in this paper and work with your local public cloud teams to make sure the instances you decide to use are available in the closest region and that you understand their costs | source | 9 | |
| 152 | Improving Facility Management through Machine Learning | Christopher Beall | The facility management industry is experiencing an influx of smart technologies and a retiring workforce. There are not enough qualified replacements coming into the industry to offset the retiring engineers and maintenance staff. Facility owners are faced with adopting technology to augment their workforce shortages and are constantly being asked to do more with less. More facilities, more square footage, more complex systems, while being given fewer resources and tighter budgets. Technology, specifically advanced analytics and machine learning, must be adopted and operationalized to augment the current tools and staff. The primary business drivers are enhanced productivity through root cause analysis, energy reduction, and streamlined operations, while establishing and exceeding sustainability goals | root cause analysis, machine learning | source | 10 |
| 153 | Incorporating Auxiliary Information into Your Model Using Bayesian Methods in SAS Econometrics | Matthew Simpson | In addition to data, analysts often have available useful auxiliary information about inputs into their model-for example, knowledge that high prices typically decrease demand or that sunny weather increases outdoor mall foot traffic. If used and incorporated correctly into the analysis, the auxiliary information can significantly improve the quality of the analysis. But this information is often ignored. Bayesian analysis provides a principled means of incorporating this information into the model through the prior distribution, but it does not provide a road map for translating auxiliary information into a useful prior. This paper reviews the basics of Bayesian analysis and provides a framework for turning auxiliary information into prior distributions for parameters in your model by using SAS Econometrics software. It discusses common pitfalls and gives several examples of how to use the framework | standard deviation, regression analysis, proc step | source | 20 |
| 154 | Interpretation Methods for Black Box Machine Learning Methods in Insurance Rating-Type Applications | Gabe Taylor, Ray Wright |
Traditional Generalized Linear Models (GLMs) are often favored in the insurance realm for rating due to their interpretation simplicity and intuitive distributional assumptions. This is partly driven by insurance regulation needs and partly based off customers' demand of explanation for their rates. In insurance rating, there is often a need to provide credit reason codes and occasionally customer feedback in determining the factors adversely impacting policyholders' individual premium. Machine learning models (sometimes referred as black-box methods), on the other hand, often lack transparency and interpretability but have powerful predicting potential. Actuarially, modelers have to balance between accurate confident pricing and model interpretability, for both the collective customer population and individual customers | source | 17 | |
| 155 | Introducing PROC SIMSYSTEM for Systematic Nonnormal Simulation | Bucky Ransdell, Randy Tobias |
Simulation is a key element in how decisions are made in a wide variety of fields, from finance and insurance to engineering and statistics. Analysts use simulation to estimate the probability that a business process will succeed, that an insurance portfolio will fail, or that a statistical method will control Type I error. Often, the base case that is used to devise such processes depends on assuming that random variation is normal (Gaussian). Normal variation is common and is often easier to compute with. But it is still an assumption, so robust simulation studies should explore what happens when that assumption is violated. The SIMSYSTEM procedure, new in SAS Visual Statistics 8.5, enables you to simulate random variation from a wide class of distributions. Y ou can use PROC SIMSYSTEM to generate random variates with any possible values for skewness and kurtosis, which define the asymmetry and tail behavior of the underlying distribution |
standard deviation, proc step | source | 17 |
| 156 | Introducing the GAMSELECT Procedure for Generalized Additive Model Selection | Michael Lamm, Weijie Cai |
Model selection is an important area in statistical learning. Both SAS/STAT software and SAS Visual Statistics software provide a rich set of tools for performing model selection over linear models, generalized linear models, and Cox proportional hazards models. With these tools, you can build parsimonious predictive models by constructing linear or fixed nonlinear effects to describe the dependency structure. But what if the dependency structure is nonlinear and the nonlinearity is unknown? And what if covariates are nonlinearly correlated? The new GAMSELECT procedure, available in SAS Visual Statistics, addresses these questions by using spline terms to approximate the nonlinear dependency, then selecting important variables in appropriate nonlinear transformations by using the boosting method or the shrinkage method. The procedure builds models for response variables in the exponential family, so that you can use it for continuous, count, or binary responses |
proc step, data cleaning, proc gam, feature selection, likelihood function, output, proc freq | source | 25 |
| 157 | Introduction to SAS/ACCESS Interface to Google BigQuery | Joel Odom | Google BigQuery is a service running on the Google Cloud Platform that facilitates analysis of massive data sets working in tandem with Google Cloud Storage. SAS/ACCESS Interface to Google BigQuery enables SAS to take advantage of this exciting technology . This paper describes Google BigQuery and discusses how it is different from other databases that you might have used in the past. Using examples, we discuss the following topics: how to get started connecting your SAS software to Google BigQuery; tricks to get the most out of SAS/ACCESS Interface to Google BigQuery; how to effectively move data into and out of Google BigQuery; and potential gotchas (or idiosyncrasies) of Google BigQuery. This paper uses an example-driven approach to explore these topics. Using the examples provided, you can apply what you learn from this paper to your environment |
google bigquery, proc sql, proc step, sql pass-through, libname statement, cas, create table, implicit sql pass-through, sas viya, connection string, reference data | source | 9 |
| 158 | Is it possible to reduce smoking by increasing taxes? An analysis on Danish data using SAS software | Anders Milhøj | In Denmark all ideas to reduce smoking are discussed. It is commonly accepted that increasing prices is an efficient tool to reach this goal. In a Danish context taxes are frequently used for such purposes as we already have a heavy taxation of all tobacco products as we also tax alcohol, fuel and even for some years ago and extra taxation on the content of fat in goods like meat, cheese, chocolate etc. The question is however whether it is true that smoking could be reduced by increasing taxes. This paper presents several statistical time series analyses by several procedures - VARMAX, X12, ARIMA and UCM - in the SAS ETS software package to answer this question. The data is series for monthly and yearly sales of cigarettes combined with series for the price of cigarettes and the overall consumer price index together with detailed information on previous changes in the consumer tax for cigarettes. The results indicate a price elasticity of at least 0.6, but the chock effect of a sudden price increase by an increasing tax fades out rater fast | regression analysis, estimate, maximum likelihood estimation, price elasticity, proc step, time series analysis, proc ucm, likelihood function, proc calis, proc varmax | source | 13 |
| 159 | It's All about the Base-Procedures, Part 2 | Jane Eslinger | "It's All about the Base-Procedures" (a SAS Global Forum 2019 paper) explored the strengths and challenges of commonly used Base SAS procedures. It also compared each procedure to others that could accomplish similar tasks. This paper takes the compari son further, focusing on the FREQ, MEANS, TABULATE, REPORT, PRINT, and SQL procedures. As a programmer, whether novice or advanced, it is helpful to know when to choose which procedure. The first paper provided best-use cases, and this paper takes it a step further in its discussion of when to choose one procedure over another. It also provides example code to demonstrate how to get the most out of the procedure that you choose | proc step, proc tabulate, proc report, proc sql, proc freq, proc print, proc means, class variable, by group processing, proc format, sas base, group by, macro variable | source | 20 |
| 160 | Joinless Join: The Impossible Dream Come True Using SAS Enterprise Guide, PROC SQL, and DATA Step | Ronda Phelps | SAS Enterprise Guide and Base SAS can easily combine data from tables or data sets by using PROC SQL Join to match on like columns or by using a DATA STEP Merge to match on the same variable name. However, what do you do when tables or data sets do not contain like columns or the same variable name and a Join or Merge cannot be used? We invite you to attend our exciting Joinless Join session where we will empower you to expand the power of SAS Enterprise Guide and Base SAS in new ways by creatively overcoming the limits of a standard Join or Merge. You will learn how to design a Joinless Join based upon dependencies, indirect relationships, or no relationships at all between the tables or data sets using SAS Enterprise Guide and Base SAS PROC SQL and DATA STEP. In addition, we will highlight how to use a Joinless Join to prepare unrelated joinless data to be utilized by ODS and PROC REPORT in creating a PDF. Come experience the power and versatility of the Joinless Join to greatly expand your data transformation and analysis toolkit. We look forward to introducing you to the surprising paradox of the Joinless Join |
proc sql, proc step, sas enterprise guide, sas base, proc report, inner join, data cleaning, where statement | source | 17 |
| 161 | Language Lessons for Data-Driven Decisions: Achieving Data Literacy | Emma Warrillow | As they collect more and more data, organizations everywhere are pushing for more data- driven decision-making. They are hiring data scientists, data engineers, and data analysts to help them harness that data and reach this goal. However, while there is no question that technical programming and statistical skills are critical inputs in this endeavor, they are not enough. When you travel abroad, a phrase book or Google Translate can help you navigate-as can learning a few key phrases-but you would not confuse this with knowing the language. The language of data literacy is no different. Speaking the language of data is a skill you need to cultivate like any other. Whether you are the traveler (aka an analytical or data professional) trying to make yourself understood, or the person trying to help those visiting, learning the language is critical. And it is especially so when you are trying to make key business decisions; getting it wrong will | data science, data visualization | source | 8 |
| 162 | Lessons Learned from Managing SAS Viya at Scale in the Cloud | Jelle Daemen, Tom Dogger |
Starting in 2015, SaasNow has been managing SAS products in a private cloud in a unique monthly, fully self-service cloud model. Starting with SAS vApp technology, SaasNow was one of the first providers to offer a true cloud experience for SAS Visual Analytics. With the release of SAS Viya in 2016, SaasNow has been able to support many more SAS products, including SAS Visual Data Mining and Machine Learning. Currently, all SAS Viya products and bundles can be automatically deployed on SaasNow within a few hours. Over the years, we have deployed and managed hundreds of environments, ranging from small four core demo environments, to multi-server production setups of up to 128 cores with a wide variety of customer-specific customizations and integrations, like customized authentication (Kerberos or SSO integrations) and external data source connections (SQL and Apache Hadoop) | sas viya, machine learning | source | 6 |
| 163 | Let's See Further than One Observation: Applying Hash Objects for Typical Clinical Programming Tasks | Valeriia Oreshko | It is commonly known that SAS executes the DATA STEP for each observation iteratively. A common method that is used to compare the current record to data from previous records is to sort using first/last automatic variables and the retain statement. This method is often time-consuming, requires the creation of additional data steps and variables, additional sorting and is not always intuitive or easy to update. This paper will demonstrate an alternative approach to typical clinical programming through hash objects: defining baseline, detecting of repeated adverse events, flagging of all records from an interval and fuzzy merging. Hash objects allow for the loading of a whole dataset into memory and for the analyzing of combinations of observations. This paper will explain techniques on how to navigate through the hash table and provide a comparison of execution speed between code containing hash objects and traditional code |
hash table, adverse events, data step, clinical programming, hash iterator, out= | source | 10 |
| 164 | Like, Learn to Love SAS Like | Louise S. Hadden | How do I like SAS? Let me count the ways.... There are numerous instances where LIKE or LIKE statements can be used in SAS - and all of them are useful. This paper will walk through such uses of LIKE as: searches and joins with that smooth LIKE operator (and the NOT LIKE operator); the SOUNDS LIKE operator; using the LIKE condition to perform pattern-matching and create variables in PROC SQL; and PROC SQL CREATE TABLE LIKE |
proc sql, proc step, create table, where statement | source | 7 |
| 165 | Look Ma, No Hands: How to Train Your Dragon for SAS | Julianna Langston, Elizabeth Langston |
Have you ever experienced pain in your hands while using a keyboard? Have you wondered how to write SAS code without using your hands? Well, you will love this paper. After a SAS accessibility developer received a repetitive strain injury (RSI) diagnosis, she started using Dragon NaturallySpeaking (a speech-to-text product) to write SAS code hands-free. In this presentation, she describes tips for getting started with Dragon, demonstrates techniques for training Dragon to understand SAS language vocabulary, and shares a case study for using Dragon with her favorite SAS programming client | proc step, proc sql | source | 7 |
| 166 | Machine Learning Approach to Combat the Opioid Epidemic | Navjot Kaur | The ever-increasing practice of using opioids or street drugs in the United States has caused the rates of mortality from drug abuse to hit the roof. Although prescribed opioids are mainly used for pain relief, there is a prevalence of illegal use of opioids and the likelihood of becoming dependent on an opioid long-term spike after just five days of use. Unfortunately, the epidemic has affected almost every age group in every U.S. population, but the opioid addiction has been disproportionately affecting older adults living in rural America. The impact is so serious that every day more than 130 Americans die from an opioid overdose. In this study, we are going to examine whether there is any correlation between the prescription of opioids and deaths from overdose. Further, our objective is to find correlations among risk factors and identify statistically significant factors in individuals or groups who are susceptible to opioid abuse | correlation, response variable, sample, predictor variable, pearson correlation, likelihood function, proc means, data mining, f1 score, insurer, regression analysis, data cleaning | source | 15 |
| 167 | Machine Learning Data Analysis for the ERP Adoption and Enterprise Performance with SAS Enterprise Miner and Python Scikit-learn | Sunjip Yim, Ho-Chang Chae |
The study on the predictive modeling for the adoption of Enterprise Resource Planning (ERP) with business performance has been lacking so far in large. Thus, we answered for this question with massive time-series firm-level data collected by South Korea Statistics agency. With more than 11,400 Korean companies' data with 256 variables in each year, we modeled twenty-four SAS Enterprise Miner nodes and wrote eight Python Scikit-learn programming codes to find the best predictive models for the ERP adoption by firms. During nine years of the survey period, we selected the years of 2006 (the start year), 2010 (the middle year), and 2014 (the end year). | source | 9 | |
| 168 | Make Beautiful Vertically Scrolling Dashboards in SAS Visual Analytics | Stu Sztukowski | Tabs are a natural way of creating content-rich business intelligence dashboards. They visually separate distinct ideas and concepts, typically flowing from left-to-right to tell a story hidden in gigabytes of tables full of numbers, words, codes, and acronyms. As users ask for additional views and insights into their data, designers may start finding themselves pressed for space. Some users may want to add large crosstabs, while others may want detailed bar charts with 25 categories. Before SAS Viya, designers had the following options: 1. Make a new tab with additional visualizations 2. Try resizing all graphs to support the new graph 3. Move visualizations into stacked containers Adding too many tabs and densely-packed stacked containers can make for a difficult and frustrating user experience. With SAS Viya and SAS Visual Analytics 7.5, designers can increase their dashboard real estate by creating vertically scrolling dashboards at the click of a button | business intelligence, bar chart | source | 17 |
| 169 | Make Your DO Loop More Efficient | G Liu | In certain cases, a DO loop does not need to run all of its iterations in order to obtain the correct result. This paper will examine three examples to illustrate when we can or cannot make the DO loop more efficient with no loss of accuracy, through the use of the LEAVE statement | do loop, leave, missing value, if-then-else | source | 4 |
| 170 | Making Administration Easier by Scripting the SAS Viya Command-Line Interfaces | James Holman | SAS Viya offers a set of rich command-line interfaces (CLIs) to directly interact with SAS Viya without using a graphical interface. This paper describes how these CLIs can be scripted and chained together to accomplish administrative tasks. Examples include backi ng up and then changing the logging levels for SAS Cloud Analytic Services (CAS), auditing failed logins, updating themes across a set of SAS Viya reports, batch creating caslibs and adding access controls to them, as well as many other tasks. All of these tasks can be done remotely as well, without having to physically be on a SAS Viya system | sas viya, cas, access control, ods json, sas administrator, sample | source | 18 |
| 171 | Making Data Actionable: From Concept to Reality | Joseph W. Perez | Anyone can make a pretty bar graph, but can you make sound decisions based on what your development staff currently provides? How do you turn a flashy concept into an actionable visualization? Can you see the end result? Will your ideas become reality? Do you have the vision and drive to figure out how to get from today to tomorrow before it becomes yesterday? American mathematician John Tukey once said, "The greatest value of a picture is when it forces us to notice what we never expected to see." What value do you see in your data? And what ideas do you have when you see it? Learn how you can capitalize on your ideas and turn them into reality by blending the internal with the external, leveraging them into a cohesive strategy for both the short term AND the long term. In this session, see the five "Stages of the Spectrum" in action while discovering the difference between impact and influence, and how that plays into making data actionable | data visualization, data storytelling | source | 11 |
| 172 | Making Life Easier on the SAS End: Best Practices for | Julie Plano, Keli Sorrentino |
Data reports can be produced using SAS to convey the progress and potential needs of an ongoing study to research staff and investigators. It is critical this information is accurate and timely. Tailoring these reports to your team's needs allows for maximum communication. It is imperative to begin by knowing your data, the structure, variable formats, and meanings. What you report is highly dependent on the audience; this paper reviews basic concepts of report production. We cover the most requested types of information for current field studies : cumulative counts over time, frequencies, percentages, missing values, descriptive statistics , and plots. Presentation can be just as important as content. Creating visually appealing output can be done with a few extra lines of code. Including titles, formatting and system optio ns as well as utilizing the SAS output delivery system (ODS) can improve appearance and clarity |
proc step, proc freq, missing value, proc contents, proc sgplot, output delivery system, out= | source | 20 |
| 173 | Making Long Data Wide with a Flexible Macro | Stephanie R. Thompson | When you need to make a wide dataset from multiple sources where the data are long instead, this macro approach is for you. This macro has several variations that allow for customization and the addition of dynamic prefixes to the new variable names. The purpose of the macro was to take period - based data and put it into a format to be used for predictive modeling. Over 10 different data sources were used and this macro was easily applied to each. See how macros can make your life easier! | proc transpose, proc step, sample, macro variable | source | 13 |
| 174 | Managers: Adapt the SDPDC to Plan Employees' Professional Development | Kelly D. Smith | Presentation | source | 8 | |
| 175 | Managing "IDLE" Grid-Launched SAS Workspace Servers | Piyush K. Singh | In SAS Grid Computing, jobs may be running for hours or even days. It can be difficult to determine if these jobs are active or idle. Batch jobs like those submitted using the SAS Grid Manager Client Utility have their status accurately reflected by the Load Sharing Facility (LSF) as batch jobs do not wait for input during execution. Grid-Launched Workspace Servers however, will appear in a RUNNING status to LSF and its associated commands such as bjobs, despite sitting idle waiting on a user's input. The code and techniques given in this paper will describe how to check for how long a Grid- Launched Workspace Server has been "idle", waiting on user input. Based on the idle time of the SAS session, a decision can be made to kill the respective LSF job id to free up the job slots from SAS Grid Platform. This technique can be even more helpful for the user who forgets to close their SAS Enterprise Guide session before leaving for the day, keeping a job slot busy throughout the night | sas grid, sas enterprise guide, proc step, batch job | source | 11 |
| 176 | Measuring Analytic Performance in Financial Terms for Executives | Tho H. Nguyen | Data Scientists are by their very nature good at understanding the details of information and finding the connections between often unique and unrelated data points. The value of what they do is in high demand and often goes unfulfilled because requests overwhelm their ability to fulfill everyone's desires. New analytic tools that support greater speed and performance by eliminating data movement, simplifying testing, and streamlining the move to production can provide relief for this problem but building the business case for new analytic tools is not as easy as building the business case for a fraud or churn solution. As a result, Data Scientists need to be able to communicate why an investment in new analytics tools is necessary in financial terms that executives will understand and approve. This paper focuses on enabling you to do this | data science, in-database processing | source | 5 |
| 177 | Medical Image Analyses in SAS Viya with Applications in Automatic Tissue Morphometry in the Clinic | Courtney Ambrozic | Imaging and image analytics are indispensable tools in clinical medicine today. Among the various metrics that doctors routinely derive from images, measures of the morphology of tissue structures, including their shape and size, are of key significance. Quantifying tissue morphology and linking those quantities to other clinical data enable clinicians to diagnose diseases and plan treatment strategies. Image segmentation, which classifies image pixels into regions of interest, is an important step in such tissue morphology quantification. However, common segmentation methods involve a process that is either fully or partially manual. Accordingly, these methods can be extremely arduous when you process very large amounts of data. This paper illustrates how to build end-to-end pipelines for automatically deriving clinically significant tissue morphology metrics from raw medical images by using powerful tools that were introduced in SAS Viya 3.5 | test set, data cleaning, validation set, histogram | source | 13 |
| 178 | Metrics and Medical Writing - We Don't Just Count Words | Daniel Augusto Muriel Ramirez, Dominic De Bellis |
This presentation introduces the use of various document-specific metrics in support of business objectives and productivity. Topics address within-document metrics as well as group and departmental approaches to quantifying the value added to the drug development process through medical writing expertise. The manuscript provided here is the corrected version | clinical trial, csr, resource allocation, quality control, clinical data | source | 12 |
| 179 | Migrating from SAS 9.4 to SAS Viya 3.5 | Mark L. Schneider, Susan Pearsall |
SAS Viya brings with it a wealth of new analytic and data management capabilities, as well as a resilient, scalable, and open architecture. Users of SAS 9.4 need an easy path to migrate their workloads to SAS Viya in order to leverage these platform capabilities. In this paper, we discuss your options for adopting SAS Viya to complement, and in some cases replace, a SAS 9.4 environment. The options fall into three general categories: interoperability, content promotion, and replacement. We discuss how SAS 9.4 clients can interoperate with SAS Viya's analytic servers. Further, we describe how you can promote content like library definitions and SAS Visual Analytics reports from SAS 9.4 to SAS Viya, all the while taking advantage of backward compatibility as you bring SAS programs forward. Finally, we cover scenarios in which you can completely replace SAS 9.4 work loads with equivalent SAS Viya product support. Along the way, we highlight SAS tools that can ease the migration process | sas viya, cas, sas 9, sas management console, access control, sas programming, libname statement, sas enterprise guide, spk file | source | 14 |
