SID file, SAS^® Deployment Wizard, SAS^® Migration Utility, SAS^® Environment Manager, plan file. SAS^® can seem very mysterious to IT organizations used to working with other software solutions. The more IT knows and understands about SAS how it works, what its system requirements are, how to maintain it and back it up, and what its value is to the organization the better IT can support the SAS shop. This paper provides an introduction to the world of SAS and sheds light on some of the unique elements of maintaining a SAS environment.

Structured Query Language (SQL) does not recognize the concept of row order. Instead, query results are thought of as unordered sets of rows. Most workarounds involve including serial numbers, which can then be compared or subtracted. This presentation illustrates and compares five techniques for creating serial numbers.

The Base SAS^® 9.4 Output Delivery System (ODS) EPUB destination enables users to deliver SAS^® reports as e-books on Apple mobile devices. The first maintenance release of SAS^® 9.4 adds the ODS EPUB3 destination, which offers powerful new multimedia and presentation features to report writers. This paper shows you how to include images, audio, and video in your ODS EPUB3 e-book reports. You learn how to use publishing presentation techniques such as sidebars and multicolumn layouts. You become familiar with best practices for accessibility when employing these new features in your reports. This paper provides advanced instruction for writing e-books with ODS EPUB. Please bring your iPad, iPhone, or iPod to the presentation so that you can download and read the examples.

SAS^® 9.4 has overhauled web authentication schemes, and the integration with enterprise security infrastructure is quite different from that of SAS^® 9.3. This paper examines advanced security features such as Secure Sockets Layer (SSL) configuration, single sign-on (SSO) support through Integrated Windows authentication (IWA), and third-party security packages like CA SiteMinder and IBM Tivoli Access Manager and WebSEAL. FIPS 140-2 compliance efforts that enforce the use of a stronger encryption algorithm for web communication and the SAS^® system itself are also described. The authentication support for mobile devices such as the iPad is different. The secure Wi-Fi connection from a mobile device to the IT internal resources, as well as how it can be safely integrated into the enterprise security configuration by using the same user repository as the SAS web applications, is explained. The configuration example is shown with SAS^® Visual Analytics 6.2.

SAS^® 9.4 and SAS^® Visual Analytics support a wide list of authentication protocols such as Integrated Windows authentication (IWA), client certificate, IBM WebSEAL, CA SiteMinder, and Security Assertion Markup Language (SAML) 2.0. However, advanced customers might want to use some of these protocols together and also have the flexibility to select which protocols to use. In this paper, we focus on a fallback authentication framework that supports IWA as the primary authentication method. When IWA fails, it uses the X509 client certificate as the secondary authentication method, and when the client certificate fails, it uses the form-based username/password as the last option. The paper first introduces the security architecture of SAS^® 9.4 and SAS Visual Analytics. It then reviews the three above-mentioned security protocols. Further, it introduces the detailed fallback authentication framework and discusses how to configure it. Finally, we discuss the use of this framework in the customer scenario from implementing the fallback authentication framework in a customer s SAS^® 9.4 and SAS Visual Analytics environment.

SAS/ACCESS^® Interface to ODBC has been around forever. On one level, ODBC is very easy to use. That ease hides the flexibility that ODBC offers. This presentation uses examples to show you how to increase your program's performance and troubleshoot problems. You will learn: the differences between ODBC and OLE DB what the odbc.ini file is (and why it is important) how to discover what your ODBC driver is actually doing the difference between a native ACCESS engine and SAS/ACCESS Interface to ODBC

This paper is an introduction to SAS^® Studio and covers how to perform basic programming tasks in SAS Studio. Many people program in the SAS^® language by using SAS Display Manager or SAS^® Enterprise Guide^®. SAS Studio is different because it enables you to write and run SAS code by using the most popular web browsers, without requiring a SAS^® 9.4 installation on your machine. With SAS Studio, you can access your data files, libraries, and existing programs, and write new programs while using SAS software behind the scenes. SAS Studio connects to a SAS sever in order to process SAS programs. The SAS server can be a hosted server in a cloud environment, a server in your local environment, or a copy of SAS on your local machine.

SAS^® and SAS^® Enterprise Miner^™ have provided advanced data mining and machine learning capabilities for years beginning long before the current buzz. Moreover, SAS has continually incorporated advances in machine learning research into its classification, prediction, and segmentation procedures. SAS Enterprise Miner now includes many proven machine learning algorithms in its high-performance environment and is introducing new leading-edge scalable technologies. This paper provides an overview of machine learning and presents several supervised and unsupervised machine learning examples that use SAS Enterprise Miner. So, come back to the future to see machine learning in action with SAS!