| 180 | Minimizing Environmental Disturbances: Applying Leave No Trace Principles to SAS Programming | Carl Sommer | It is essential that all components behave appropriately when integrating multiple programs and macros. Although they might have worked fine in their original context, issues can come up in a reuse scenario as the result of either expectations of certain state conditions in the SAS environment that are suddenly not in effect, or because some other program has altered the state of the SAS environment. The "Leave No Trace Seven Principles", which focus on outdoor ethics, behavior, and cleanliness, can also be applied to SAS programming. These principles help establish a coding standard that contributes positively to the business continuum, to a business process modernization, or to a migration strategy within a customer environment. This paper presents this rubric, as well as a macro that can be used to create and compare environmental snapshots, in order to help you quickly identify, isolate, and understand the before and after state of your environment | macro variable, proc step, proc sql, proc compare, sas log, dictionary tables, macro quoting function, macro definition, %if-%then-%else | source | 11 |
| 181 | Mixed-Effects Models and Complex Survey Data with the GLIMMIX Procedure | L. Grisell Diaz-Ramirez, Bocheng Jing |
Longitudinal data with repeated measures are of great interest in clinical and epidemiology research and are often analyzed using a mixed-effects model. These data are frequently collected through complex survey methods. Thus , the survey features probability weights, clusters, and strata should be included in the analysis to obtain accurate population-based estimates. Fitting mixed models with survey data is still an area of active research, and currently there is no survey analysis procedure in SAS for mixed models. As a workaround, researchers can use some of the options available in the SAS GLIMMIX procedure. However, when there are many strata or clusters, the model tends to be computationally intensive and complicated to optimize. In this paper, we explore different methods to account for survey features in mixed models using PROC GLIMMIX |
fixed effects, covariance, random intercept, proc mixed, sample, estimate, sample size, correlation, proc glimmix, likelihood function, random slope, confidence interval, longitudinal data, cpu time, repeated measures, proc step | source | 10 |
| 182 | Model Governance for AI: Rethinking Our Approach to Validating and Using Black Box Models | Naeem Siddiqi | Model governance, including model validation, is a well-practiced discipline. The traditional approaches have served the industry well over the years. These typically involve activities such as backtesting, performing methodology and documentation reviews, benchmarking models, and performing qualitative assessments. A central part of this governance process is in model explainability, where knowing the exact variables and their weights is considered key. In the emerging world of artificial intelligence (AI) and machine learning models, it is often very difficult to understand model components, and traditional approaches might not be ideal. Such new models, known as black box models, require a re-think on how we approach model governance. This paper proposes a new framework for model governance that does not depend on knowing specific details about what is inside the model | model governance, data cleaning, model explainability, model training, model validation, sample, partial dependence | source | 5 |
| 183 | Model-Based Risk-Adjustment in Clinical Outcome Research | Jiming Fang, Feng Qiu |
To compare clinical outcome across patient groups or health providers, we need to account for the variations from patient-level demographic and clinical treatments. Model-based risk- adjustment approach provides strategy to control these variations, which is commonly used in hospital report CARDS. In the present paper, we will focus on the concept of the model - based risk-adjusted outcomes using different regression models, including multilevel hierarchical model, according to the type of outcomes |
regression analysis, proc mixed, logistic distribution, logistic regression, poisson distribution, proc step, binomial distribution, proc phreg, poisson regression | source | 10 |
| 184 | Modeling of Between - and Within-Subject Variances using Mixed Effects Location Scale (MELS) Models | Qianheng Ma, Donald Hedeker |
Intensive longitudinal data are increasingly obtained in health studies to examine subjective experiences within changing environmental contexts. Such studies often utilize ecological momentary assessment (EMA) and/or experience sampling methods to obtain up to 30 or 40 observations for each subject within a period of a week or so. In this paper, we focus on data from an adolescent smoking study using ecological momentary assessment in which there was interest in examining mood variation associated with smoking. We describe the mixed-effects location scale (MELS) model which allows covariates to influence both the within-subject (WS) and between-subject (BS) variances, in addition to their influence on the mean. The model also includes a subject-level random effect to the within-subject variance specification. This permits subjects to have influence on the mean, or location, and variability, or (square of the) scale, of how soon the first smoking episode happened after wake-up | proc step, proc mixed, normal distribution, likelihood function, numeric functions, sample, fixed effects, longitudinal data, response variable, covariance, markov chain, maximum likelihood estimation, monte carlo | source | 14 |
| 185 | Modelling Imbalanced Classes | Humphrey Brydon | In this study separate sampling was applied to various modelling procedures to assist in the identification of the most important variables describing smartphone users who are security compliant. Initial analysis of the data found that only 7% of smartphone users reported applying security measures to protect their phones and/or their personal information stored on their devices. Due to the class imbalance in the target variable, predictive modelling procedures failed to produce accurate models. Separate sampling proportions were introduced to establish if classification accuracy could be improved. This study tested target class over-sampling ratios of 20%, 30%, 40% and 50% and compared the results of the models fitted on these data sets to those fitted on the original data where no separate sampling was applied. Models fitted included: decision trees, 5-fold cross-validated decision trees, logistic regression, neural networks and gradient boosted decision trees | proc hpsplit, logistic regression, neural network, logistic distribution, regression analysis, prior probability | source | 6 |
| 186 | Modernize your SAS Visual Analytics Environment | Gregor Herrmann | In recent releases of SAS Visual Analytics on SAS Viya, many reporting and dashboarding capabilities have been introduced that make the move to the current version of SAS Visual Analytics very attractive. Because the underlying architecture has changed, this move has to be prepared carefully. We take a closer look not only at how you can promote SAS Visual Analytics reports from earlier releases to the current version, but also at which other object types can be converted to SAS Viya. These include SAS Visual Data Builder queries, information maps, SAS Stored Processes, and SAS OLAP cubes. After attending this session, you should have a comprehensive overview of which parts of your SAS 9 applications are ready to be promoted to SAS Visual Analytics on SAS Viya | sas viya, sas 9, sas management console, sas base, user-defined format, spk file | source | 9 |
| 187 | Modernizing Credit Risk Analytics: From Risk Management for Banking into Stress Testing | Christian Macaro | Due to the continuous evolution of regulations issued around the world1 and the constant advancements in methodology, the analysis of credit, market, and operational risk are complex topics that require tools to be updated during development, implementation, and even during the evaluation p hase itself. These tools need to be customized to the specific needs of a variety of financial institutions and, yet they need to leverage a common platform containing common components needed for the analysis. SAS Solution for Stress Testing is a powerful and highly customizable environment where several areas of interest (expected credit loss, risk weighted asset, economic capital, and more) can be evaluated. This paper presents some of the details about the evolution of credit risk analytics from SAS Risk Management for Banking into SAS Solution for Stress Testing | credit risk, stress testing | source | 9 |
| 188 | Monitor Assignment for Students with Disabilities with SAS: Boston Public Schools | Angela Zhang | Presentation | source | 7 | |
| 189 | MOVIE REVIEWS: TO READ OR NOT TO READ! Spoiler Detection with Applied Machine Learning | Sreejita Biswas | Would you still watch The Avengers if you found out that Iron Man dies at the end? Would you watch the final season of Game of Thrones if somebody told you Bran was going to be the king of the North? Highly unlikely! Given that many invest a great deal of time and emotions into movies and T.V. shows, having the experience ruined by, otherwise harmless, scrolling through Facebook can be frustrating. Having always been a movie lover myself, I have experienced first-hand how spoilers can take away from the enjoyment of a movie. Along with being detrimental to viewer experience, these revelations may also hurt the entertainment industry by causing people to lose interest in a particular film, resulting in the loss of revenue for the filmmakers. This research is aimed at spotting spoilers in online movie reviews in order to protect the reader's movie watching experience. To this end, the paper will explore the likelihood of a spoiler based on the movie's genre, rating, and year of release | proc freq, f1 score | source | 11 |
| 190 | Moving from Messy Data to a Clean Analytic Dataset: Common Data Management Techniques using SAS | Bethany A. Bell | Despite the amount of quantitative research that exists in the social and behavioral sciences, many graduate programs do not offer classes focused on the multitude of steps necessary to manage quantitative datasets. Instead, this skill is often learned through trial and error with the beginning SAS user having to use multiple resources, including, but not limited to the plethora of proceedings papers from SAS Global Forum as well as regional users' groups as well as other SAS friendly resources such as UCLA's Institute for Research and Digital Education and SAS publications. Although these resources are incredibly useful when a SAS user knows what procedure he/she needs, they are less useful for the novice analyst who does not know where to begin. The focus of this paper is to help guide the novice user through common data management techniques to transform raw, messy data into an analytic dataset | binary variable, proc step, proc freq, temporary dataset, missing value, proc contents, factor loading, sample, proc corr, factor analysis, array functions, nominal variable, interval variable, data governance, sas syntax, proc means, regression analysis | source | 27 |
| 191 | Multilingual Sentiment Analysis: An RNN-Based Framework for Limited Data | Ethem Can, Aysu Ezen-Can |
Sentiment analysis is a widely studied natural language processing task, whose goal is to determine users' opinions, emotions, and evaluations of a product, entity, or service that they review. One of the biggest challenges for sentiment analysis is that it is highly language- dependent. Word embeddings, sentiment lexicons, and even annotated data are language- specific. Furthermore, optimizing models for each language is very time-consuming and labor- intensive, especially for recurrent neural network (RNN) models. From a resource perspective, it is very challenging to collect data for different languages | test set, neural network, deep learning | source | 7 |
| 192 | Multinomial vs. Ordinal. Does model selection make a difference? for SAS Global Forum 2020 | Corey Leadbeater | Regression modeling, a foundational component of data analysis and machine learning, is one of the most highly sought-after skills by employers seeking new data scientists.1 While most data science curricula tend to include regression modeling techniques, the conceptual nuances between theoretical and practical applications can be nebulous. In this paper we use SAS 9.4 and the 2016 Monitoring Futures Survey to demonstrate the utility and effects of model selection on context. That is, the ability of the model to properly communicate the users intended information. A cross-sectional secondary analysis using Pearson's Chi-Square test statistic for independence was conducted on the 2016 Monitoring Futures Study dataset to determine the association between "behavior risk" (v7335) and each of the three predictor variables, "parental communication" (v7254), "time spent alone after school" (loner) and student letter grades (v7221) | regression analysis, feature selection, odds ratio, logistic regression, logistic distribution, response variable, chi-square test, missing value, likelihood function, class variable, maximum likelihood estimation, ordinal logistic regression, predictor variable, proc freq | source | 15 |
| 193 | My Sharky Secrets for Telling Fabulous Data Stories | Patricia S. Aanderud, Jaime D'Agord |
One of the hardest things about presenting data is capturing people's attention! Numbers might prove your point, but how do you get others to care about them? Simple-use data storytelling! Data storytelling enables you to mix narratives with data so that you can maximize your impact. Data storytelling is a popular way to present data. However, many data professionals don't understand the value of using these methods when presenting data. By keeping the message focused, considering the audience, and using a convincing narrative, data storytellers engage and move listeners to act. This powerful technique will help you clearly communicate the business insights found in your data. And, in doing so, you can enable decision-making and create a lasting, positive impact on your organization. Let's make you a data storyteller today! | data storytelling, data visualization | source | 19 |
| 194 | Network Pattern Match for Identifying Fraud with SAS Visual Investigator | James Morris, Nicholas Ablitt |
Network analytics plays an increasingly important part in detecting and investigating criminal activity and regulatory compliance issues. SAS Visual Investigator provides a user interface that, among other things, facilitates the investigation of network activity. These networks are often a combination of relationships formed through common entities referred to in the data (for example, people, locations, and so on) as well as events that tie these entities together. If complex organized activity is detected, a key question an investigator might have is, "Does this activity occur elsewhere in my data?". SAS provides the NETWORK procedure to analyze graph data, and at SAS Global Forum 2019, the SAS Cloud Analytic Services (CAS) action patternMatch was introduced, which executes graph queries. Its functionality enables you to search copies of a query graph within a larger graph, with the option of respecting node or link attributes (or both) |
machine learning, cas action, insurance claims, auto insurance, network analytics, application fraud | source | 17 |
| 195 | Neural-Network Based Forecasting Strategies in SAS Viya | Steven C. Mills | Recent literature indicates that hybrids of machine learning and classical time series models are among the top contenders in accurately forecasting the future. Classical linear models are parsimonious and often perform well, but they are unable to capture nonlinear relationships in the data. On the other hand, machine learning models such as neural networks (NNs) are very good at modeling nonlinear effects. Knowing when and how to use machine learning models might seem difficult, but these decisions can be distilled down to best practices that any analyst can use with little experience. This paper discusses several NN-based modeling strategies available in SAS Visual Forecasting software and the important factors to consider in choosing and training a model. The discussion includes key features of the data that inform the decision to use machine learning models, feature generation options to augment the training process, and best practices to fit a robust model | neural network, time series analysis, machine learning, missing value, predictor variable, sample, data cleaning, by group processing, binary variable, training set, trend, feature extraction, response variable, forecast horizon, network optimization, histogram, model training | source | 14 |
| 196 | Next Steps: Important Considerations for Moving Your Data and Formats into CAS | Kevin Russell | Now that your SAS Cloud Analytic Services (CAS) environment is available, you are ready to start moving your SAS data sets and formats from SAS 9.4 to CAS. However, there are a number of considerations to think about before, during, and after you move your data and formats. This paper, written for the beginner CAS user, focuses on three main considerations in the context of examples that illustrate how to load SAS data sets and formats. The first consideration that is discussed is the likely change in character encoding from LATIN1 or WLATIN1 to UTF-8 and how to prevent possible truncation of data. Next, the advantages of loading data into CAS in parallel versus serially is discussed. After you load your data, you might see that the size of the loaded table is larger than the source data set. This paper explains how the increase in size affects memory and how to reduce the size of a loaded table | proc step, dataset, character variable, numeric variable, cpu time, proc format, big data, sas viya, proc contents, format statement, proc options | source | 14 |
| 197 | NLP with BERT: Sentiment Analysis Using SAS Deep Learning and DLPy | Doug Cairns, Xiangxiang Meng |
A revolution is taking place in natural language processing (NLP) as a result of two ideas. The first idea is that pretraining a deep neural network as a language model is a good starting point for a range of NLP tasks. These networks can be augmented (layers can be added or dropped) and then fine-tuned with transfer learning for specific NLP tasks. The second idea involves a paradigm shift away from traditional recurrent neural networks (RNNs) and toward deep neural networks based on Transformer building blocks. One architecture that embodies these ideas is Bidirectional Encoder Representations from Transformers (BERT). BERT and its variants have been at or near the top of the leaderboard for many traditional NLP tasks, such as the general language understanding evaluation (GLUE) benchmarks. This paper provides an overview of BERT and shows how you can create your own BERT model by using SAS Deep Learning and the SAS DLPy Python package | neural network, deep learning | source | 11 |
| 198 | Noninvasive Beehive Monitoring through Acoustic Data Using SAS Event Stream Processing and SAS Viya | Yuwei Liao | Honey bees are critical pollinators; their demise would probably be disastrous for human beings. Thus, maintaining healthy bee colonies is vitally important. Beekeepers usually monitor the status of beehives by performing manual examinations in order to check whether the queen bee is missing or to look for any other potential problems. But not only are manual hive inspections time-consuming, they are also disruptive to the colony. Research in computational bioacoustics has discovered connections between the sounds in the hive and different behaviors of bees. Yet, to date, no automatic acoustic monitoring system has been completely successful. Acoustic data from beehives are messy. With many thousands of bees performing an array of time-varying tasks, and with external sounds from birds, crickets, cars, trains, and other sources, it is difficult to make the link between sound recorded in a beehive and hive health | proc freq, sas viya, autoregressive model, stream processing, real-time processing, proc princomp, frequency analysis, model training, sample | source | 24 |
| 199 | Obtaining National Readmission Estimates: Examining Readmission Rates in the United States | Patrick Karabon | In the past several years, there has been increased focus from healthcare stakeholders in reducing the rate of inpatient hospital readmissions. Policies, including the Hospital Readmissions and Reduction Program (HRRP) from the Centers of Medicare and Medicaid Services (CMS), have placed financial penalties on hospitals with excessive readmission rates. The Nationwide Readmissions Database (NRD) is a comprehensive nationwide database that enables users to obtain population-based national estimates of readmissions. With a claims-based data structure, this database allows for analysis of readmission trends, analysis of primary cause of readmission, or even analysis of time to readmission. This E-Poster shows how to obtain population-based estimates and trends by using both PROC SQL and the SAS/STAT Survey Sampling and Analysis Procedures |
proc step, chi-square test, proc sql, proc surveymeans, logistic regression, proc surveylogistic, proc calis, sample, nominal variable, trend analysis, macro variable, logistic distribution, regression analysis | source | 9 |
| 200 | ODS: It's Not Just for Tables Anymore. Using Formatted Text and Lists in Your Reports | Scott Huntley | You're used to seeing tables in your ODS reports, but maybe you need that extra something to drive your results home. Did you know that you can add formatted text and lists to your report? With PROC ODSLIST and PROC ODSTEXT, you can add richer descriptions to your reports, or even generate data-driven lists and blocks of text. The examples in this paper demonstrate several ways to display your output. Whether you need to create a form letter or an infographic, PROC ODSLIST and PROC ODSTEXT can help you generate the report that you want |
proc step, dataset | source | 11 |
| 201 | Open Source Python & R Lang on our SAS Shared Grid (SSG) | Thomas Ball | In November 2018 the requirement was received to enable Open Source Python and R Lang on our SAS Shared Grid Environment to provide enhanced functionality for our Data Science community. Our platform is one of the Biggest SAS Environment's in the world. In May 2019 the functionality was enabled on our SAS platform following successful User Acceptance Testing. This functionality along with SAS' interoperability with other data source types has provided new opportunities for our business users | data science, sas model manager, data mining | source | 5 |
| 202 | Open-Source Model Management with SAS Model Manager | Glenn Clingroth | Open-source models that are developed in Python, R, TensorFlow, and so on, are increasingly important to organizations that produce and deploy analytical and machine learning models. Not only are the models created using open-source tools, they are deployed to open-source environments that use Docker and Kubernetes in place of more traditional environments. SAS Model Manager is evolving to be a management platform that handles traditional SAS models and open-source models as equal partners. This paper discusses strategies for managing the life cycles of Python, R, and TensorFlow models using SAS Model Manager | score code, sample, data cleaning | source | 16 |
| 203 | Optimal Use of Extended Data Types, Memory Mapping in SAS Viya 3.3 | Saurabh Tripathi | The major challenges for business users these days, are optimally ingesting data in terms of size, time and performance. This paper focuses on optimal use of SAS Cloud Analytic Services (CAS), and how SAS Viya users will be benefitted using a set of rules to achieve the goals in the most efficient manner. We developed a cognitive approach of data ingestion in SAS Viya, which can handle the data bloating and load time that in effect improves the performance of SAS Visual Analytics on SAS Viya | sas viya, cpu time, proc step, character variable | source | 9 |
| 204 | Optimize SAS Viya: Dividing Load through the Use of Multiple SAS Cloud Analytic Services Servers | Brandon Kirk | Analytic workloads mixed with data preparation and visualization can put SAS Viya to the test. Learn how to optimize your investment and orchestrate different workloads by leveraging multiple SAS Cloud Analytic Services (CAS) servers. In this paper, we explain setting up and administrating additional CAS servers to keep processes optimized. Once the CAS servers are set up, we cover data administration and securing the data for the intended workload. Orchestrating the workload on SAS Viya keeps your general SAS Visual Analytics report consumers and machine learning data scientists from stepping on each other's toes | cas, sas viya, data cleaning | source | 8 |
| 205 | Optimizing Collection Strategies with SAS Intelligent Decisioning | Luiz Kauffmann, Sunny Zhang |
Debt collection is an important part of asset resolution and credit loss mitigation for many institutions. Traditionally, collection decisions are based primarily on delinquency status, product type, and collateral type. In many cases, this can be a complicated and time-consuming process. As a result, recovery rates can be lower than expected due to lower collection revenue and high recovery costs. In this paper, we introduce a new collection decision process that identifies the optimal collection channel with the action using recovery action scores. The recovery scores estimate the "expected payoff" based on both customer -level risk models and channel propensity models incorporating cost variables. Micro-segments for customers are further developed to assist the mapping of specific actions to different customers | days past due, past due, machine learning, financial services | source | 10 |
| 206 | Optimizing Supply Chain Robustness through Simulation and Machine Learning | Bahar Biller, Jinxin Yi |
This paper introduces a supply chain simulator that has been built using SAS Simulation Studio. The key features of the SAS simulation technology, which enable the development of digital supply chains and the analysis of thousands of scenarios to perform risk-and-return tradeoff, are discussed. The paper concludes with a description of how computational efficiencies can be achieved through an integrated use of SAS Simulation Studio and SAS Visual Data Mining and Machine Learning | machine learning, dataset, scenario analysis | source | 9 |
| 207 | Organize and Manage Files by Using SAS Programming | Weibo Wang, Heyang Li |
In daily work, we often save different types of files on physical servers or hard disks. Statistics show that 7.5 percent of an organization's documents are lost entirely, while another 3 percent are misfiled. As time goes by, the servers or hard disks would get cluttered. We may want to regularly organize, archive or delete some old files to release space. Many applications can do these, SAS software being one of them. SAS is not only an analytics, business intelligence and data management software, but also a powerful tool that can make your housekeeping work more efficient. Together with SAS Management Console Scheduler or UNIX crontab, you can set up automated process to regularly scan your physical server or hard disk. The process can send you a report of all the files in a directory. It can delete files of a size over a threshold or having not been modified for some days. It can remove duplicate files. It can archive files. It can split and merge files | macro conditional, business intelligence | source | 6 |
| 208 | PARSING: Using SAS When the Data Are Hiding in a Non-Standard Format | Andrew T. Kuligowski | Sequential files? Spreadsheets? Databases? There are numerous tutorials that instruct the SAS user in techniques to extract data from standard sources. Sometimes, however, the desired data is hidden inside a non-standard source; information may be found within the flow of a text document, for example. This presentation will address some techniques that can be used when not dealing with cleanly formatted data, through use of an example where data are found within a free -form text file. It will deal with identifying what can be considered useful data and what can be discarded, then tackle techniques to extract the data for further analysis, reporting, or whatever is the desired result. Please note that this paper covers many of the items covered in "Parsing Useful Data Out of Unusual Formats Using SAS", which the author first wrote and presented approximately 10 years ago. This paper uses a new example for use as a learning tool | sample, out= | source | 12 |
| 209 | Performance Do's and Don'ts for SAS Visual Analytics | Gregor Herrmann | When you ask users of business intelligence applications what are the important factors that determine whether they accept the solution, usability and performance are frequent mentions. While usability is predefined by the design of the application, performance is determined by a variety of aspects. These range from tuning the compute server to optimizing data structures for reporting over adjusting report design to the choice of the browser that runs SAS Visual Analytics. In this paper, I describe the most important adjusting screws that report authors and administrators should look at if they want to improve overall performance of SAS Visual Analytics | user-defined format, business intelligence | source | 6 |
| 210 | Piecewise Latent Growth Curve Models to Test for Discontinuities in Disease Prevalence Trends | Lin Yan | The International Classification of Diseases (ICD) is a standard coding and classification system of diagnoses and health conditions developed by the World Health Organization (WHO). The ICD has changed over time; different versions of the ICD are used in health data repositories of hospital and physician service records that contain diagnosis codes. These changes can result in discontinuities in prevalence estimates for health conditions, such as hypertension, arthritis, and dementia, as the meaning of a condition changes over time. Our research purpose is to demonstrate piecewise latent growth curve models to test for discontinuities in ICD-coded data. We apply these models to health data from one Canadian province (population 1.2 million) for cardiorespiratory health conditions captured in ICD versions 8, 9, and 10 | proc mixed, random intercept, goodness of fit, negative binomial regression, regression analysis, estimate, panel data, binomial distribution, fixed effects, proc glimmix | source | 10 |
| 211 | Pies and Donuts: A New SAS ODS Graphics Procedure Dessert! | Dan Heath | Pie charts are a very common graphical display, particularly when showing part-to-whole comparisons. Previously, you needed to use the Graph Template Language (GTL) to create this display in the SAS ODS Graphics system. But now, with the SGPIE procedure, pie and donut charts are a "piece of cake" to create! This paper focuses not only on the features of this new procedure, but also on the effective use of pie charts in general |
graph template language, bar chart | source | 11 |
| 212 | Play More, Achieve More: Training the Next Generation of Data Analysts Through Strategic Gaming | Hillary Graham | How did you learn to program? Most of us would answer this question by pointing to on-the- job experience. Documentation and instructional videos have ample value, but we all know that the best way to learn something is to jump in and do it. However, building a comprehensive programming skill set can take years' worth of individual experiences, and, with the ever-changing landscape of technical innovation, the expectation for analytical programmers to learn faster is increasing. Recent research indicates that gaming can be an effective way to learn new skills because games encourage an emotional investment that traditional training methods do not | proc iml, proc step | source | 11 |
| 213 | Post-9/11 GI Bill: Teasing Out Insights about Veterans' Success in Postsecondary Education | Walter Ochinko | This paper (1) examines the challenges researchers face in using publicly available databases to track veterans use of GI Bill benefits and their success in earning postsecondary credentials, and (2) presents the methodology and findings of several Veterans Education Success reports that, despite these challenges, teased out important insights on veteran outcomes. Several Department of Veterans Affairs (VA) databases provide information about the use of GI Bill educational benefits, but they do not allow researchers to differentiate among veterans and other individuals who use those benefits, such as eligible family members or individuals who are still serving. Moreover, the VA data focus on beneficiaries who are using benefits even though some individuals may have exhausted their benefits yet may still be attending school | sample, default rate | source | 18 |
| 214 | Power to the Report Viewers | Atrin Assa | In this paper, we explore how you can take the guesswork out of creating reports by giving report consumers the ability to make reports their own. Based on the controls that you give consumers, they can change what they see, by doing things like toggling legends and changing chart types. With even more power, they can create self-service queries like filters, ranks, and custom groups, and even change business metrics- all with undo and redo. You'll learn the different levels of control report authors can provide and all the great ways you can customize a report you're reading just for you | source | 9 | |
| 215 | Practical Geospatial Analysis of Open and Public-Use Data | Pradeep Mohan | As a geospatial data scientist, you might want to analyze publicly available data sets from several sources, including kaggle.com, data.gov, and other academic or scientific organizations. Analyzing open and public-use data sets such as these poses many challenges, including disparate data formats (ESRI shapefile, NetCDF , GeoJSON, and so on), disparate geographic references, and the use of street address data instead of actual spatial units. This paper presents a variety of real-world examples of public-use geospatial data to show you how to transform the data into a prespecified geographic reference, import the data into SAS Viya via a Python interface, and select suitable subsets that can be convenient for data analysis. These subsets are then analyzed by using SAS/STAT procedures KRIGE2D, SPP , and GLIMMIX. The paper also demonstrates how to map the estimates from these procedures by using the Base SAS mapping PROCEDURE SGMAP invoked from Python |
sas viya, proc step, spatial covariance, covariance, covariance structure | source | 20 |
| 216 | Predict Freezes in Natural Gas Pipelines by Using SAS Machine Learning and Python Scikit-Learn | Bingi Arnold Kanagwa M.S, Ho-Chang Chae |
Presentation | machine learning | source | 8 |
| 217 | Pricing Life Insurance Contracts with Early Exercise Features Using Neural Networks | Helgard Raubenheimer | This paper describes an algorithm based on neural networks to price life insurance contracts embedding American options. We focus on equity-linked contracts with surrender options and terminal guarantees on benefits payable upon death, survival, and surrender. We use the Monte Carlo approach to generate artificial sample paths of the processes. We then use the least squares neural networks regression estimates to estimate from this data the so- called continuation values, rather than the ordinary least squares (OLS) estimates. For the purpose of fitting the neural network, we use the NEURAL procedure in SAS/IML. The results from this investigation showed that neural network regression estimates provide adequate improvements to the OLS estimates and are thus useful additions to pricing option embedded insurance products |
neural network, life insurance, sample, monte carlo, proc step, regression analysis, ifrs 17, proc iml, proc calis, policy | source | 9 |
| 218 | Principal Component Analysis Demystified | Caroline Walker | Have you used or thought of using Principal Component Analysis (PCA) as a feature extraction method in your machine learning pipelines, but wished for a better understanding of what a principal component is and how it's obtained? We take a deep dive into a small dimensional data set, present a visual explanation of the role played by eigenvalues and eigenvectors when PCA is applied, and illustrate how what you start with leads to what you end with, what the advantages are, and what could get lost along the way | proc princomp, dimensionality reduction, covariance, correlation | source | 7 |
| 219 | Programmatic Tools for Parallel Processing in SAS | Leah Durbak | Presentation: This poster features three tools for efficiently leveraging parallel processing using the SAS Grid within the Chronic Conditions Warehouse Virtual Research Data Center (CCW VRDC): • the SAS macro facility, • programs that write programs, and • Automated log check programs. Please use the headings above to navigate through the different sections of the poster | source | 8 | |
| 220 | Propensity Score Matching with Survey Data | Bocheng Jing | Clinicians and healthcare researchers often use national -representative surveys to conduct observational studies. Propensity score met hods are one of the popular methods to draw estimates for key exposures, treatments, and interventions. I t is surprising that propensity score methods have not been ubiquitously incorporated in survey observational studies. Some authors have proposed incorporating sampling weights as a covariate in the propensity score model, while others have used sampling weights as survey weights, that is, a weighted model. Thus, there are still inconclusive results on which method would account best for complex survey design features. In our paper, we used five different methods to calculate propensity scores that account differently for survey design features. We used both matching and weighting methods to adjust the outcome model. It was unclear which met hod of propensity score calculation was better | propensity score, treatment effect, regression analysis, sample, logistic regression, logistic distribution, proc step, sample weight, proc surveylogistic, correlation, proc logistic, survey weight, odds ratio | source | 14 |
| 221 | Python and the SAS Quality Knowledge Base for Better Data Quality and Entity Resolution | Arnold Toporowski | Python coders can now leverage the power of the SAS Quality Knowledge Base and dramatically improve their data quality and data matching results. This session explores the capabilities now available to Python coders and gives coding examples and demonstrations showing how to leverage SAS Quality Knowledge Base capabilities such as parsing, standardization, and match-coding to better prepare data for analytics. Techniques for entity resolution and duplicate elimination are also explored | data cleaning, cas action, entity resolution | source | 12 |
| 222 | Raising the Bar! Developing World-Class Reports with SAS Visual Analytics | Lucy Smith | SAS Visual Analytics 8.x provides around 40 different visuals out of the box. This gives all users, both novice and advanced, a lot of potential ways to present their data. However, as the reporting requirements and dashboards develop, the default visuals mig ht not always be enough. This paper covers three exciting ways to enhance and extend the default visuals in SAS Visual Analytics. The first method starts with default visual and explores options in SAS Visual Analytics to customize the appearance. The second method looks at using the custom graph builder to extend the default visuals to represent your data in the required way. The last method showcases how to include a completely new custom visual to SAS Visual Analytics, using the D3.js JavaScript framework for visualization. This paper is for SAS Visual Analytics users of any ability, and the techniques covered are applicable in SAS Visual Analytics 8.x on SAS Viya | line chart, series plot, bar chart, sas viya | source | 11 |
| 223 | Rare Events or Non-Convergence with a Binary Outcome? The Power of Firth Regression in PROC LOGISTIC | Patrick Karabon | Rare Events and separation are both common analytical challenges encountered when working with a binary variable. Problems with convergence of a logistic regression model due to complete separation is a particular challenge. Firth's Penalized Likelihood is a simplistic solution that can mitigate the bias caused by rare events in a data set. Called by the FIRTH option in PROC LOGISTIC, this method will even converge when there is complete separation in a dataset and traditional Maximum Likelihood (ML) logistic regression cannot be run. The implementation of the Firth method is straightforward in SAS and has advantages as compared to other potential methods, including Fisher's Exact test, traditional ML logistic regression, and Exact logistic regression. This paper briefly introduces the Firth method and discusses the advantages of this method compared to other methods |
regression analysis, logistic regression, logistic distribution, likelihood function, sample | source | 9 |
| 224 | Reaching Your Potential as a Statistician and Leader | Gary R. Sullivan | In year's past, a popular question in interviews, mentoring sessions or goal-setting discussions was "Where do you hope to be in five or ten years?" Many statistical leaders will tell you that the path of their career was not what they planned, if there even was a plan. But they can tell you some skills and/or experiences that were instrumental in helping them achieve advancement and success. So, perhaps a better question is "How will you advance your skills so that you can grow as a statistician, take on new roles and challenges, and continue to contribute to your organization and/or profession in a way that is fulfilling and rewarding?" Simply put, "How will you reach your potential as a statistician and leader?" This presentation will provide insights and guidance to this question through personal experiences and leadership study. Concepts that will be discussed include networking, business acumen, strategic thinking and teamwork | clinical trial, proof of concept | source | 17 |
| 225 | Read Before You Read: Reading, Rewriting & Re-Reading Difficult Delimited Data in a Data Step | Michael Chu | Loading delimited data within the DATA STEP can get interesting/frustrating quickly if you have quirky data. The abrupt appearance of a delimiter in an unquoted character field shifts all following fields to the right; the random removal of one shifts fields to the left. Left unchecked, the line will not get processed correctly, as SAS attempts to read each separated field using its neighbour's INFORMAT. Thankfully, there is potentially a way to spot issues like these, namely via the "INPUT @" statement. What's more, it may also be possible to correct them on-the-fly by directly modifying the "_INFILE_" automatic variable. This additional coding can be injected into the existing DATA STEP code such that the original INPUT statement(s) can continue to function properly even when faced with the difficult delimited data. This paper provides an in-depth exploration of the approach outlined above. Readers can immediately test out this concept using the supplied code |
input statement, txt, delimited file, sas log, sample, automatic variable, character variable, dataset | source | 12 |
| 226 | Reading in a Comma Delimited File with a Data Dictionary | Kalyani Telu | Comma-separated values files (CSV) are one of the most common files that SAS programmers import to create SAS datasets. When using PROC IMPORT, user defined formats and labels will be lost and the programmer needs to establish informats, formats and labels to each variable. This process is typically done manually, and it is highly time consuming if the dataset has many variables. This process can be automated through storing meta data in an EXCEL file and writing a simple SAS program to combine the meta data with the raw data. A dynamic approach has been developed for reading large raw CSV files using simple DATA STEP statements understandable to beginner programmers |
raw data, input statement, proc import, proc step, dataset | source | 6 |
| 227 | Real-Time Call Analytics Using SAS Viya and Open-Source Platforms | Ranjit Jangam, Anvesh Reddy Minukuri |
IVR calls in call centers is one of the primary engagement channels for customers to interact with service and product provider companies. Call data is very rich and can play a big role in improving customer experience e.g. provide real-time agent recommendation and customer feedback. This data can also be used for post-call analytics to help drive effective and efficient future handling of calls and improve agent performance thereby meeting company service level agreements (SLA) e.g. first call resolution. Customer call interaction can be captured by converting audio transcripts to text. However, there are very few systems that are capable of manually converting audio transcripts to text on a real-time basis. SAS Viya provides a variety of new capabilities, one of these capabilities being voice transcriptions and its execution on cloud on a real-time basis | sas viya, customer experience, sample, recommendation engine, first call resolution, customer satisfaction, rest api | source | 13 |
| 228 | Recent Developments in Survival Analysis with SAS Software | G. Gordon Brown | Are you interested in analyzing lifetime and survival data in SAS software? SAS/STAT and SAS Visual Statistics offer a suite of procedures and survival analysis methods that enable you to overcome a variety of challenges that are frequently encountered in time-to-event data. This paper brings you up to date on new approaches and procedures in SAS software and provides an overview on when to use these procedures to overcome the challenges inherent in conducting survival analysis in today's world. Procedures and methods that you will learn about include performing model selection and model comparison with the PHSELECT procedure, fitting the Fine and Gray model and fitting Bayesian frailty models with the PHREG procedure, analyzing accelerated failure time models with the LIFEREG procedure, and handling interval-censored data with the ICLIFETEST and ICPHREG procedures. You will see how to deal with nonproportional hazards by using the LIFETEST procedure or the RMSTREG procedure |
proc phreg, censoring, survival analysis, proc step, sample, kaplan-meier plot, proc lifereg, feature selection, survival function, regression analysis, bayesian statistics, numeric functions, frailty model, proportional hazards assumption, proc means | source | 9 |
| 229 | Reducing Ecological Footprint: Examining Panel Data | Neil Belford | Degradation of the global environment is occurring at an alarming rate. It is feared by many that the current path of economic development is unsustainable. In this paper, worldwide panel data is explored to develop a model that can be used to find characteristics that are associated with a nation's ecological impact, as measured by Ecological Footprint Accounting. Through panel regression in SAS, several factors are found that are associated with a reduction in Ecological Footprint among OECD countries after controlling for GDP and population size. These findings point to general policy objectives that can be used by industrialized and industrializing countries to reduce their ecological impact | regression analysis, panel data, missing value, correlation, time series analysis | source | 7 |
| 230 | RegExing in SAS for Pattern Matching and Replacement | Pratap Singh Kunwar | SAS has numerous character functions which are very useful for manipulating character fields, but knowing Perl Regular Expressions (RegEx) will help anyone implement complex pattern matching and search-and-replace operations in their programs. Moreover, this skill can be easily portable to other popular languages such as Perl, Python, JavaScript, PHP and more. This presentation will cover the basics of character classes and metacharacters, using them to build regular expressions in simple examples. These samples range from finding simple literals to finding complex string patterns and replacing them, demonstrating that regular expressions are powerful, convenient and easily implemented | character variable, character functions | source | 33 |
| 231 | Relationship analysis and customer churn forecasting in a financial cooperative institution | Carolina Silva, Brunno Sousa Ramos |
The analysis of financial institutions, facing the new era of information access technologies, opened several ways to be addressed and explored, in particular to increase understanding about clients needs and improve the relationship with customers. In view of this, the objective of the present paper is to analyze the exit of clients in a financial cooperative, in order to understand the behaviors related to churn and identify in advance its possible occurrence. The development of the analysis was done following the steps of the SEMMA methodology. SAS Institute defines SEMMA as a data mining process of Sampling, Exploring, Modifying, and Assessing large amounts of data. The analyzed clients were initially separated in three clusters, using the K-means algorithm. In each group, a predictive modeling was done with algorithms of Random Forest, Decision Tree and Logistic Regression. Predictive modeling provided greater understanding about churn incidence reasons | data validation rules, proc hpforest, proc hpsplit, financial services, cluster analysis, logistic regression, sample, confusion matrix, proc fastclus, logistic distribution, regression analysis | source | 7 |
| 232 | Resources for SAS Admins and Architects: What You Need to Know to Get the Job Done Hassle-Free | Shelley Sessoms | As a SAS administrator or architect, your customers expect you to know everything related to SAS. And there's a lot to know. What is available to you, and where do you find all this information? Attend this discussion and you'll walk away with a multitude of resources to help you and your customers succeed with SAS | sas viya, sas administrator | source | 13 |
| 233 | REST Just Got Easy with SAS and PROC HTTP | Joseph Henry | Hypertext Transfer Protocol (HTTP) is the lifeblood of the web. It is used every time you upload a photo, refresh a web page, and in most every modern application you use today from your phone to your TV. A big reason why HTTP has become so ubiquitous with modern technology is because of a software architectural style known as Representational State Transfer (REST). REST has become the standard for interacting with independent systems across the web, which means that the consumption of REST APIs is, or is going to be, mandatory. The HTTP procedure in SAS has enabled developers to use REST APIs simply and securely for many years now, but the demands of current systems keep increasing. PROC HTTP has been given quite a few updates to make using this great procedure even easier than it ever was before |
proc http, sas viya, proc step, rest api, macro quoting function | source | 8 |
| 234 | Risk Modeling on the Fast-Track: Pythonistas (and Others) Harness the Power of the SAS Risk Engine | Dave Stonehouse | With the availability of the SAS Risk Engine on SAS Viya, SAS provides a first-class platform for developers to create and deploy risk models. The SAS Risk Engine is a complete, scalable solution that delivers a comprehensive set of risk models and risk metrics. The SAS Viya version of the engine accelerates development of custom risk applications using Python and other languages. Developers can focus their effort on the specific value-added algorithms related to their business context while leveraging key features from SAS, like risk simulation and stress testing. With the SAS Risk Engine on SAS Viya, the path from designing your model to deploying it in a high-performance environment has never been easier. In this paper, we demonstrate the power of developing risk applications in Python based on the SAS Risk Engine with examples from both market and credit risk | sas risk engine, sas risk, pd, credit risk, market risk, sas viya, monte carlo, excel | source | 9 |
| 235 | RSUB, CLI for SAS Server Environments | Matthew J. Kastin | RSUB is a command line interface, written in Java, which takes advantage of SAS Integration Technologies to fill a gap in SAS 9. NORC at the University of Chicago performed a migration from PC SAS to a clustered SAS Server Platform without SAS/GRID. This left many users accustomed to performing batch processing without their local SAS executable or alternatives provided in SAS Grid, such as bsub and sasgsub commands. Creating the RSUB utility fulfilled the needs of these users, providing them a CLI to us e for batch processing and scheduling in third party enterprise schedulers |
sysrc, sas output, batch processing, sas grid, options statement, sas enterprise guide | source | 13 |