A SAS^® license of any organization consists of a variety of SAS components such as SAS/STAT^®, SAS/GRAPH^®, SAS/OR^®, and so on. SAS administrators do not have any automated tool supplied with Base SAS^® software to find how many licensed copies are being actively used, how many SAS users are actively utilizing the SAS server, and how many SAS datasets are being referenced. These questions help a SAS administrator to take important decisions such as controlling SAS licenses, removing inactive SAS users, purging long-time non-referenced SAS data sets, and so on. With the help of a system parameter that is provided by SAS and called RTRACE, these questions can be answered. The goal of this paper is to explain the setup of the RTRACE parameter and to explain its use in making the SAS administrator s life easy. This paper is based on SAS^® 9.2 running on AIX operating system.

The power of social media has increased to such an extent that businesses that fail to monitor consumer responses on social networking sites are now clearly at a disadvantage. In this paper, we aim to provide some insights on the impact of the Digital Rights Management (DRM) policies of Microsoft and the release of Xbox One on their customers' reactions. We have conducted preliminary research to compare the basic text mining capabilities of SAS^® and R, two very diverse yet powerful tools. A total of 6,500 Tweets were collected to analyze the impact of the DRM policies of Microsoft. The Tweets were segmented into three groups based on date: before Microsoft announced its Xbox One policies (May 18 to May 26), after the policies were announced (May 27 to June 16), and after changes were made to the announced policies (June 16 to July 1). Our results suggest that SAS works better than R when it comes to extensive analysis of textual data. In our following work, customers reactions to the release of Xbox One will be analyzed using SAS^® Sentiment Analysis Studio. We will collect Tweets on Xbox posted before and after the release of Xbox One by Microsoft. We will have two categories, Tweets posted between November 15 and November 21 and those posted between November 22 and November 29. Sentiment analysis will then be performed on these Tweets, and the results will be compared between the two categories.

This presentation is an open-ended discussion about techniques for transferring data and analytical results from SAS^® to Microsoft Excel. There will be some introductory comments, but this presentation does not have any set content. Instead, the topics discussed are dictated by attendee questions. Come prepared to ask and get answers to your questions. To submit your questions or suggestions for discussion in advance, go to http://support.sas.com/surveys/askvince.html.

The Challenge: assigning outbound calling agents in a telemarketing campaign to geographic districts. The districts have a variable number of leads, and each agent needs to be assigned entire districts with the total number of leads being as close as possible to a specified number for each of the agents (usually, but not always, an equal number). In addition, there are constraints concerning the distribution of assigned districts across time zones in order to maximize productivity and availability. Our Solution: use the SAS/OR^® procedure PROC CLP to formulate the challenge as a constraint satisfaction problem (CSP) since the objective is not necessarily to minimize a cost function, but rather to find a feasible solution to the constraint set. The input consists of the number of agents, the number of districts, the number of leads in each district, the desired number of leads per agent, the amount by which the actual number of leads can differ from the desired number, and the time zone for each district.

With a growing enterprise analytics environment that comprises global users and a variety of sensitive data sources, a system administrator is faced with the challenge of knowing who logs into the system, how often, and what applications and what data sources are being consumed. This information is necessary for auditing the consumers of data as well as for monitoring the growth of data sources for hardware expansion. With the use of SAS^® Audit, Performance and Measurement Package, along with some additional middle-tier logging and SAS^® code, information about the major consumers of the environment can be loaded into LASR tables and analyzed with SAS^® Visual Analytics reporting tools.

As complicated as the macro language is to learn, there are very strong reasons for doing so. At its heart, the macro language is a code generator. In its simplest uses, it can substitute simple bits of code like variable names and the names of data sets that are to be analyzed. In more complex situations, it can be used to create entire statements and steps based on information may even be unavailable to the person writing or even executing the macro. At the time of execution, it can be used to make queries of the SAS^® environment as well as the operating system, and utilize the gathered information to make informed decisions about how it is to further function and execute.

Because the macro language is primarily a code generator, it makes sense that the code that it creates must be generated before it can be executed. This implies that execution of the macro language comes first. Simple as this is in concept, timing issues and conflicts are often not so simple to recognize in application. As we use the macro language to take on more complex tasks, it becomes even more critical that we have an understanding of these issues.

Macro variables and their values are stored in symbol tables, which in turn are held in memory. Not only are there are a number of ways to create macro variables, but they can be created in a wide variety of situations. How they are created and under what circumstances effects the variable s scope how and where the macro variable is stored and retrieved. There are a number of misconceptions about macro variable scope and about how the macro variables are assigned to symbol tables. These misconceptions can cause problems that the new, and sometimes even the experienced, macro programmer does not anticipate. Understanding the basic rules for macro variable assignment can help the macro programmer solve some of these