| 236 | Sailing Your Data Lake with Self-Service Data Prep | Peter Baquero | Did the crystal clear analytics promised by your data lake never materialize? Have you been left with a murky data swamp, fearing what might be lurking beneath the surface? There are many reasons that the promise of data lakes hasn't come to fruition. One of those reasons is the lack of a single comprehensive tool to connect, integrate, transform, and govern the data in a data lake. This tool should be usable by citizen data scientists to deliver true self-service data preparation. It's easy to understand why there hasn't been a single tool that connects, integrates, transforms, and governs data in a data lake. By their nature, data lakes are not homogenous environments. Any tool working with data in a data lake needs to be able to work with structured, semi-structured, unstructured, and binary data. Integration is the next challenge. Baseline data integration should include basic table manipulation such as joining, appending, subsetting, and so on | data lake, data analyst, sas engineer, data wrangling, data governance, sample, credit card, data profiling, data integration, sas viya | source | 20 |
| 237 | Sample Size Calculations Using SAS, R, and nQuery Software | Jenna Cody | A prospective determination of the sample size enables researchers to conduct a study that has the statistical power needed to detect the minimum clinically important difference between treatment groups. With knowledge or assumptions about the study design, drop- out rate, variation of the outcome measure, and desired power and alpha levels, the required sample size for a study can be calculated. This paper discusses methods for calculating sample size by hand and through the use of statistical software. It walks through the method for computing sample size using the POWER procedure and the GLMPOWER procedure in SAS and compares the commands and user interfaces of SAS with R and nQuery software for sample size calculations |
sample, sample size, effect size, proc step, proc power, power analysis, normal distribution, proc glmpower, type ii error, proc power and sample size, anova, chi-square test, sas output, alternative hypothesis, null hypothesis, type i error, correlation, two-way anova, standard deviation, clinical trial | source | 19 |
| 238 | SAS and Amazon Redshift: Overview of Current Capabilities | Nicolas Robert | Amazon Redshift has become a very popular database management system in modern cloud infrastructures. It provides flexibility and agility, and it can scale easily and save costs. More and more companies are using Redshift as their new data warehousing tool. How does SAS fit in this context? How do SAS users work efficiently with Redshift data? This paper gives an overview of the current access and integration capabilities between SAS products and Amazon Redshift. We cover this from both SAS Viya and SAS 9 standpoints, including news from the latest releases | proc sql, amazon redshift, sas viya, proc step, cpu time, sql pass-through, implicit sql pass-through, create table, inner join, sas 9, proc append, amazon s3, in-memory analytics, latency, connection string, dataset | source | 20 |
| 239 | SAS and Microsoft Office 365: A Programming Approach to Integration | Chris Hemedinger | Many of us are now using cloud-based productivity tools like Microsoft Office 365. And some of us are using SAS software in the cloud, too. For those of us who use SAS to read and create Microsoft EXCEL documents, cloud-based files can add an extra wrinkle when we automate the process. It also adds some exciting possibilities! The Microsoft Office 365 suite offers APIs to discover, fetch, and update our documents using code. In this paper, I show you how to use SAS programs to reach into your Microsoft OneDrive and SharePoint cloud to read and update your files. And I show you how to leverage the collaborative features of Microsoft Teams to publish useful updates to your colleagues and yourself. The approach relies on the REST APIs in Microsoft Office 365 and on the HTTP procedure in SAS |
proc step, macro variable, proc http, sample, rest api, excel, recommendation engine | source | 25 |
| 240 | SAS BI Platform to Improve Use of Analytics in Higher Education: Do the Stats Match the Intent? | Sean W. Mulvenon | A national trend for all university and college administrators is in creased pressure from l egislators and other education advocates to increase persistence and graduation rates of all students. Learning analytics (LA) models have been implemented nationwide with the use of Learning Management Systems (LMS) software to identify access patterns of students to support materials, identify those who might be performing below expectations, and to use this information to be more effective in providing educational support. A quick review of data structures and the common metrics employed revealed numerous statistical anom alies that might be problematic in use of LA and LMS in higher education. For example, one dubious metric identified was "duration", which was an aggre gate of the time a student used to complete practice exams | data lake, data science, proof of concept | source | 7 |
| 241 | SAS Data-Driven Design - How to Develop More Flexible, Configurable, Reusable Software through Data Independence of Control Data | Troy Martin Hughes | Data-driven design describes software design in which the control logic, program flow, business rules, data models, data mappings, and other dynamic and configurable elements are abstracted to control data that are interpreted by (rather than contained within) code. Thus, data-driven design leverages parameterization and external data structures (including configuration files, control tables, decision tables, data dictionaries, business rules repositories, and other control files) to produce dynamic software functionality. This hands- on workshop introduces real -world scenarios in which the flexibility, configurability, reusability, and maintainability of SAS software are improved through data -driven design methods, as introduced in the author's 2019 book: SAS Data-Driven Development: From Abstract Design to Dynamic Functionality | data dictionary, macro variable, %if-%then-%else | source | 8 |
| 242 | SAS DataFlux Matchcodes: A Practical Use at the Canada Revenue Agency for Tax Filing Status | Jason A. Oliver | To streamline the "data analytics supply chain," the Canada Revenue Agency (CRA) in partnership with SAS has established an automated matchcodes program in SAS DataFlux software, which takes collected web domain records and matches them on existing taxpayer databases in the data lake, using combinations of company or trade name, phone number, and/or postal code. Ultimately, this helps to reveal any non -filers as well as taxpayer risk/audit history, which can be used to derive predictive analytic s algorithms, and is much more efficient than manual lookup of company contact info from web pages against the operational taxpayer database. This case study can conceivably be applied in the abstract for multiple types of government functions, such as inv estigations or law enforcement or national security-but in this presentation, it is from a taxation enforcement perspective | data lake, type i error | source | 12 |
| 243 | SAS Enterprise Miner vs. Scikit-Learn - How do they recommend me good songs? | Raphael Lima | Presentation | data mining | source | 10 |
| 244 | SAS Event Stream Processing at the Edge: Reduce or Eliminate the Need to Transmit Data for Analysis | Bob Augustin, Sri Raghavan |
The new frontier is the Intelligent Edge. The intersection of people, places, sensors, things and computers defines the intelligent edge. HPE tested SAS ESP using NVIDIA Tesla T4 GPUs that were installed in an HPE EL4000, along with m510 server cartridges. This allows for video processing to be performed right at the edge, which reduces or eliminates the need to transmit large amounts of raw data to the data center or cloud for analysis. There will still be times when a subset of summarized data needs to be transmitted to the data center for further analysis. For example, immediate analysis can happen at the edge, but summarized data transmitted from the edge to the data center can then be combined with other like sources to look for trends across a larger sample. These trends could be looking for issues where a design update may be required or they could be looking at how environmental factors affect equipment in the field | stream processing, real-time processing, benchmarking | source | 15 |
| 245 | SAS Grid Execution with Amazon Web Services (AWS) | Piyush K. Singh | Because of cost and maintenance, industries are moving towards the cloud adaption. Day by day, companies are pushing their data and processing in the cloud. Having data and processing closer makes data processing efficient. Currently, AWS is one of the key cloud providers in the market. Increase in the use of the AWS environment in the industries makes it important to establish the connection between AWS and SAS as well. This paper explains the techniques and configurations that SAS users should consider to access and process the AWS files in the SAS Grid environment. Though there are SAS procedures like S3 which can be used in SAS code to read/write files, we need to make other configuration changes, proxy settings, and access checks before we process AWS files in the SAS Grid platform. This paper explains the techniques which can help the SAS user to process cloud data easily and efficiently in the SAS Grid environment | sas grid, proc step, sas viya, sas enterprise guide | source | 11 |
| 246 | SAS Grid Manager and SAS Viya: A Strong Relationship | Edoardo Riva | SAS Grid Manager and SAS Viya implement distributed computing according to different computational patterns. They complement each other in providing a highly available and scalable environment to process large volumes of data and produce rapid results. From finding the best way to allocate tens of jobs on multiple machines, to allocating huge amounts of data to quickly analyze in parallel, this paper shows how to architect and implement SAS Grid Manager and SAS Viya to effectively support your business | sas viya, sas grid, sas enterprise guide, cas, sas studio, data mining, batch job, sas 9, machine learning, parallel processing | source | 20 |
| 247 | SAS Grid Manager: Inside Look into Grid-Launched Servers | Murali Srinivasan | As a SAS Grid Manager administrator, you might wonder whether you can improve utilization of compute resources so that your organization has better return on investment. You start SAS server processes in your grid environment by running server start-up scripts and through application invocation. SAS server configuration in SAS metadata determines how a server is launched. This paper presents best practices for instantiating SAS servers within a grid environment. Grid-launched servers benefit from SAS Grid Manager features including workload balancing, parallel processing, high availability, and flexibility with scaling the architecture | sas enterprise guide, sas grid, data cleaning, sas management console, data mining | source | 22 |
| 248 | SAS Intelligent Decisioning: An Approach to High Availability for Real Time Integration | Michael Goddard | Are you thinking about real-time analytics-integrating your analytics with your business applications? Do you understand the deployment patterns for SAS Micro Analytic Service? Do you understand high availability with SAS Viya? This paper outlines two approaches to implementing high availability for SAS Viya , which is used for real-time analytics using SAS Intelligent Decisioning. In order to implement high availability, you need to understand the deployment patterns and you need to understand that SAS Intelligent Decisioning can be used for batch and real-time processing. Learn about the architecture considerations, deployment patterns, and an approach to high availability using a shared-nothing deployment architecture pattern | sas viya, real-time processing, rest api, cas, analytics platform, exception handling | source | 20 |
| 249 | SAS Knows Good Manufacturing Practice | Lois Wright | The two key system requirements for pharmaceutical manufacturers are electronic data storage and statistical process control, and both must comply with the electronic records requirements of Title 21 CFR Part 11. SAS Life Science Analytics Framework is specifically designed for electronic records and electronic signatures. SAS Life Science Analytics Framework efficiently manages the transformation, analysis, and reporting of life science data. SAS/QC software, operating within SAS Life Science Analytics Framework, provides the specialized tools required for analysis of manufacturing data. SAS/QC is designed for process and manufacturing engineers, as well as for directors of quality improvement. This presentation shows how these two SAS products, working together, satisfy the business needs of the pharmaceutical manufacturing industry |
life sciences, quality control, 21 cfr part 11, standard deviation | source | 11 |
| 250 | SAS on an Amazon Web Services Data Lake: Enabling Access to Data Lakes on SAS | Dilip Rajan | Modern business applications produce a large variety of data at different volumes and speeds. Traditional data warehouses were not designed to consume and process such large amounts of data. Due to their tightly coupled storage and compute nature, customers end up having to pay for data at rest even if that data is not frequently queried or used. Data lakes provide the ability to store data in raw format, which enables a wide variety of use cases such as real-time analytics, machine learning, batch processing from different sources such as log files, clickstream events, and so on. SAS and its integration with the AWS data lake solution (Amazon Simple Storage Service, or Amazon S3) provides users with the flexibility to access data in disparate formats using AWS Glue | data lake, amazon redshift, proc sql, amazon s3, ansi sql, machine learning, parquet, data warehouse, unstructured data | source | 11 |
| 251 | SAS Options Precedence in SAS Grid Environment | Piyush K. Singh | This paper helps to understand the precedence of SAS Options mentioned in different configuration files. There are multiple configuration scripts files that get deployed with SAS Grid install, and this paper explains the purpose of these scripts and how we should edit these scripts to meet the new business needs for SAS options which are not present with default SAS Grid deployment. The codes and techniques given in this paper will describe how to edit these configuration scripts files to set the desired options or environment variables. There can be different kinds of jobs to be executed in SAS Grid. The Platform Administrator creates different SAS Application Server context for different kinds of business requirements or different user groups. It becomes important to know how these different SAS Application Servers are being used (with different SAS jobs) | sas grid, sas enterprise guide, proc options, proc step | source | 7 |
| 252 | SAS Packages: The Way to Share (a How To) | Bartosz Jabłoński | When working on Base SAS code, especially when it becomes more and more complex, there is a point in time when a developer decides to break it into small pieces. The developer creates separate files for macros, formats/informats, and for functions or data too. Eventually the code is ready and tested and it is time for the deployment. The issue is that the code had been written on a local Windows machine and the deployment is on a remote Linux server. Folders and files have to be created with the proper structure, code has to be run in the right order and not mixed up. Moreover it is not the developer who is deploying... Small challenge, isn't it? How nice would it be to have it all (i.e. the code and its structure) wrapped up in a single file - a portable SAS package - which could be copied and deployed with a one-liner like: %loadPackage(MyPackage)? In this article an idea of how to create such a SAS package in a fast and convenient way will be proposed/shared. We will discuss: a concept of how to build a package, the tools required to do so (framework), and a how to of the process (i.e. generating packages, loading, and using them). The intended readers for the following document are intermediate SAS users (i.e. with good knowledge of Base SAS and practice in macro programming) who want to learn how to share their code with others. | macro variable, sas base, proc step, sas packages, macro definition | source Extended version (2026-07-23) SAS Packages Framework SAS Packages Archive SAS Packages Tutorial |
21 |
| 253 | SAS Software in a Crisis: Spatial Insights from Mapping a Humanitarian Disaster | Michael Matthews | Base SAS software includes powerful tools for spatial analytics that can be used in a variety of circumstances. This case study examines the unfolding of the Ebola crisis in 2018, and the efforts of the Humanitarian OpenStreetMap Team (HOT) community to support ai d workers in reaching impacted communities. Using SAS Output Delivery System graphics procedures to analyze the OpenStreetMap data, the work undertaken by global volunteers and mapping communities to identify terrain, infrastructure, and housing can be a nalyzed to show the impact both spatially and over time. This analysis demonstrates techniques that are applicable for any organization using the open data from OpenStreetMap or other mapping data sources | proc step, dataset, sas base, scatter plot, proc surveyreg, proc http | source | 20 |
| 254 | SAS Stored Process Web Services and Dynamic Data Exchange | Greg Dorfner | The growing need for a Web User Interface is becoming more apparent in todays' applications. At Prime Therapeutics LLC we paired a Pega Web interface with SAS through Web services and SAS Stored Processes to build an application named Benefit Modeling Tool (BMT). This paper will describe the steps we took in building and supporting the BMT application | web service, sas enterprise guide, sas administrator, sample | source | 19 |
| 255 | SAS Studio Custom Tasks: Tips and Tricks for the Adventurous Task Author | Olivia Wright | SAS Studio provides built-in point-and-click tasks for generating and executing complex SAS code. SAS Studio also enables users to embark on the journey of creating their own interface for their own SAS code, known as a custom task. Building a custom task is easier than you might think. There are great resources available for getting started writing custom tasks: SAS Communities articles, GitHub examples, free e-learning, and previous SAS Global Forum papers. But what about those adventurous task authors who have progressed out of the "getting started" phase? This paper focuses on more daring custom task concepts that aren't covered in introductory material. Examples include writing the optional requirements and dependencies sections, creating a multi-step (multiple-task) workflow, incorporating Apache Velocity Template Language code beyond the #foreach, and working with SAS Cloud Analytic Services (CAS) tables. Join me on this quest to create advanced custom tasks that push the limits and incorporate the features provided by SAS Studio and Apache Velocity Template Language | sas viya, cas action, sas studio, sample, proc step | source | 12 |
| 256 | SAS System Elasticity in Amazon Web Services Using SAS Grid Manager for Platform | Glenn Horton | In many systems, demand for computational resources varies over time. Wouldn't it be nice if the resources available at any given time to a SAS system, and the associated cost, were scaled based on the demand for resources at that time? Now, that can happen when the SAS system lives in Amazon Web Services (AWS) where you pay for only what you use. SAS Grid Manager for Platform knows how busy your system is and can dynamically grow the system when demand gets close to exceeding availability. And we can shrink the system when the demand decreases. The system can grow to a finite upper limit or unbounded. And all this is based on system attributes you define. This presentation describes how a SAS system is configured to make this happen, the AWS infrastructure requirements, and system attributes used to grow and shrink resources | sas grid, sas management console | source | 7 |
| 257 | SAS Visual Analytics SDK: Embed SAS Visual Analytics Insights in Your Web Pages and Web Apps | Brad Morris, Robby Powell |
Embedding SAS Visual Analytics insights in your web pages and web apps lets you share insights through the portals your users regularly access. You already use SAS Visual Analytics to gain insights through drag-and-drop interactions and rich visualizations of your analytics results. And you share those insights with others to collaborate and solve challenges. The next step is embedding your insights in your corporate portal to increase the number of eyes that see what you see. Welcome to the SAS Visual Analytics SDK! The SAS Visual Analytics SDK is a collection of JavaScript libraries that web developers can use to embed SAS Visual Analytics insights within their web pages and web apps. Add to your web pages and web apps entire SAS Visual Analytics reports or individual objects from your reports. Join this session to learn about the SAS Visual Analytics SDK and see live demos of how easy it is to embed live, interactive SAS Visual Analytics content in your web pages and web apps | sas viya, bar chart | source | 14 |
| 258 | SAS Visual Analytics: "Where" Can Tell You "Why" | Robby Powell | Everything happens somewhere and much of our data includes location information. Internet of Things (IoT) sensors include x,y coordinates in their data and the mobile apps we depend upon use location to provide specialized, targeted information. How are you gaining insight from location information in your dashboards and data explorations? Gain deeper insight with SAS Visual Analytics, which includes many location analytics capabilities out-of-the-box, as well as access to advanced location analytics capabilities through integration with Esri. With location analytics, you can do the following: visualize data on a map; enhance your existing data by geocoding or enhancing with Esri demographics data; gain insights through dense data; perform travel-time and travel- distance analysis; include your multi-layer web maps; and more in SAS Visual Analytics. Attend this session to see how "where" can tell you "why" using location analytics in SAS Visual Analytics | geospatial analytics, scatter plot | source | 12 |
| 259 | SAS Viya 3.5 Kerberos Constrained Delegation: Putting the Dog on a Leash | Stuart J. Rogers | Do you maintain a SAS Viya 3.5 environment leveraging Kerberos for authentication? Are you struggling with the strict security requirements of your IT department? SAS Viya 3.5 introduces support for Kerberos constrained delegation. This paper outlines the additional steps you must complete to correctly implement Kerberos constrained delegation with your SAS Viya 3.5 environment. This implementation enables you to leverage technologies such as Microsoft Windows Defender Credential Guard on your client desktops. Learn about the configuration changes you need to make and learn some effective troubleshooting techniques to get your environment working and make your users happy | sas viya, sas studio, sas enterprise guide, sql server, proc sql, bootstrap | source | 20 |
| 260 | SAS Viya Monitoring Using Open-Source Tools | Bryan Ellington | Did you know that SAS Viya has an event-driven infrastructure that gives you access to a continuous flow of logs, metrics, and other activity? This paper discusses the event-driven architecture of SAS Viya and demonstrates how you can leverage it to send logs, metrics, and events to leading open-source tools. See how to export metrics to Prometheus and set up custom alerts that are triggered when specified thresholds are met or exceeded. Understand how to use Grafana to visualize metrics on dashboards that are customized to your needs. Learn how to send logs and events to Elasticsearch, and how to efficiently filter, search, and report on this data using Kibana | sas viya, data pipeline | source | 13 |
| 261 | SAS Viya: Approaching SAS Viya Backup and Restore in the Cloud | Sounak Nag, Suvadeep Chatterjee |
Presentation: Backups are an important part of any application, and the ability to recover quickly with the least data loss is critical. In this e-poster, we will discuss how to use cloud-native options for back up and restore for SAS Viya using SAS Backup Manager, command-line interface (CLI), and the different cloud offerings. We will also discuss the considerations before deployment and installation to recover SAS Viya using these cloud-native features. We have performed multiple backups and restore exercises across Amazon Web Services (AWS), Google Cloud Platorm (GCP), and Microsoft Azure. | sas viya | source | 9 |
| 262 | SAS Viya: Autoscaling SAS Micro Analytic Service (MAS) Nodes Using Compute Virtual Machines on the Google Cloud Platform | Suvadeep Chatterjee, Sounak Nag |
Presentation: With the advent of the cloud-ready SAS Viya 3.4, there is a demand for scalability for the Viya services on Cloud. SAS Viya includes a number of different microservices to support a scalable and elastic architecture. This e-poster deals specifically on how to separate SAS Micro Analytic Service (MAS) on a separate node and then auto-scale based on the CPU usage on that node using Google Cloud Platform's managed instance groups and other GCP native features. We have tested this and validated the functionality by publishing models with auto scaled MAS nodes. This presentation guides you through the process and how to use Google cloud-native features to achieve this. | sas viya | source | 9 |
| 263 | Scalable Cloud-Based Time Series Analysis and Forecasting Using Open Source Software | Javier Delgado | Many organizations need to process large numbers of time series for analysis, decomposition, forecasting, monitoring, and data mining. The TSMODEL procedure , available in SAS Visual Forecasting and SAS Econometrics software, provides a resilient, distributed, and optimized generic time series analysis environment for cloud computing. PROC TSMODEL offers capabilities such as automatic forecast model generation, automatic variable and event selection, automatic model selection, and parameter optimization. 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. In addition, PROC TSMODEL supports open- source integration with external languages Python and R |
time series analysis, by group processing, proc step, moving average, big data, cas, data mining, time series data, seasonal decomposition, outlier, parallel processing, proc print, feature selection, sysrc, dataset, proc freq | source | 21 |
| 264 | Section 508 and Maps: Breaking Down Barriers for People with Visual Impairments or Blindness | Ed Summers, Sean Mealin |
Independent access to maps and geospatial data has always been a systemic barrier for people with visual impairments or blindness (VIB). That barrier inhibits our participation in the classroom, on the job, and within our communities. SAS is working to solve that problem. This paper defines accessibility as it relates to maps and geospatial data, describes initial support for accessible maps in SAS Graphics Accelerator, and explains how you can use SAS 9.4 to create maps that are accessible for everyone, including people with VIB | data visualization, sas output | source | 8 |
| 265 | Securing SAS Viya Access with Single Sign-on and 2FA | Sandeep Grande | Presentation: A quick look at data breach trends shows that most of the security breaches involved weak, default or stolen passwords. Two-factor authentication (2FA) strengthens access security by requiring two methods (also referred to as factors) to verify your identity. These factors can include something you know -like a username and password, plus something you have - like a smartphone app to approve authentication requests. 2FA protects against phishing, social engineering and password brute-force attacks and secures your logins from attackers exploiting weak or stolen credentials. Single sign-on (SSO) is a session and user authentication service that permits an end user to enter one set of login credentials (such as a name and password) and be able to access multiple applications. | sas viya | source | 9 |
| 266 | Security Considerations in SAS Cloud Analytic Services for Google Cloud Platform Data Sources (Google BigQuery, Google Cloud Storage, Google Bigtable) | Sanket Mitra | Presentation | google cloud storage, sas viya, cloud storage | source | 8 |
| 267 | Semi-automatic Feature Engineering from Transactional Data | James A. Cox, Biruk Gebremariam, Tao Wang |
Transactional data are ubiquitous: whether you are looking at point-of-sale data, weblog data, social media data, genomic sequencing data, text, or even a standard relational database, these data generally come in transactional form. But data mining assumes that all the data are neatly packaged into a single record f or each individual being observed. With experts noting that 80% of a data scientist's time is spent in data preparation, is there a way to automate this process to make that job easier? We have developed a toolkit that can be deployed through SAS Studio and SAS Data Management Studio software to address this conundrum. Given transactional tables, which represent both categorical and numerical data, you can use this toolkit to pipeline a series of steps -including steps to reduce cardinality of cat egorical data, to roll columns up to a subject level, and to generate "embeddings" of those columns at the subject level -in order to prepare the data for the modeling task | macro variable, score code, adverse events, response variable, sas studio, proc step, feature extraction, proc freq, macro definition, sample, data cleaning | source | 17 |
| 268 | Show Me the Money! Preparing Economics Students for Data Science Careers | Steven C. Myers | Economists make great data scientists. Economics offers as a discipline many valuable skills such as problem-solving ability and storytelling. When economic theory and deep business acumen combine with applied econometric analytics plus an obsession to understand the data generating process and methods of dealing with dirty data, you have a lot of research savvy. Add a layer of deep SAS programming and analytics, and you have the beginnings of a great data scientist. We partnered in 2015 with the SAS Global Academic Program to offer a joint certificate in economic data analytics. Every graduate and undergraduate since have received the certificate. This paper will discuss the methodologies taught and pedagogies used in our program. Students learn programming in a team- and problem-based learning environment working on real data and real problems | data science, proc step, regression analysis, sas programming, machine learning, inferential statistics, time series analysis, problem solving, correlation, longitudinal data, excel, proc reg | source | 19 |
| 269 | Simple and Efficient Bootstrap Validation of Predictive Models Using SAS/STAT Software | Isaiah Lankham, Matthew T. Slaughter |
Validation is essential for assessing a predictive model's performance with respect to optimism or overfitting. While traditional sample-splitting techniques like cross validation require us to divide our data between model building and model assessment, bootstrap validation enables us to use the full sample for both. This paper demonstrates a simple method for efficiently calculating bootstrap-corrected measures of predictive model performance using SAS/STAT procedures. While several SAS procedures have options for automatic cross validation, bootstrap validation requires a more manual process. Examples focus on logistic regression using the LOGISTIC procedure, but these techniques can be readily extended to other procedures and statistical models. |
sample, bootstrap, data cleaning, proc step, logistic distribution, key performance indicator, proc logistic, feature selection, ods output, proc surveyselect, sample size, output, logistic regression, proc sql | source github |
15 |
| 270 | Simulating Data for Complex Linear Models | Phil Gibbs, Kathleen Kiernan |
One of the core tools of any statistician is working with linear models, from simple or multiple regression models to more complex, generalized linear mixed models. Data simulation enables you to be more comfortable with new types of models, by providing data to a model that will give known results. This paper introduces you to the random number functions in the SAS DATA STEP and shows you how to construct programs to simulate data for a variety of models. The paper briefly discusses basic areas of data simulation, such as linear regression. The paper then shows how to generate data for more complex mixed models, including repeated measures models under a variety of within-subject covariance structures. Also, the paper covers generalized linear mixed models like logistic and Poisson. Finally, nonlinear mixed models are discussed. After reading this paper, you should be able to better understand models with difficult parameterizations, especially in the covariance structure |
covariance, proc mixed, proc step, normal distribution, rand, treatment effect, fixed effects, covariance structure, proc iml, poisson distribution, estimate, repeated measures, regression analysis, logistic regression, repeated measures model, correlation, sample, uniform distribution, logistic distribution, sample size, weibull distribution, dataset, do loop, power analysis, proc glimmix, proc nlin, poisson regression, proc univariate, response variable, proc genmod | source | 20 |
| 271 | Simulating Time Series Analysis Using SAS - Part II Cointegration | Ismail E. Mohamed | Presentation: Many analysts mistakenly use the framework of linear regression (OLS) models to model variables of Time series data and to predict change over time or extrapolate from present conditions to future conditions. Time series data are different from randomly selected data and time series techniques must be used to analyze time series data. | time series analysis, time series data, proc reg, statistical software, regression analysis | source | 24 |
| 272 | Slinging Hash: The HASHING Functions Available in SAS | Richard D. Langston | This paper provides an overview of the HASHING* functions that have been introduced in 9.4m6 of the SAS System. These functions can perform hashing using MD5, SHA1, SHA256, SHA384, SHA512, and even old-school CRC methods, along with HMAC computations | txt, cas, sas studio, hashing function | source | 8 |
| 273 | Smarter and Faster Self-Service Data Preparation | Atrin Assa | In this paper, you'll learn about the latest and greatest self-service data preparation capabilities of SAS Visual Analytics. You will understand how smart suggestions can help you improve the quality of your data, how the new interface can help you work faster, and how better-prepared data can help you build better visualizations, better reports, and tell a more compelling data story | sample, missing value | source | 11 |
| 274 | Smarter and Faster with SAS Visual Analytics | Rick Styll | SAS Visual Analytics is the smartest business intelligence tool available. Automated Explanation, the new name for automated analysis, has been rewritten and redesigned to give you smarter and clearer insights, more interactivity, and easier-to-read explanations. In seconds, you can get the analytical story for the business intelligence hidden in your data that would take you hours to do manually. On top of that, you can automatically see suggested visualizations and identify related measures. For more advanced analytica l visualizations, like decision trees, you get human-friendly natural language descriptions, drawing out insights that are easy to digest | proc hpsplit, correlation, artificial intelligence, champion model, regression analysis | source | 13 |
| 275 | Solving Health Problems on the Academic Frontier: Training Students to Use SAS Through Research | Charlotte Baker | SAS is a valuable tool for data management and analysis in a variety of settings, and academia is no exception. As an educator of future public health professionals and a researcher with a data driven epidemiology laboratory, integrating various analytic tools into my courses and my lab is necessary. Students in my research laboratory complete data analysis to help solve large problems related to human and animal health issues, but many do not join the group with backgrounds in data analytics or statistics. It is absolutely imperative that these students get a well-versed education, and quickly, through-real world opportunities. This presentation discusses our methodology for training students, some of the benefits and struggles of training students in this manner, and demonstrate some of the overall competencies our students acquire through this experience | data science, sas studio | source | 5 |
| 276 | Solving the VAST Challenge: Three Case Studies Using SAS Visual Analytics | Riley Benson | The VAST Challenge is an annual contest that provides a synthetic data set and a series of questions for teams from around the world to test their existing tools against and to push boundaries by developing novel tools and methods. Teams within SAS have dedicated their spare time to create entries to the VAST Challenge for several years. This paper explores three of those entries showing how SAS Visual Analytics, other SAS tools, and open-source software were combined to tackle the challenges and show how the challenge has helped uncover areas for growth | time series analysis, outlier, sample, proc freq, dataset | source | 17 |
| 277 | Some _FILE_ Magic | Michael S. Zdeb | The use of the SAS automatic variable _INFILE_ has been the subject of several published papers. However, discussion of possible uses of the automatic variable _FILE_ has been limited to postings on the SAS-L listserv and on the SAS Support Communities web site. This paper shows several uses of the variable _FILE_, including creating a new variable in a data set by concatenating the formatted values of other variables and recoding variable values | put statement, input statement, numeric variable, missing value, data step, length statement, pdv, proc step, character variable, proc stdize, automatic variable, character functions, trailing @ | source | 7 |
| 278 | Sound Insights: A Pipeline for Information Extraction from Audio Files | Biljana Belamaric Wilsey, Xiaozhuo Cheng |
Audio files, like other unstructured data, present special challenges for analytics but also an opportunity to discover valuable new insights. For example, technical support or call center recordings can be used for quickly prioritizing product or service improvements based on the voice of the customer. Similarly, audio portions of video recordings can be mined for common topics and widespread concerns. To uncover the value hidden in audio files, you can use a pipeline that starts with the speech-to-text capabilities of SAS Visual Data Mining and Machine Learning and continues with analysis of unstructured text using SAS Visual Text Analytics software. This pipeline can be illustrated with data from the Big Ideas talk series at SAS, which gives employees the opportunity to share their ideas in short, TED Talk-type presentations that are recorded on video | machine learning, unstructured data, proc freq, parsing | source | 8 |
| 279 | Statistical Programming in the Pharmaceutical Industry: Advancing and Accelerating Drug Development | Harper Forbes | In pharmaceutical product development, statistical programming and the analysis of clinical trial data has always played a key role in helping to deliver medicines to patients. Over the last decade, the industry landscape and analysis needs have changed dramatically. Most organizations have shifted from a one-size-fits-all approach to personalized healthcare, where genetics and other biological information influence individual treatment. Clinical trial design and analysis has become more innovative-and complex-with the use of synthetic control arms, patient reported outcomes, real-world evidence, and biomarker data. Industry data standards under CDISC have created guidance for most data sources but novel endpoints might require additional considerations. Statistical programmers and analysts need to ensure that data is FAIR-findable, accessible, interoperable, and reusable; this is critical to the longevity of an organization who prioritizes their data as an asset | statistical programmer, clinical trial, clinical research, clinical programming, clinical data, pharmaceutical industry, adverse events, data science, data cleaning, cdisc | source | 10 |
| 280 | Steer Your Hybrid SAS Viya/SAS9 Ship Toward the 'Governed Data' Port | Bogdan Teleuca | Imagine you are in charge of a ship marked SAS Viya on starboard and SAS9 on port. The water is data. Navigation challenges: stay afloat, avoid the rocky shores of regulatory scrutiny, and circumvent the shallow waters of customer complaints. This session guides you safely toward the "Governed Data" port. Learn when to steer to SAS9, and use SAS Business Data Network as a chart and SAS Lineage as your tracking system. Find out which SAS Data Management REST APIs and command-line interface scripts can give you the extra steering. Turn to SAS Viya and accelerate data processing, preparation, and discovery. Improve decisions made with SAS Intelligent Decisioning. Enter key data governance events in your ship's log, a SAS Cloud Analytic Services (CAS) table. Watch key indicators on your SAS Visual Analytics dashboards. Learn the most useful CAS actions and SAS Viya REST APIs. Finally, understand the protection and remediation kits available, in case of an emergency | data validation rules, sas viya, rest api, cas action, data governance, proc step | source | 15 |
| 281 | Step-by-Step SQL Procedure | Charu Shankar | PROC SQL is a powerful query language that can sort, summarize, subset, join, and print results all in one step. Users who are continuously improving their analytical processing will benefit from this hands-on workshop. In this paper, participants learn the following elements to master PROC SQL: 1. Understand the syntax order in which to submit queries to PROC SQL 2. Summarize data using Boolean operations 3. Manage metadata using dictionary tables 4. Join tables using join conditions like inner join and reflexive join 5. Internalize the logical order in which PROC SQL processes queries |
proc sql, proc step, group by, dictionary tables, order by, inner join, where statement, index | source | 17 |
| 282 | Stepping Up Your SAS Output Delivery System Graphics Game | Sara Richter | Do you want to create eye-catching and informative data displays, but don't have access to SAS Viya or SAS Visual Analytics? Not to worry! With a little bit of extra work and ingenuity, you can use SAS Output Delivery System (ODS) Graphics, available in Base SAS, to turn mediocre data displays into high-impact communication tools. This paper provides ideas for combining tables, charts, and formatting elements to step up your ODS Graphics game. It demonstrates how SGPLOT procedure output can be combined with REPORT procedure output into a single visual element and gives examples of how SG annotate functions can enhance standard graphs. These code examples will empower the applied statistician to create more advanced data visualization elements |
proc step, proc sgplot, proc report, bar chart, sg annotation, data visualization, sas base, line chart, user-defined format | source | 28 |
| 283 | Survey Data Analysis Made Easy wth SAS | Melanie Dove, Katherine Heck |
Population-based, representative surveys often incorporate complex methods in data collection, such as oversampling, weighting, stratification, or clustering. Analysis of these data sets using standard procedures (such as the FREQ procedure) results in incorrect estimates and might overstate the statistical significance of results due to the complex survey design factors. However, SAS survey procedures, such as the SURVEYFREQ and SURVEYMEANS procedures, make it easy to adjust for the complex sample design and weighting of representative surveys. This hands-on workshop (HOW) provides an overview of complex survey design and explains how SAS survey procedures can adjust for complex survey design factors. Attendees learn how to easily generate accurate frequencies, percentages, means, and odds ratios from survey data sets using SAS survey procedures |
sample, proc step, confidence interval, proc calis, proc surveyfreq, proc surveymeans, proc freq, nominal variable, cluster sampling, proc surveylogistic, proc surveyreg | source | 8 |
| 284 | Surviving the Cox Proportional Hazards Model with the POWER Procedure | Rachel R. Baxter | Prior to the release of SAS/STAT 14.2, power analyses for survival methods were immured to a single option- TWOSAMPLESURVIVAL - within the POWER procedure. The weakness of TWOSAMPLESURVIVAL is its inability to address the aspects of survival analysis like the Cox proportional hazards mod el. Modeling is a key tool toward understanding this association of a given number of covariates with an outcome and thus requires a high level of statistical power. Statistical power informs researchers of the probability of their study predicting a conclusion to be true when in fact it should be false, which is called a type II error. The appropriate sample size ensures a low probability of a type II error equating to a high level of statistical power. With the new features in SAS/STAT 14.2, researchers are now able to conduct power analyses for the Cox proportional hazards model in survival statistics by using the COXREG option in PROC POWER |
sample size, power analysis, sample, proc power, survival analysis, proc step, hazard function, type ii error, proc phreg | source | 6 |
| 285 | Synchronized Multivariate Resampling to Designated Distribution and Population Level with PROC SURVEYSELECT | Zhiyong Chen | PROC SURVEYSELECT is a powerful SAS procedure for random resampling. With SAMPSIZE and STRATA option, the population level can be altered in the resampled data for designated variables. To further extend the function of PROC SURVEYSELECT, we developed an innovative approach which can perform synchronized multivariate resampling with PROC SURVEYSELECT. The approach first prepares a cross-bin flag through crossing all involved variables, then calculates the expected percent for each level of the created cross-bin flag by crossing the designated percent of each level in each involved variable. Based on the derived percent, the sample number for each cross-bin level is calculated, and finally applied in PROC SURVEYSELECT for resampling |
proc step, sample, proc surveyselect, normal distribution, proc freq, proc sql | source | 10 |
| 286 | Take Control of Your Data! | Diane Hatcher | As organizations embrace analytics, data is increasingly taking center stage in the modern enterprise. Users are finding innovative ways to build analytics into their daily processes, using data from warehouses, data lakes, and external sources to feed into their models. Often, the data sets and insights they fuel are stored in sandboxes and personal workspaces, inaccessible to others that might benefit from them. The proliferation of these data islands inhibits collaboration and creates storage and governance concerns | data lineage, data wrangling | source | 11 |
| 287 | Teaching SAS Coding with Self-Checking Exercises | Jared Dean | Programming is an essential skill for many people and for most STEM jobs. Teaching students to code is a standard part of statistics and computer science curriculums. In my opinion, students need individual hands-on practice to master the skill of writing code regardless of the programming language. Classroom lectures help explain concepts and design patterns, but they cannot substitute for students spending time practicing. Designing meaningful practice with timely feedback is difficult for faculty. Manual grading is either non-specific or has a significant lag between assignment completion and student feedback. In this paper, I outline how professors can teach SAS using SAS Analytics Cloud with self- grading assignments to help the students get immediate feedback on programming exercises. This case study leverages SAS Analytics Cloud and Jupyter Notebooks along with the SAS kernel and nbgrader | proc compare, proc step | source | 11 |
| 288 | Tell Me A Different Data Story | Kat Greenbrook | The easiest data story to write involves an event- something in real life- changing over time. This change can be measured with the right data metric, comparing its value before and after the event. The more dramatic the change, the more interesting the data story. But insight projects are usually more complicated than a single event. Many involve the analysis of multiple events or the output of different segmentation profiles. This insight also needs to be effectively communicated to initiate change. This paper builds on knowledge in the paper Tell Me A Data Story (3168-2019), Kat presented at SAS Global Forum 2019. This paper explains how to write different data stories, depending on the insight you have to communicate, and how to effectively tell them using data visualization. All of these learnings can be applied to SAS Visual Analytics to enhance your data storytelling | data visualization, data storytelling | source | 7 |
| 289 | Ten Minutes to Your First Hello World: REST APIs | Andy Bouts | Like many other software applications, your time to first Hello World (TtFHW) with the SAS Viya REST APIs should be seamless and simple. The goal of this paper is to demonstrate with a standard workflow that the SAS Viya playbook works out of the box. Then, once you have used the playbook, you'll be enabled to envision how you can extend it for your use cases. The playbook uses standard Python modules to connect to the SAS Viya REST APIs and is designed with easy to read variable declarations and helper functions for ease of use. The GitHub project includes all dependencies and is designed to work with SAS Viya 3.4 or 3.5 | sas viya, rest api | source | 10 |
| 290 | Testing Hypotheses for Equivalence and Non-inferiority with Binary and Survival Outcomes | Joseph C. Gardiner | The classic superiority test of comparison of two treatment groups seeks to show that they differ on a measure of their efficacy. However, whe n a new treatment has a similar therape utic action to an exist ing standard treatment, there might b e little difference in their efficacy. An equivalence study is designed to show t hat the diffe rence in a measure of efficacy between the new and standard treatments is within a pre-specified margin of clinical indifference. In another context, the new treatment may offer lower cost and/or better patient compliance but might have a lower efficacy than the standard treatment. A non-inferiority study is designed to show that the new treatment is not less effective than the standard treatment to within a pre-specified margin of clinical indifference. In this paper, we discuss the formulation of tests of hypotheses for equivalence and non-inferiority studies in the context of a two-sample design for binary and survival outcomes | sample, proc step, equivalence testing, odds ratio, relative risk, confidence interval, proc freq, hazard ratio, survival function, likelihood function, sample size, proc power, proc calis, type i error, alternative hypothesis, normal distribution, null hypothesis, proc phreg, hypergeometric distribution, proportional hazards assumption, superiority, binomial distribution | source | 23 |
| 291 | That's How I Like It: Personalize Reports in SAS Visual Analytics Apps for iOS, Android, and Microsoft Windows 10 | Cheryl L. Coyle | The report you are viewing on your phone is both beautiful and informative. You can drill, filter, and sort the data, but there's a burning question you need to answer. If only you could make a few modifications to the report, you could gain the insight you are searching for. Viewer customizations, a collection of capabilities recently introduced in SAS Visual Analytics, puts the power in the hands of report viewers to gain their own insights. You can personalize your report-viewing experience and answer the questions that used to lie just out of reach. Customize filters, groups, and ranks for your data to get a different view and gain new insights. Change data items and visualizations to dig a bit deeper. While viewing your report, you can make decisions more quickly by customizing the report according to your needs-no need to bother the report designer with change requests | bar chart | source | 21 |
| 292 | The 4 Ws: A Strategy Session on SAS Customer Intelligence 360 Personalized Consumer Experiences | Jaclyn Coate | Now more than ever, consumers are looking for brands, products, and experiences that fit their wants and needs. They no longer take the first product offered to them. With information at their fingertips, they can research and make more knowledge decisions regarding their purchases. Marketers are faced with the challenge of answering the four Ws: who, what, when, and where. Who is your target consumer? What are they going to purchase? When are they going to purchase? Lastly, where is the best channel to make and/or continue contact? Marketers are responsible for strategizing, creating, and executing complex, omni-channel personalized campaigns for their consumers. SAS Customer Intelligence 360 contains a multi-module system that enables marketers to accomplish not only campaign execution across connected marketing channels, but also provides actionable insights and continued campaign optimization | rest api, machine learning | source | 10 |
| 293 | The Curious CAT (Q, S, T, X) Functions | Jinson J. Erinjeri, Saritha Bathi |
The CAT family of functions CAT, CATQ, CATS, CATT, CATX in SAS are very useful in concatenating strings. These functions not only join strings, but also serve to write compact codes with an additional advantage of easier discernment of the same. The CAT family attain these positives by eliminating the need of complex and extended logics associated with multiple functions. In this paper, we present the various CAT functions with relevant examples of their application. The main objective of this paper is to remove the curiosity of the CAT family and encourage the programmers to add these functions as a must in their toolbox | character functions, catq | source | 9 |
| 294 | The Essentials of SAS Dates and Times | Derek P. Morgan | The first thing you need to know is that SAS software stores dates and times as numbers. However, this is not the only thing you need to know. This paper is designed to give you a solid base for working with dates and times in SAS. It introduces you to functions and features that enable you to manipulate your dates and times with surprising flexibility. This paper shows you some of the possible pitfalls with dates (and times and datetimes) in your SAS code and how to avoid them. We show you how SAS handles dates and times through examples, including the ISO 8601 formats and informats, and how to use dates and times in TITLE and FOOTNOTE statements. The paper closes with a brief discussion of EXCEL conversions |
sysdate9, date variable, sample, excel, macro variable, proc format, user-defined format, missing value, proc step, error message, character variable, input statement, numeric variable, time, flat file, time., macro function | source | 25 |
| 295 | The Face Behind That Fraud: Humanizing Data | Eva Velasquez, Charity Lacey, John Watkins, Jared Peterson |
Over $905 million in total fraud losses in 2017 & there is a human behind every record. Businesses must do all they can to protect new & existing customers. When that fails, ultimately someone has been impacted - whether it's a breach of organizational data or a theft of someone's identity to commit a crime. The Identity Theft Resource Center, a victim assistance non-profit, aids anyone with a U.S. identity credential by providing access to the information & guidance they need - whether it's emotional or financial. The impact of the increase in data breaches, exposing over 147.4 million sensitive records from January to September 2019 alone, is astronomical. As the ITRC sees an influx of calls & live-chats, the strain on the existing resources available to assist consumers increases exponentially | service provider, credit card | source | 5 |
| 296 | The Hack-a-saurus Guide to SAS Grid: Saving Time with Parallel Database Queries | Ted D. Williams | Have you ever worked overtime or missed a deadline because it took hours (even days) to extract the data you needed from multiple sources? And heaven help us if there is a flaw in the query, a change in data structures, new customer requirements, or a execution error after 12 hours of runtime. But a new dawn has broken; SAS Grid supports parallel processing. SAS Grid enables a single program to query multiple data sources in parallel making retrieval and aggregation as simple as F3. Unlike other parallel processing solutions, SAS Grid does not require learning a completely new syntax (I'm looking at you, DS2). Unfortunately, it is not a simple as standing up a SAS Grid server and running Enterprise Guide 'Analyze for Grid'. Do you live in the WORK library? Sorry, no longer shared. Like global variables? Too bad, those break too. Do you use macros? Tough luck, SAS Grid doesn't know about your complied macros | sas grid, parallel processing, temporary dataset, macro definition, work library, sas enterprise guide | source | 12 |
| 297 | The History and Evolution of SASPy, Including an Overview of What It Can Do and How to Use It | Tom Weber | SASPy is one of the most popular SAS open-source packages, available on the SAS GitHub site (https://github.com/sassoftware/saspy) and also installable via Python Package Index (PyPI) and Anaconda. But, how did it come to be? How and why did SAS create an open-source Python interface to your existing SAS 9.4 systems, and without even needing any updates to your existing SAS servers for it to be able to connect to them? Often, having an understanding of the design criteria and constraints of a software package helps you better understand and use it, so you can be more productive in what you are really trying to accomplish. This session blends in this insight while giving an overview of SASPy, including the various ways it can connect to your different SAS installations, how to configure it for each one, a walk-through of the core functionality, and a look at some of the more advanced features | cpu time, sas 9, sas viya | source | 12 |
| 298 | The Increasing Use of Artificial Intelligence in the Intelligence Community | Gordon Robinson | The management of intelligence data within a law enforcement environment has traditionally been a very manual process. Reviewing the contents of intelligence reports and creating structured records has traditionally been the responsibility of intelligence professionals. With the massive increase in the amount of data being processed and the advancements in technology, the intelligence community is increasingly turning to artificial intelligence to help automate some of the tasks. This paper discusses some of the areas of progress and some of the challenges being faced | artificial intelligence, entity resolution | source | 9 |
| 299 | The New solveBlackbox Action in SAS Optimization 8.5 | Edward P. Hughes | In SAS Viya 3.5, SAS Optimization 8.5 introduces the solveBlackbox action. This action greatly expands both the means of access to this essential optimization technique and the range of its potential applications. Black box optimization uses innovative methods to find solutions to some of the most challenging optimization problems, in which the functions involved might be nonsmooth, discontinuous, and computationally expensive to evaluate. You can call the solveBlackbox action from programs in the CASL, Python, Lua, Java, and R languages. And because this action uses CASL to define objective functions, there is almost no function it cannot work with; any CASL script can become an objective function of the solveBlackbox action. In addition, this action makes extensive use of multilevel parallelism, enabling you to make the most effective use of distributed computational resources. Distributed computational tasks that are called by this action can in turn call other distributed tasks | odds ratio, regression analysis, proc step, machine learning, cas action, predictor variable, linear programming, raw data | source | 14 |
| 300 | The Past, Present, and Future of Training SAS Professionals in a University Program | Jonathan W. Duggins, James E. Blum |
University students who are fortunate enough to encounter SAS typically do so in a variety of courses and contexts. A few programs offer courses designed specifically to build SAS programming fundamentals before moving on to courses with specific applications. However, many curricula merely include offerings that target the applications of immediate interest without building a broad skill base to prepare students for careers that rely heavily on programming. This session discusses past and current practices at several institutions, the philosophies behind them, and where they succeed and fail. As SAS and its role in the marketplace evolve, a university curriculum must evolve with it. Therefore, this session closes with a discussion of how SAS and universities can work together to provide the training necessary to continue preparing high-quality programmers | proc step, regression analysis, sas programming, anova, proc genmod | source | 6 |
| 301 | The Plight of the Honeybee | Rachael Bishop | A decline in the honeybee population is raising concerns worldwide. Honeybees are an important part of agricultural industries. Is it possible that the neonicotinoid pesticides used to protect crops from damaging insects are also harming the insects necessary for pollinating the same crops? This study explores the effects of these pesticides on honeybees by comparing annual honey production yields and honeybee colony counts in the United States from 1995 through 2015 | proc hpsplit, response variable, binary variable, data mining, regression analysis | source | 8 |
| 302 | The SAS Encoding Journey: A Byte at a Time | Mickael Bouedo | UTF-8 is becoming the most dominant character encoding for the World Wide Web. It supports a great number of characters from many languages, including English, and is compatible with ASCII characters. While all characters are one byte in WLATIN1, the same familiar characters might take several bytes in UTF-8. This major difference in data representation imposes several challenges for SAS programmers. Transcoding errors, truncation, or garbage characters such as � might appear unexpectedly when your data is processed. To help you in this endeavor to overcome these challenges, this paper explains the basics of character encoding and how it is handled in SAS. It defines some common terms such as ASCII, single-byte character set (SBCS), multi-byte character set (MBCS), and Unicode. It also introduces SAS functions and macros that are available to detect potential problematic characters and to make it easier to fix the problem | error message, proc contents, proc step, sas viya, proc print | source | 15 |
| 303 | The Secreit to Finding the Right Talent in Tech: Why Artists Can Be Your Greatest Asset | Miranda Wickett | We are all looking for talent. Talent who will stay with our companies and help them grow. We are looking for great cultural fit- people who communicate well, work hard and get the job done. If you think finding people with more STEM is your answer, you might want to shift your perspective. Learn why and how dancers, actors, artists and performers will change the face of your team for the better. We will consider how empathy, teamwork and communication skills are developed through the arts and how they can help your existing technical team of developers, analytics professionals and managers grow. We will outline why adding people with significant arts training can help your team thrive, bring unique perspective and disrupt the 'way things have always been done'. We will also consider what creative, active learning looks like in a traditional business classroom and why you should ask about learning styles and varied experience while recruiting | source | 13 | |
| 304 | The Secret to Successful CECL Model Implementation | Monica Wang, Jackie Yanchuck |
The deadline is coming, the deadline is coming! Are your Current Expected Credit Loss (CECL) models ready to go? Financial institutions are required to have CECL models implemented as soon as 2020 or as late as 2023. Whether your model development process just started or your models have been finalized for a while now, the implementation process is still a large project to take on. This process can be just as long and complex as the model development process. So what's a financial institution to do? Fortunately, this paper is written by two model implementation experts. They have seen a wide array of models implemented for a wide range of financial institutions, and they're here to give you the tips and tricks for the most successful model implementation possible, using SAS Model Implementation Platform (MIP) component of the SAS Expected Credit Loss (ECL) Solution | transition matrix, data wrangling, data cleaning | source | 5 |
| 305 | The Seven Most Popular Machine Learning Algorithms for Online Fraud Detection and Their Use in SAS | Patrick Maher | Today, illegal activities regarding online financial transactions have become increasingly complex and borderless. This unlawful activity results in substantial economic losses for both customers and organizations. Many techniques have been proposed for fraud prevention and detection in the online environment. All these techniques have the same goal of identifying and combating fraudulent online transactions. However, each machine learning technique comes with its characteristics, advantages, and disadvantages. This session reviews the use of the most common machine learning algorithms used in online fraud detection, the strengths and weaknesses of these techniques, and how these algorithms are developed and deployed in SAS. Types of fraud discussed include credit card fraud, financial fraud, and e-commerce fraud. Algorithms reviewed include neural networks, decision trees, support vector machines, K-nearest neighbor, logistic regression, random forest, and naïve Bayes | machine learning, proc hpsplit, false discovery rate, outlier, neural network, proc hpforest, support vector machine, regression analysis, logistic regression, response variable, training set, logistic distribution, sample, predictor variable | source | 6 |
| 306 | Think Globally, Act Locally: Understanding the Global and Local Macro Symbol Tables | Michelle Buchecker | One of the most missed questions on the Advanced SAS Certification exam during Beta testing dealt with global and local macro variables. Even people who got nearly every other question right missed these questions. In this paper, you learn when a local symbol table gets created, when a macro variable gets placed in the local table, what happens if you have a macro variable by the same name, and why does any of this matter. %LET, CALL SYMPUT, CALL SYMPUTX, and the INTO clause in SQL each have their own rules for addressing how they interact with the local and global tables. Techniques for debugging and displaying symbol tables are also discussed. Learn them here, live them when you leave |
symbol table, macro variable, local symbol table, global symbol table, call symput, call symputx, macro definition, missing value, macro put, proc sql, proc step | source | 11 |
| 307 | Three Reports on a Page and 4-Page Layouts on the Fly with SAS and the Output Delivery System | Derek P. Morgan | This paper details the creation of a ready-to-publish document with multiple data sources and differing page layouts of the same type of information based on page fit. Each set of information is placed on one, two, or three pages depending on fit, and requires three calls to the REPORT procedure using three different data sets. This solution does not involve ODS DOCUMENT. Instead, we calculate how much horizontal and vertical space the report requires and use the macro language to execute code conditionally. This allows us to dynamically determine which page layout to apply, including setting or suppressing page breaks with ODS STARTPAGE=. This solution makes extensive use of inline formatting as well. The output was produced as an RTF file using SAS University Edition running on a Microsoft Windows 10 machine |
proc report, proc step, macro variable, proc sql, character variable, macro loop, excel | source | 22 |
| 308 | Time After Time: D-I-D and ITS Models in SAS | Margaret Warton | Healthcare and other epidemiological researchers are increasingly turning to difference-in-differences (D-I-D) and interrupted time series models (ITS) to analyze pre- and post- changes in outcomes around an intervention or exposure. These models are often used in quasi-experimental studies with non-randomized exposures using retrospective observational data, and they allow a causal interpretation and adjustment for secular trends in the outcome of interest. D-I-D models compare the rate of change an outcome measure before and after an exposure in exposed and control groups based on a single measure in each period. D-I-D data can be modeled with repeated-measures generalized linear models with an interaction term between time period and the exposure variable. The ITS - an extension of the D-I-D design - compares trends in an outcome over multiple pre- and post- time period measures, allowing for a discontinuity (an "interruption") in both control and exposure average rates during the study period. Models of this type account for situations where rates of an outcome of interest shift in both exposed and control groups at a specific time point, such as the change from ICD9 to ICD10 diagnosis codes during 2015. We will discuss the statistical basis of these methods and illustrate data structure, modeling methods, power calculations and interpretation of model estimates for both models with data from studies performed in a large integrated healthcare system. | propensity score, interaction effect, time series analysis, proc step, repeated measures, correlation, relative risk, sample, proc mixed, random intercept, trend analysis, time variable, regression analysis, proc genmod, sample size, anova, binomial distribution, poisson distribution, binary variable | source | 21 |
| 309 | Time Is on Your Side. Yes It Is. Using Base SAS to Manipulate Time | Carole Jesse | Time can be tricky. In relational database management systems (RDBMS) there can be many time-based (temporal) components (for example, DATETIME, date, and time fields). They are typically stored as numeric data types in the native RDBMS format for temporal components. Sometimes they are stored as character fields in the RDBMS. The way that the SAS software interprets and reads temporal components depends on several things. The challenge of reading and converting them into useful variables for data analysis in SAS requires some finesse, but any SAS programmer or analyst can master this with a few tips and tricks. This presentation helps you understand how temporal components are stored and formatted in RDBMS and subsequently how to take advantage of all that Base SAS has to offer for working with temporal components |
sas base, proc sql, format statement, proc step, create table, sysdate9, sas function, excel | source | 16 |
| 310 | Tips for Building Rich Interaction in Your SAS Visual Analytics Reports | Jeanne Marie Tan, Sierra Shell |
Your data has a lot to say. How do you make these insights available to your audience? Enhance the report viewing experience by taking advantage of features in SAS Visual Analytics to build reports that engage your audience in exploration. This paper highlights features including automated analytical objects, object interactions, drilling h ierarchies, as well as report and page controls. The paper also describes techniques to make the most out of these features. With these tips, you can create an interactive report that helps you and your audience dig deeper into the data to gain insights | bar chart, response variable | source | 11 |
| 311 | Tips for Writing Custom SAS Studio Tasks That Use CAS Actions for Advanced Analytics | Brian R. Gaines | The SAS Studio programming interface includes point-and-click user interfaces, called tasks, that enable you to use SAS software without writing any code. As you navigate a task's menus and options, the underlying SAS code is automatically generated for you in real time, making SAS analytics accessible even to users who are not familiar with SAS code. SAS Studio tasks cover a wide range of analytical areas, but you can also create custom tasks to suit your needs. For example, the code that a task generates can include the CASL programming language that you can use to interact with SAS Cloud Analytic Services (CAS). CAS is a cloud-enabled, in-memory analytics engine from SAS that uses distributed computing for highly scalable, very fast execution. Because CASL is a relatively new language, custom tasks that generate CASL code make it easier for you to use CAS actions and learn the CASL syntax as you migrate your analytics to CAS | covariance, machine learning | source | 16 |
| 312 | Tips, Traps, and Techniques in BASE SAS for vertically combining SAS data sets | Jayanth Iyengar | Although not as frequent as merging, a data manipulation task which SAS programmers are required to perform is vertically combining SAS data sets. The SAS system provides multiple techniques for appending SAS data sets, which is otherwise known as concatenating, or stacking. There are pitfalls and adverse data quality consequences for using traditional approaches to appending data sets. There are also efficiency implications with using different methods to append SAS data files. In this paper, with practical examples, I examine the technical procedures that are necessary to follow to prepare data to be appended. I also compare different methods that are available in BASE SAS to append SAS data sets, ba sed on efficiency criteria | proc step, proc append, proc sql, set statement, proc compare, proc contents, sas base, cpu time, dataset, sas log, proc freq, data cleaning, sas programmer, proc datasets, length statement | source | 18 |
| 313 | Transdermal Medicine - Challenges and Solutions with SAS | Marina Komaroff | In 21 century Transdermal Drug Delivery System (TDDS or TDS) has become the fastest growing division in pharmaceutical industry because of innovative technologies and various advantages over the oral medicine. Fast growth came along with multiple challenges . The challenging dermal safety studies must be con ducted to determine transdermal system performance, like adhesion, discomfort, irritation, and sensitization. Non -inferiority tests are also required to justify acceptable adhesion and irritation of a new product in comparison to an active reference. The rationale for data analyses and clinically meaningful choices for non- inferiority margins is still an area of research and development. This paper concentrates on the analyses and reporting of transdermal data utilizing SAS | sample, sample size, null hypothesis, proc step, treatment effect, proc ttest, data science, confidence interval, proc mixed, adverse events, equivalence testing, pk parameter, standard deviation, clinical endpoint, proc freq | source | 14 |
| 314 | Transfer Learning for Mining Digital Phenotype by SAS Viya | Satoki Fujita | With the remarkable development of various AI and machine learning methods in recent years, various advanced technologies based on collected data have been born in each industry. One of the technologies represented by them is deep learning. Image recognition, speech recognition, and natural language processing are well-known usage applications for deep learning. Computers automatically extract important features that affect the results from data without human intervention. This method achieves recognition and identification accuracy that is much higher than that of the method, and is not inferior to humans | deep learning, training set, sas viya, neural network, heatmap, accuracy, clinical research, big data | source | 18 |
| 315 | Translating SQL to SAS and Back: Performing Basic Functions Using DATA Steps vs. PROC SQL | Katie Haring | Many beginning SAS software users know how to write SQL code or SAS code, but not both. This quick tip is designed to teach users how to complete basic data manipulation and visualization processes using both DATA steps and PROC SQL |
proc sql, proc step, create table, where statement | source | 6 |
| 316 | Tricks of the Trade: Streamlining and Enhancing Your SAS Sessions | Debra A. Goldman | We are all here today because we have chosen SAS; coding in SAS is likely a part of daily life, whether as programmers, database managers, or statisticians. Some of us with options at our companies have chosen SAS over other coding languages, such as R or Python. SAS is incredibly powerful and flexible; its PROCedures are validated and its code tested. It provides an impressive amount of options within PROCedures, and by default, the output contains all the relevant information for model fitting, statistical tests, and parameter estimates. Unfortunately, SAS, by design, requires more code to be written to perform the same operation compared to other languages and much of the output is valuable only for review and not necessarily for reporting. We live and die by the semi-colon, run statements, and procedure calls. From my R user colleagues, the sheer differential in writing is enough to turn them off. However, coding in SAS need not be so cumbersome and reporting so tiresome | proc step, proc report, correlation, proc corr, spearman correlation, %if-%then-%else | source | 11 |
| 317 | Troubleshooting Encoding Issues When Integrating Data from Multiple Systems | Lydia Sbityakov, Breanna Barton |
Symptoms, diagnoses, and cure for encoding issues: When integrating multiple data sources in a real-world environment, especially with legacy databases, it is not uncommon to encounter data sets with different encodings. What types of errors can this cause? How can you recognize that what you are seeing is a result of mixing various encodings? What are some of the methods that can be used to determine what the native encoding of your data sources and SAS session are? This session walks through troubleshooting a real-life example and its resolution | sas enterprise guide, libname statement, sas library, dataset, proc step | source | 10 |
| 318 | Turn Yourself into a SAS Internationalization Detective in One Day: A Macro for Analyzing SAS Data of Any Encoding | Houliang Li | With the growing popularity of using Unicode in SAS, it is more and more likely that you will be handed SAS data sets encoded in UTF -8. Or at least they claim those are genuine UTF -8 SAS data. How can you tell? What happens if you are actually required to generate output in plain old ASCII encoding from data sourc es of whatever encoding? This paper provides a solution that gives you all the details about the source data regarding potential multibyte Unicode and other non-ASCII characters in addition to ASCII control characters. Armed with the report, you can go back to your data provider and ask them to fix the problems that they did not realize they have been causing you. If that is not feasible, you can at least run your production jobs successfully against cleaned data after removing all the exotic characters | character variable, proc print, dataset, sas log, proc step, sas viya, proc contents, excel, macro loop, note, macro definition | source | 16 |
| 319 | Turning the Crank. A Simulation of Optimizing Model Retraining | David R. Duling | Model retraining is a common practice in the advanced model life cycle. However, the critical question is how do you know when you need to retrain the model? Once the model is retrained, how do we determine when we need to redeploy the model? Can we predict how long the model will be relevant? The answers can depend on one or more of many factors including calendar fluctuations, business cycles, data drift, model performance, expected benefit, and many others. Given those factors, we want to find the optimal points in time to retrain and redeploy a predictive model. This paper presents a simulation study of different strategies and techniques for optimizing model retraining with the goal of maintaining optimal business performance | sample, model monitoring, proc hpsplit, sensitivity analysis, sas model manager, proc step, training set, correlation, logistic regression, regression analysis, model training, score code, response variable, logistic distribution | source | 17 |
| 320 | Twenty Ways to Run Your SAS Programs Faster and Use Less Space | Stephen B. Sloan | When we run SAS programs that use large amounts of data or have complicated algorithms, we often are frustrated by the amount of time it takes for the programs to run and by the large amount of space required for the program to run to completion. Even experienced SAS programmers sometimes run into this situation, perhaps through the need to produce results quickly, through a change in the data source, through inheriting someone else's programs, or for some other reason. This paper outlines twenty techniques that can reduce the time and space required for a program without requiring an extended period of time for the modifications. The twenty techniques are a mixture of space-saving and time-saving techniques, and many are a combination of the two approaches. They do not require advanced knowledge of SAS, only a reasonable familiarity with Base SAS and a willingness to delve into the details of the programs | proc step, dataset, proc sort, numeric variable, by group processing, proc means, character variable | source | 5 |
| 321 | U-can-NIX Leaving Your SAS Session to Do That | Lorelle Benetti | SAS programmers often play the role of "electronic storage space steward," requiring them to monitor the electronic space their programs and data sets take up as well as the electronic space their colleagues utilize. With tight deadlines, it's easy for programmers to write programs or create data sets without putting a lot of thought into where the data sets are stored or even how large those data sets are. As a result, electronic space and organization can quickly get out of hand. This presentation provides examples of how to utilize UNIX commands to locate files that you may be able to delete in order to free up electronic space. For example, data sets with "old" or "temp" at the end of the file name are typically intermediate data sets before a final data set is created or updated. The presentation also provides examples of reading in files, such as EXCEL files, that are not SAS files by using the UNIX FILENAME PIPE command and utilizing FIND - MAXDEPTH to monitor electronic space |
proc step, dataset, proc report, excel | source | 13 |
| 322 | Unlock the Business Value of IoT with Analytics | Jon Klopfer | The impact of Internet of Things (IoT) Analytics might be greater-and taking hold more quickly-than many observers expect. Organizations that are pursuing an IoT strategy are finding they can't compete effectively without using analytics. Despite the importance of IoT Analytics, many organizations lack a clear vision to execute their initiatives. According to a recent study by PWC, "more than six out of ten organizations have failed to take operational IoT initiatives past proof-of-concept stage or beyond implementation." This paper provides an example IoT setup using SAS software, open source software, and a Raspberry Pi to build a project that can be utilized for demonstrating IoT Analytics | flat file, sample, data cleaning, sas viya | source | 19 |
| 323 | Unsupervised Contextual Clustering of Abstracts | Jacob Noble, Himanshu Gamit |
This study utilizes publicly available data from the National Science Foundation (NSF) Web Application Programming Interface (API). In this paper, various machine learning techniques are demonstrated to explore, analyze and recommend similar proposal abstracts to aid the NSF or Awardee with the Merit Review Process. These techniques extract textual context and group it with similar context. The goal of the analysis was to utilize the Doc2Vec unsupervised learning algorithms to embed NSF funding proposal abstracts text into vector space. Once vectorized, the abstracts were grouped together using K-means clustering. These techniques together proved to be successful at grouping similar proposals together and could be used to find similar proposals to newly submitted NSF funding proposals. To perform text analysis, SAS University Edition is used which supports SASPy, SAS Studio and Python JupyterLab. Gensim Doc2vec is used to generate document vectors for proposal abstracts | proc princomp, dimensionality reduction | source | 7 |
| 324 | UPDATE to the Rescue | Robert Virgile | The UPDATE statement provides a unique method of combining two SAS data sets. That method is invaluable when needed; on the other hand, it is rarely needed. This paper explores a variety of ways to expand the usefulness of the UPDATE statement, making it the simplest solution to a broad variety of programming problems | missing value, update statement, master data management, sample | source | 9 |
| 325 | Upgrading Clinical Trial Reports from ODS LISTING to ODS TAGSETS.RTF | Christopher J. Smith, Joshua M. Horstman |
Several pharmaceutical and biotechnology companies are still using ODS LISTING as the primary method for producing outputs. While there is nothing inherently wrong with this approach, using either ODS RTF or ODS TAGSETS.RTF can provide more aesthetically pleasing outputs in the same font as the main clinical study report. In addition, it can provide a more harmonized approach between tables, listings and figures by having a file format compatible with graphics procedures. Lastly, it lays the foundation to have a more robust validation process by having the option to read the actual RTF file back into a SAS data set. This paper provides examples of typical clinical tables and listings and how to create them in the TAGSETS.RTF destination. Several commonly used options found within the REPORT procedure such as HEADLINE, HEADSKIP, SPLIT=, SKIP, and many others are only available in the LISTING destination |
proc report, proc step, clinical trial, ods rtf, missing value, adverse events, csr, system organ class, label statement, class variable, sas output, data cleaning, proc template | source | 25 |
| 326 | Urge to MERGE? Maybe You Should UPDATE Instead | Ben Cochran | The DATA STEP's UPDATE statement is similar to the MERGE, but it has some helpful built -in logic of which many users of SAS may not be familiar. In m ost cases, this built in logic can yield much simpler DATA steps. This paper sheds light on some of these build-in features and takes a step-by -step approach to showing you how to take advantage of this power that is already there |
update statement, missing value, merge statement, dataset | source | 7 |
| 327 | Use SAS to Model Non-Linearity | Xiaoting Wu, Donald S. Likosky |
The relationship between an outcome and a continuous predictor is often not linear. However, linearity is one of the assumptions for a multivariable regression model. Many options, such as transformation and restricted cubic splines, are available to handle non-linear relationships; however, these models are often hard to interpret. Linear spline is a simple approach to account for non-linearity and can provide interpretable results. This paper illustrates the use of linear splines to describe the relationship between a continuous variable and a binary outcome in a regression model. We used the relationship between hematocrit and blood transfusion as an example. Low hematocrit, a continuous measurement of volume percentage of red blood cells in whole blood, is known to be associated with blood transfusion in a clinical setting. We first assessed the non-linearity using the LOESS, GAM and SGPLOT procedures. We then constructed the linear splines using BASE SAS programming |
logistic distribution, interval variable, logistic regression, odds ratio, regression analysis, confidence interval, loess, proc step, covariance, proc gam, proc logistic | source | 12 |
| 328 | Using a Heat Map or the GTILE Procedure: Does Size Matter in Your Graphics? | Devi Sekar | With SAS 9.2 and beyond, ODS Graphics brings in a new way of generating high quality graphs. Many users still find themselves at the crossroads, trying to decide what path to follow - the traditional SAS/GRAPH or ODS Graphics. Both can produce most of the common types of graphs, such as scatter plots, regression and box plots, line graphs, bar charts, and histograms. In this paper we will share examples to generate a heat map using SGPLOT in ODS Graphics and a GTILE graph with SAS/GRAPH and discuss the advantages of each. With heat maps, you can display patterns in the data for a chosen response variable for one-dimensional data (in a map) or two-dimensional data (in a table or graph). Heat maps make tables and graphs easier to interpret, by shading the background color based on the frequency of observations in each cell of the graph or table. With GTILE, the response values are shown by both color gradient and area |
heatmap, graph template language, proc sgplot | source | 16 |
| 329 | Using a Microsimulation to Evaluate Population and Policy Changes on Program Integrity Priorities | Jonathan Smith, Meghan Beckowski, Michael Greene, Andrew Kemple, Bil Westerfield |
The Centers for Medicare & Medicaid Services (CMS) conducts Risk Adjustment Data Validation (RADV) to ensure the accuracy and integrity of data submitted for Medicare Advantage (MA) payments. CMS requires a simulation to forecast future program integrity trends. The RADV Synthetic Plan Microsimulation makes individual-level projections with a series of machine learning models and uses an agent-based simulation framework to forecast outcomes. SAS Viya and Python are used together in a streamlined framework, from data manipulation, to modeling, to simulation, and finally to visualization. Beneficiary data, including social determinants of health, are simulated with machine learning models such as enrollment events, health conditions, and payment error. Bootstrapping enables CMS to estimate variability in future payment error via point estimates and confidence intervals enabling CMS to compare various sampling strategies | sas viya, machine learning, confidence interval, bootstrap, proc step, dataset, by group processing, macro variable, sample, data cleaning | source | 15 |
| 330 | Using Analytics to Predict Tax Recovery and Prioritize Audits and Investigations in Canada | Jason A. Oliver | The Canada Revenue Agency (CRA) has made tremendous inroads in the last two years by leveraging the power of predictive analytics, notably by using web domain and E-Commerce data for corporate taxpayers. This session leverages the capabilities of SAS Enterprise Guide and SAS Enterprise Miner in unearthing predictive patterns of interest with the clear objective of strengthening a feedback loop between tax risk assessment and the corresponding accrual of tax via audit. We examine powerful data learning techniques, as they apply to tax-based analytics, such as neural networks, decision trees, and regression analysis | proc hpsplit, data cleaning, feature selection, training set, validation set, neural network, logistic regression, regression analysis, class variable, data mining, confusion matrix, logistic distribution, type ii error, type i error, sas enterprise guide, forward selection, maximum likelihood estimation, predictive analytics, akaike information criterion, correlation, likelihood function | source | 19 |
| 331 | Using Base SAS Code to Dynamically Load Data to the LASR Analytic Server | Andrew Gannon | This paper serves as an introduction to loading data into the LASR Analytic Server for use in Visual Analytics (VA) using SAS code. Many organizations have robust solutions built in SAS that create reports. Some of these reports are in output files such as XLSX or PDF, others are in the form of OLAP Cubes. As Visual Analytics is swiftly moving into many organizations as the premier reporting tool, the need to automatically and dynamically load data into the platform becomes more crucial. Currently many users utilize the Data Preparation tab inside of Visual Analytics to load their data in. The purpose of this paper is to show how to use base SAS code to dynamically load data into the LASR Analytic Server for use in the Visual Analytics environment | libname engine, sas base | source | 5 |
| 332 | Using Factor Analysis and MANOVA to Explore Academic Achievement in the 2016 Monitoring the Future Survey Data | Stefanie Kairs | The 2016 Monitoring the Future survey is part of an annual, long-term study of American adolescents and adult high school graduates conducted by the University of Michigan's Institute for Social Research. This secondary data analysis uses the FACTOR procedure in SAS Studio software to perform factor analysis to extract latent structures describing academic achievement, environment, and student delinquency. A total of 17,719 observations were used to perform multivariate analysis of variance (MANOVA) via the GLM procedure to explore the relationships between the extracted factors and demographic variables for ethnicity, gender, and population density. The SAS code and results are presented here, along with a discussion of the necessary data cleaning steps, data quality assessment, and post hoc analyses. Population density explains 2% (Pillai's trace = 0.022, p < 0.0001) of the variance in academic achievement, academic environment, and at-risk behaviors |
manova, anova, factor analysis, missing value, predictor variable, multivariate analysis, sas studio, null hypothesis, scree plot, post hoc test, proc factor, proc glm, multiplicity, proc genmod, data cleaning, proc princomp | source | 20 |
| 333 | Using Jupyter to Boost Your Data Science Workflow | Hunter Glanz | From state-of-the-art research to routine analytics, the Jupyter Notebook offers an unprecedented reporting medium. Historically, tables, graphics, and other types of output had to be created separately and then integrated into a report piece by piece, amidst the drafting of text. The Jupyter Notebook interface enables you to create code cells and markdown cells in any arrangement. Markdown cells allow all typical formatting. Code cells can run code in the document. As a result, report creation happens naturally and in a completely reproducible way. Handing a colleague a Jupyter Notebook file to be re-run or revised is much easier and simpler for them than passing along, at a minimum, two files:one for the code and one for the text. Traditional reports become dynamic documents that include both text and living SAS, R, Python or other code that is run during document creation. With Jupyter, you have the power to create these computational narratives and much more! | data science, sas log, sas studio, txt | source | 12 |
| 334 | Using Lag and Other Function in SAS to Create Final Datasets Temperature Study | Abbas S. Tavakoli | Temperature control is important for the health of premature babies. One way to monitor body temperature in premature infants is through continuous measurement of central (abdominal) and peripheral (foot) skin temperature. Fourteen premature infants, born ≤ 32 weeks gestational age and birthweights < 1500 grams were enrolled for study after Institutional Review Board approval and parental consent for participation in this study. Each infant had one skin temperature probe (thermistor) attached to their abdomen and one skin temperature probe to the sole of one foot. The data downloaded from incubator had several problems such as extra rows, missing value for several minutes, and so forth. The data for each days included about 86,400 rows. The final datasets for each infant included averaged data for every minute of all variables for 28 days. Lag and several functions in SAS were used to prepare data for analyses | data science, statistical software, dataset, excel, proc freq, histogram | source | 14 |
| 335 | Using Machine Learning and Demand Sensing to Enhance Short-Term Forecasting for CPGs | Kedar Prabhudesai | Consumer packaged goods companies (CPGs) account for some of the biggest industries in the world, providing essential items on a regular basis. Supply chain management at CPGs is complex because several products are supplied through multiple channels and d istribution methods. Products follow complex order patterns characterized by promotional events, seasonal influences, natural disasters, and so on. Given this complexity, it is crucial to generate accurate short -term forecasts of order quantities that refl ect the realistic demand for products. Such forecasts enable companies to drive an efficient supply chain response to improve customer service. This paper uses machine learning along with traditional time -series forecasting models to generate enhanced we ekly and daily forecasts by using historical -demand signal data and point-of-sale data. The model first creates enhanced weekly forecasts, and then breaks down enhanced weekly forecasts into daily forecasts | neural network, data cleaning, machine learning, standard deviation, proc forecast, proc step | source | 10 |
| 336 | Using Python with Model Studio for SAS Visual Data Mining and Machine Learning | Jagruti Kanjia | There are many benefits to using Python with Model Studio. It enables you to use the SAS Scripting Wrapper for Analytics Transfer (SWAT) package with the SAS Code node. It makes interacting with SAS Viya easier for nontraditional SAS programmers within Model Studio. It is used within a SAS Code node for deployable data preparation and machine learning (with autogenerated reporting). It enables you to use packages built on top of SWAT, such as the SAS Deep Learning Python (DLPy) package, for deep learning within a SAS Code node. It gives you the ability to call Python in an existing open source environment from Model Studio to authenticate and transfer data to and from native Python structures. It lets you use your preferred Python environment for either data preparation or model building and call it through a SAS Code node for use or assessment within a pipeline. It enables you to use Python with the Open Source Code node | macro variable, sas viya, response variable, cas, data cleaning, data wrangling, nominal variable, score code, supervised learning, feature importance, variable list, missing value, data step, deep learning, validation set, gradient boosting, machine learning, where statement, sample, dataset, training set, data transformation, histogram | source | 35 |
| 337 | Using SAS and R Integration to Manage and Create a Multilevel Complex Database: A Case Study of the U. S. Department of Defense Military Health System Data Repository | Akhtar Hossain, Nikki R. Wooten, Laura A. Hopkins |
As the most established statistical analysis system, SAS is widely used by researchers and programmers in diverse fields. R, an open-source statistical programming language, has widespread usage among small and medium enterprises as well as both researchers and programmers. Despite widespread use, both SAS and R have advantages and limitations. Consequently, integrating SAS and R provides an efficient solution to practical data management and analysis problems. Using the U. S. Department of Defense's Military Health System Data Repository, we present a case study integrating SAS and R to manage and analyze complex, multi-sourced administrative and medical claims databases. SAS code to utilize the R interface within SAS is also provided | data governance, data science, proc step, r programming | source | 6 |
| 338 | Using SAS Customer Intelligence 360 Multivariate Testing to Determine Incremental Campaign Revenue | Mia Patterson-Brennan, Kate Davies |
Do you want to determine which campaign would be the most financially successful at increasing revenue for each customer segment? Probably your business has different customer segments, but you want to determine which single campaign will be the most relevant at driving the most business value. Most companies want to identify their most impactful campaigns. Accenture found that "91% of consumers are more likely to shop brands who provide relevant offers and recommendations," but concluding which campaign resonates the most with your visitors can be overwhelming and time-consuming. In this paper, we discuss SAS 360 Discover, SAS 360 Engage Digital, and SAS Studio solutions can be leveraged for an e-commerce business to determine which campaign and functional effort was the best at delivering incremental revenue and conversion per customer segment | sas studio, proc glm, hypothesis testing, proc genmod, proc step | source | 16 |
| 339 | Using SAS for Generating and Sending Certificates by Email | Alan Ricardo da Silva | This paper shows a %certificate macro to create a basic structure of a certificate and then send it by email in a pdf format. Before to use the %certificate macro, it is required only a list (or a file) with the name and email of the participants | macro definition, macro variable | source | 7 |
| 340 | Using SAS Macro Variable Lists to Create Dynamic Data-Driven Programs | Joshua M. Horstman | The SAS macro facility is an amazing tool for creating dynamic, flexible, reusable programs that can automatically adapt to change. In this hands-on workshop, you'll learn how to create and use macro variable lists, a simple but powerful mechanism for creating data-driven programming logic. Don't hardcode data values into your programs. Eliminate data dependencies forever and let the macro facility write your SAS code for you! | macro variable, variable list, proc sql, macro facility, proc step, call symputx, macro definition, &variable, macro function, macro loop, macro variable list | source | 11 |
| 341 | Using SAS OpRisk Global Data to Improve Decision-Making at a Bank | Mentje Gericke, Helgard Raubenheimer |
The management of financial losses is crucial as banks are required to set aside regulatory capital to absorb unexpected losses. Banks also need to calculate economic capital to ensure solvency according to their own risk profile. The main financial risks faced by banks are market, credit, and operational risk. Operational risk, the focus here, includes fraud, improper business practices, and so on. Barings Bank's loss of over USD1 billion due to rogue trading activities is an extreme example of such risk. In order to calculate capital to withstand this risk, the aggregate distribution of expected losses for the next year is determined. The extreme quantiles of this distribution are of specific interest. For instance, a bank should hold capital to survive a one-in-a-thousand-year aggregate operational loss (the 99.9% VaR of the distribution) | operational loss, predictor variable, probability distribution, operational risk, proc freq, loss event, likelihood function, gamma distribution, qq plot, retail banking, dataset, expected loss, scenario analysis, poisson distribution | source | 17 |
| 342 | Using SAS PRX functions for medical records | Amy Alabaster, Mary Anne Armstrong |
SAS programmers analyzing complex free text data, like that present in the electronic medical record, likely find basic string functions insufficient or unwieldy for extracting meaningful data points from highly variable text sources. In addition to coded data like diagnoses, a patient's medical record consists of doctors' notes, test results, and reports with immense provider variation in text-cryptic abbreviations and typos are only the beginning. Perl regular expressions tackle many text problems like those encountered by programmers analyzing the medical record, and SAS uses this powerful tool with a suite of functions and call routines. This paper reviews the basics of implementing Perl regular expressions in SAS using SAS functions like PRXPARSE, CALL PRXSUBSTR, PRXMATCH, PRXCHANGE, and CALL PRXNEXT. Text examples draw from several medical specialties | proc print, type i error, proc step, type ii error, substr function | source | 13 |
| 343 | Using SAS to Pick the Right Report | Kelly D. Smith | Presentation: Scorecard Variables FT/PT Macro Kelly D. Smith Report Macro Profile PhotoThe equity project at Central Piedmont Community College originally consisted of one report per new student cohort; the report was generated from historical information obtained over a three-year period. In order to offer information on a more frequent and timely basis, the original report has been split into a series of mini-reports: first term, first year, first term in second year, second year, and third year outcome. Selecting the appropriate report for a cohort depends on both the cohort start date and the date of code execution. SAS software can be used to automatically analyze, select, and produce the appropriate report(s) through the use of macro functions within a larger macro. Please use the headings above to navigate through the different sections of the poster | macro function | source | 8 |
| 344 | Using SAS Visual Analytics Reports for Operational Reporting in Clinical Trials | Jun Wen, Jack Shostak |
Our presentation shows how to use SAS Visual Analytics to implement data visualization for operational reports in clinical trials. In clinical trials, operational reports are often used to analyze and monitor site characteristics and performance metrics as well as patients' enrollment, safety, and follow up. Data visualization can track the site and enrollment information in a dynamic and interactive way, and provide significant time savings with no decrease in accuracy compared to traditional static reporting. At the Duke Clinic Research Institute, we have successfully implemented data visualization for operational reports using SAS Visual Analytics. In our operational reports, we use SAS Visual Analytics Geo Maps to monitor site enrollment distribution. In our Geo Maps, we add the display rules to identify at risk sites, and the time series to show the site activation/enrollment by time | bar chart, correlation, sas visual analytics, line chart, disposition table, clinical trial, data visualization, proc freq | source | 17 |
| 345 | Using SAS9API and R to Create Violin Plots, Interactive 3D Plots, and a Shiny App for SAS Data Sets | Olga Pavlova | Presentation: Open-source tools are extremely popular within the data science community and R language is one of them. While SAS Viya allows easy collaboration between SAS and open-source languages like R and Python using HTTP protocol, SAS9 lacks this feature. To address this issue we at Analytium designed our SAS9API solution. It is based on REST API and allows you to connect to your SAS server. With SAS9API help you can get and manage SAS data and metadata. We also created a wrapper R package rsas9api. Among other options, it allows loading SAS data into R. Here we present different open-source data visualization techniques: violin plots, interactive 3D plots and even more interactive Shiny apps. | data science, data visualization, rest api | source | 7 |
| 346 | Using the ODS Report Writing Interface to Streamline Publication of Existing Reports with Complex Tables | James Brittain, Barnali Das |
This paper examines the SAS Output Delivery System (ODS) Report Writing Interface (RWI) to streamline the publication process of "Health, United States", an annual report on health and health care in the United States. The report's tables contain multiple nested categories within rows and columns and are suitable for printing (PDF) and downloading (Microsoft EXCEL). The publication process from SAS datasets currently involves several labor-intensive steps. In addition, Section 508 of the Rehabilitation Act of 1973 requires all federal agency website content to be accessible to people with disabilities. The process of manually labeling content for screen readers ("tagging") presents an additional challenge for these complex nested tables. The publishing process is being automated and the output delivery is being enhanced using the RWI. The ODS templates and Cascading Style Sheets are used to standardize the format of the output and varied formatting using conditional processing |
excel, dataset, sample, output delivery system | source | 19 |
| 347 | Using the Optgraph Procedure to Construct Closed Knight's Tours on Standard and Variant Chessboards | Joe DeMaio, Shafiul Alam |
A closed knight's tour of a chessboard uses legal moves of the knight to visit every square exactly once and return to its starting position. The closed knight's tour is a recreational variation of the well-known Traveling Salesman Problem (TSP). Gerlach uses Warnsdorff's heuristic technique and SAS to produce closed knight's tours for the traditional 8 x 8 board. Generalizing the same approach, Gerlach and Gerlach produce closed knight's tours for the n x 8 x 8 chessboard for n = 2, 3, 4, 5, 6 and theorize that the technique works for larger values of n as well. In 1991, Schwenk completely classified the n x m rectangular chessboards that admit a closed knight's tour. In 2011, DeMaio and Mathew completely classified the i x j x k rectangular prism chessboards that admit a closed knight's tour. This paper demonstrates how to utilize PROC OPTGRAPH with the TSP option to construct closed knight's for rectangles, rectangular prisms, hexagons and other variant chessboards |
proc step, network optimization | source | 15 |
| 348 | Using the R interface in SAS to Call R Functions and Transfer Data | Bruce F. Gilsen | Starting in SAS 9.3, the R interface enables SAS users on Windows and Linux who license SAS/IML software to call R functions and transfer data between SAS and R from within SAS. Potential users include SAS/IML users and other SAS users who can use PROC IML just as a wrapper to transfer data between SAS and R and call R functions. This paper provides a basic introduction and some simple examples. The focus is on SAS users who are not PROC IML users, but who want to take advantage of the R interface |
proc step, dataset, proc iml, sysdate9, proc options, missing value, estimate, variable, date variable, options statement, work library, regression analysis, proc reg | source | 17 |
| 349 | Variable Selection Using Random Forests in SAS | Denis Nyongesa | Random forests are an increasingly popular statistical method of classification and regression. The method was introduced by Leo Breiman in 2001. A good prediction model begins with a great feature selection process. This paper proposes the ways of selecting important variables to be included in the model using random forests. The variables to be included in the model are indexed or ranked according to the score of importance of each variable. The comparison of performance between random forest models (variables selected by the random forest method) and logistic regression models (variables selected by the stepwise method) is demonstrated | proc hpforest, sample, feature importance, feature selection, regression analysis, correlation, logistic regression, logistic distribution, predictor variable, response variable, proc step, data cleaning | source | 11 |
| 350 | Visual Data Insights: Image and Ranked Precise Values for Quick, Easy, and Reliable Inference | LeRoy Bessler | This paper provides eight widely applicable graphic examples and the embodied design principles for effective visual communication that apply to any graph and any software. Learn how to deliver the best results with Output Delivery System (ODS) Graphics when doing trend or time series plots, horizontal or vertical bar charts, pie charts, and choropleth maps (maps in which geographic unit areas are color-coded to identify their category or data range). As extras, two easy-to-create and easy-to-interpret special cases are included with exhibits and code in a handout, along with a reference list of design guidelines. Even if you are just a requestor or viewer of other people's data visualization, come to learn what to ask for and what to expect. This paper assumes no prior knowledge of ODS Graphics |
bar chart, vertical bar chart, line chart, time series analysis | source | 20 |
| 351 | Visualizing America: SAS Graphical Depictions utilizing the American Community Survey (ACS 2018) | Wendy B. Dickinson | Presentation: SAS provides an array of powerful tools to analytically examine-- and graphically depict-- large-scale datasets. This project explored the PROC FREQ functionality to produce mosaic plots for categorical data. Mosaic plots, as described by Hartigan and Kleiner (1981), are formed when numbers in a contingency table are represented by rectangles of areas proportional to the numbers, with shape and position rendered to expose deviations from independence models. The resulting visual depiction (collection of rectangles for the contingency table) is called a mosaic (Hartigan & Kleiner, 1981, 1984; Friendly, 1994, 2000, 2002). The colors and patterns displayed within the mosaic plots illustrate and define the relationships displayed by the categorical variable values. The American Community Survey, 2018 Release, was utilized as the empirical data source. The American Community Survey (ACS) is conducted annually by the United States Census Bureau. Social, housing, economic, and demographic data comprise the 4 major survey sections of the ACS. Within these four major survey sections, the ACS yields rich, personal data covering more than 40 topic areas, such as educational level of attainment, commute time to work, type of housing, and ethnicity. By combining SAS functionality with varying combinations of categorical information from a large-scale national dataset, mosaic plots were generated to create a visual snapshot-in-time of the ACS respondents. This work thus provides a contemporary window into the daily lives of millions of Americans across our country. |
proc freq, nominal variable, proc step | source | 12 |
| 352 | Visualizing Geographical Data with a Tile Grid Map in SAS | Lingxiao Li | The tile grid map is an increasingly popular tool to visualize statistical data, such as the US population change by state, on a map. It mimics an actual map with a set of equal squares in a rectangular grid. For example, you can make a tile grid map of the United States with each square representing an individual state. Unlike the choropleth map, the tile grid map does not show the perception bias that favors larger regions. Furthermore, the squares are well-suited for laying out "rubber stamp" graphs with subsetted data to create map-based small multiples. You can use the small multiples to effectively compare and analyze data in different regions of the map. Although the Graph Template Language (GTL) does not directly support this visualization, you can easily make one in SAS with a combination of the DATA STEP, the SQL and SUMMARY procedures, and the SAS ODS Graphics procedures (often called the Statistical Graphics procedures) |
macro function, scatter plot, time series analysis, macro definition, macro variable, proc step, proc sql, proc sgplot, proc means | source | 13 |
| 353 | Vroom Vroom! Fast-Track Your Organization onto SAS Visual Analytics in SAS Viya | Cathy Warner | As a business leader new to SAS Visual Analytics in SAS Viya, you might be wondering, "How do I get my team up and running quickly so that we can uncover the insights that we need?" You might have some of these questions: How is data accessed and refreshed? How should the data be structured? What security is available? How would I implement a development process and workflow? How do I convert my team from what we are using now? What else do I need to be thinking about? Classes, documentation, and videos teach how to use the SAS Visual Analytics software, much like driver's education teaches how to drive a car. This session builds off that content. You learn how to plan your route, organize your trip, set up traffic rules, and even make your car "self-driving." Your driving instructor is a SAS consultant who is on the road with new users every day | sas viya, sas 9, sas administrator, sas studio, sas enterprise guide, sample | source | 27 |
| 354 | What Happens After Police Shootings? | Aravind Dhanabal | In the United States, the use of lethal force by police officers has come under extreme scrutiny. Police Departments have instituted new programs such as the wearing of body cameras and recruiting social workers to work together with on-duty officers. Due to limited resources, these cannot be instituted by all departments; therefore, it is important to make data-driven decisions to make the most of those resources. This paper explores the possibility of underlying biases and prejudices that are leading to officers not being held accountable. To accomplish this, police shooting data and the results of the follow-on investigations were examined. The goal was to identify if there is a predictive model to evaluate if a police officer will face Grand Jury Indictment based on the attributes within the dataset. The police disposition (which specifies if an officer faced a Grand Jury Indictment or not) was used as the target variable | proc hpsplit, data cleaning | source | 7 |
| 355 | What's Bugging You? Find Out with the Interactive SAS Code Debugger | Jenna Austin | Life's too short to put up with buggy code. Join us as we show you how to use the new SAS Code Debugger to debug SAS code in the SAS Function Compiler (FCMP) procedure and in the SAS Cloud Analytic Services FCMP action set. SAS Code Debugger is a full-featured, interactive debugger that runs on SAS Viya 3.5 and SAS 9.4M6. The debugger is available as a stand-alone tool, and it is also built into SAS products such as SAS Model Implementation Platform. With SAS Code Debugger, you can discover programming problems, improve code quality, and debug complex models with ease. You can also set breakpoints, watch variables, and step into nested functions and subroutines. This paper introduces SAS Code Debugger, shows you tips for debugging SAS code, and gets you up and running quickly | source | 20 | |
| 356 | What's New in SAS Data Management | Nancy A. Rausch | The latest releases of SAS Data Management software provide a comprehensive and integrated set of capabilities for collecting, transforming, and managing your data. The latest features in the product suite include capabilities for working with data from a wide variety of environments and types including Apache Hadoop, cloud data sources, relational database management system (RDBMS), files, unstructured data, images, and streaming data, with the ability to perform extract, transform, load (ETL) and extract, load, transform (ELT) transformations in diverse run-time environments including SAS, Hadoop, Spark, SAS Analytics, cloud, and data virtualization environments. The SAS Data Management offering has been enhanced to include integration with SAS Studio, including a new ETL flow building capability that can be used to build reusable data flow processes. Enhancements have also been added to leverage analytics and artificial intelligence (AI) to help automate data management tasks | proc step, sas viya, star schema, data governance, data cleaning, sample, excel | source | 16 |
| 357 | What's New in SAS Drive? | Cheryl L. Coyle, Scott P. Leslie |
Do you use SAS Drive to organize and share content with your colleagues? Did you know you can customize SAS Drive so that the items you want to see are easy to find? In all versions of SAS Drive, you can add items to Quick Access. In SAS Drive 2.2, you can reorder the items in Quick Access through a simple drag-n-drop, and you can get recommendations for what to add to your Quick Access area. In all versions of SAS Drive, you can hide and show object tabs. In SAS Drive 2.2, you can make any folder in your organization's folder structure into a tab - so the information you care about most is at your fingertips. To help you visually identify your favorite items, we've added stars to the tiles, and to help you adjust SAS Drive so that it's most useful to you, you can now resize the Information Pane | source | 13 | |
| 358 | What's New in SAS Visual Analytics? Smart Business Intelligence, Smart Analytics | Rajiv Ramarajan, Mark Malek |
The new SAS Visual Analytics provides a richer reporting experience with the combined editor and viewer. It provides better suggestive user assistance that is embedded in the user's flow of working with data and content. It gives the report author the ability to provide exploring features for report consumers. There are more ways to interact with the visuals. Sharing and reuse are made easier. The automated explanation object allows for enhanced storytelling with analytics | bar chart, confusion matrix, response variable | source | 14 |
| 359 | What's New in SAS Visual Data Mining and Machine Learning: From a Programmer's Perspective | Tao Wang | The latest releases of SAS Visual Data Mining and Machine Learning software provide a comprehensive set of data mining and machine learning capabilities. The latest features in this product suite include enhanced principal component analysis through the new Kernel PCA action set, the new SEMISUPLEARN procedure to "impute" missing target labels in your training data automatically, and the new SPARSEML procedure for discovering insights from sparse data that now exist in many business domains. There are also many new features in existing action sets and procedures, such as the Image action set and the SVMACHINE procedure, and much more. This paper provides an overview of some hot topics and the latest features of the SAS Visual Data Mining and Machine Learning product suite and includes use cases and examples that demonstrate how to take full advantage of product capabilities from the programmer's perspective |
machine learning, proc princomp, proc step, training set, cas action, supervised learning, big data, sas viya, sas programmer, semi-supervised learning, support vector machine, parallel processing, response variable, sample, data science, regression analysis | source | 13 |
| 360 | What's Your Favorite Color? Controlling the Appearance of a Graph | Richann J. Watson | The appearance of a graph produced by the Graph Template Language (GTL) is controlled by Output Delivery System (ODS) style elements. These elements include fonts and line and marker properties as well as colors. A number of procedures, including the Statistical Graphics (SG) procedures, produce graphics using a specific ODS style template. This paper provides a very basic background of the different style templates and the elements associated with the style templates. However, sometimes the default style associated with a particular destination does not produce the desired appearance. Instead of using the default style, you can control which style is used by indicating the desired style on the ODS destination statement. However, sometimes not a single one of the 50-plus styles provided by SAS achieves the desired look. Luckily, you can modify an ODS style template to meet your own needs. One such style modification is to control which colors are used in the graph |
bar chart, proc template, proc step, sample, series plot, output delivery system | source | 33 |
| 361 | Who Are You When Playing World of Warcraft? An Analysis of Player Demographics and Social Behavior | Alyssa Venn, Joe DeMaio |
Presentation: ● As of the survey, there were two factions, 12 classes, and 22 races. This gives players many choices during character creation. We were curious how a person's real-life demographics affects who and how they play, from their character's design and class to what aspects of the game they focus on | source | 12 | |
| 362 | Why is it taking so long!? Utilizing query optimization to pull from external sources | Kurt Stultz | Presentation: What is a passthrough query? A passthrough query changes the location of execution of a query and consequentially, the order of operations of a data request. Instead of running your query locally in SAS, a passthrough sends the SQL syntax directly to the server/database hosting the data. This allows for processing/aggregating to be done before the data is transferred to SAS. * Side note: Since the query will be processed on another server, the SQL must match the language of the server | proc sql | source | 7 |
| 363 | Wildcarding in WHERE Clauses | David B. Horvath | Presentation: There is wildcarding allowed within where clause like expressions by using special characters. But if you want to search for those specific characters, you have to override them. If you forget to escape the character, you'll get unexpected matches. | where statement | source | 7 |
| 364 | Winning Tactics for SAS Visual Data Mining and Machine Learning: Why it's the Only Skill You Need | Chris St. Jeor | The ability to rapidly identify actionable data insights determines today's business success. SAS Visual Data Mining and Machine Learning on SAS Viya is a one-stop shop that enables both technical and non-technical stakeholders to complete that task with ease. From data exploration to machine learning, SAS Visual Data Mining and Machine Learning enables analysts to load data and find the needed answers quickly and efficiently. In this workshop, users will gain hands-on experience using SAS Visual Data Mining and Machine Learning as you walk through an analytics project from start to finish. Users will learn how to import and explore the data, build several predictive models, and then determine a champion model to answer business questions. Users will walk away with a baseline knowledge of the application and the various control mechanisms it provides | machine learning, champion model, akaike information criterion, response variable, data cleaning, feature selection, gradient boosting, sample, score code, correlation | source | 12 |
| 365 | Work Smarter, Not Harder: Learning to Live without Your X | Amit Patel, Lewis Mitchell |
Within the SAS community, a great deal of the population are experts in their own fields, but those aren't always technical roles. After supporting hundreds of users over a number of years, we realized there is one question that always comes up - how do we live without our X? With X-commands, pretty much anything is possible, and that unlimited potential makes both administrators and auditors nervous. As environments evolve and controls tighten, the loss of X-commands is all but inevitable for most teams; but never fear, there are alternatives! This paper will discuss how to replace the following: getting file listings; making and deleting directories and files; changing file permissions; Microsoft Windows compression. By harnessing the power of built-in SAS functions as well as other functionality, we are able to replicate much of what users have lost out on when faced with X-commands being disabled, enabling users to finally move on from their X | macro definition, sysrc, create table, macro variable, proc sql, macro flow control, proc step, macro loop | source | 52 |
| 366 | Write Custom Parallel Programs by Using the iml Action | Rick Wicklin, Arash Dehghan Banadaki |
This paper introduces theiml action, which is available in SAS Viya 3.5. The IML action supports most of the same syntax and functionality as the SAS/IML matrix language that SAS software has supported for decades. With minimal changes, most programs that run in the IML procedure can also run in theiml action. In addition, theiml action supports new programming features for parallel programming. Theiml action is different from most actions, which perform a specific task. Theiml action provides a set of general programming tools that you can use to implement a custom parallel algorithm. The programmer can control the computation itself and can control how the computation is distributed among nodes and threads on a cluster of machines (or threads on a single machine). The parallel programming features are demonstrated by using examples of simulation, power estimates, and scoring regression models |
proc step, sample, monte carlo, dataset, proc iml, macro definition, macro facility | source | 18 |
| 367 | Write SAS to Generate SAS: Three Code-Generating Techniques for Many Automation Solutions | Yun (Julie) Zhuo | While SAS has no shortage of automation techniques, it does not always occur to SAS programmers to write SAS codes that generate SAS codes. This paper introduces three SAS techniques having the capacity of generating SAS codes from driver data sets. Driver data sets can be external specification files, the readily-available SAS dictionary metadata, or even the data values in the original data sets. Through real-world examples, we will demonstrate that the code generators can be effective tools in many applications where there are either repetitive programming or immature and changing data. Additionally, this paper will also provide programming tips and compare the three techniques using the simplicity, flexibility, and efficiency metrics | macro variable, call execute, put statement, label statement, dataset, filename statement, proc sql, dictionary tables, sas programmer, file statement, sas log, character functions, date variable, sas output, proc contents, proc step, adverse events | source | 18 |
| 368 | Writing Data Quality rules using SAS Meta-programming | Shreyas Dalvi | Presentation: Data quality/Data Governance is important for any organization's data office, considering the major impact of bad data in all of the downstream applications like Regulatory reporting, advanced analytics. There are many tools in market to asses and govern the data quality of data warehouse and data marts. This paper explains how to implement data quality check for data marts using SAS Meta-programming technique. Meta-programming is way of programming where a program is used to write a program. In SAS this can be easily achieved by using SAS Macros and Metadata table. In this paper we have demonstrated how we can store and maintain the data quality rules in one metadata table and run the rules on various tables/extracts in data mart using SAS metadata driven approach. Data quality rules are applied on multiple extracts which are created by joining dimension and fact tables. Final report provides list of variables with rule name, description and percentage of error as per the rule. With all rules in single table we can achieve more flexibility in rule writing, maintenance and process automation. This paper is intended for Data science and Data management professional with intermediate level of SAS expertise. | data mart, data audit, data science | source | 7 |
| 369 | Writing Reliable SAS Programs | Balraj Pitlola, Venkata Sivarami Reddy Karimiddela |
Presentation: This paper presents simple reliable techniques that can benefit SAS programmers to write reliable code. Before diving into the techniques, let me tell you why it is important. Reliable code can be dependent upon the delivery of the expected functionality and the performance in a consistent, repeatable manner for a specified duration. Reliability is application-specific and is largely dependent on the code written and very little on the language itself. Because unreliable code is considered worthless or not trusted upon, reliability is considered paramount to other coding requirements such as portability, efficiency, and performance. A reliable code handles failure very well. In this paper, we discuss a few possible scenarios of failure and how to handle them. | sas programmer | source | 14 |
| 370 | You Are Using PROC GLM Too Much (and What You Should Be Using Instead) | Peter L. Flom | Ordinary least square regression is one of the most widely used statistical methods. However, it is a parametric model and relies on assumptions that are often not met. Alternative methods of regression for continuous dependent variables relax these assumptions in various ways. This paper will explore PROCS such as QUANTREG, ADAPTIVEREG and TRANSREG for these data. Keywords: Regression | regression analysis, proc step, proc quantreg, predictor variable, response variable | source | 22 |
| 371 | Your Data Will Go On: Practice for Character Data Migration | Edwin Xie | With the rapid advancement of technology, you inevitably face continual upgrades and changes in your working platforms. No matter how greatly the platform evolves, your work can keep in high continuity, and reliability if your data is successfully ported without being destroyed. Character data can be at risk during data migration, which is determined by its internationalization features such as encoding sensitivity and semantics dependence. This paper discusses the potential issues when moving your character data across environments, such as a migration to SAS with UTF-8 or other encoding environments. It also demonstrates the use of common tools during the migration, such as the character variable padding (CVP) engine. By using these tools flexibly, no matter how the environment changes, your data will go on | character variable, data migration, proc contents, proc print, options statement, cas, char., proc step, libname statement, length statement, dataset, raw data | source | 13 |
| 372 | Zen and the Art of Problem Solving | David B. Horvath | Although software development is taught as a STEM out of Science or Engineering schools, it is as much an art or craft - a creative process - as a science. This presentation focuses on innovative problem solving techniques the tools and techniques to use when your normal process just doesn't seem to get you to a solution. Much of the information in this talk is based on Robert Pirsig's Zen and the Art of Motorcycle Maintenance, which, although it focuses on Motorcycles, applies to all kinds of problem spaces (and Pirsig was a tech writer for IBM). These techniques have served me well over the years. The difference in art versus science approaches is actually supported by the way the brain works | problem solving, account | source | 8 |
This site serves as a convenient hosting location and does not work as a publisher. All copyrights belong to their respective owners.
The information on this site is provided for general reference purposes only. Whilst every effort is made to ensure that the site is up to date and accurate, no warrant, nor any responsibility or liability for, the accuracy or completeness of the content or for any loss which may arise from reliance on information contained in this site is provided. Links to other sites are provided for your convenience but no responsibility or liability for the content of those sites or of any external site which links to this site is accepted. References to any specific commercial product, process, service, or corporation are also for information only, and do not constitute endorsement, recommendation or favoring.
SAS is a registered trademark of SAS Institute Inc. SAS, Statistical Analysis System, and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration.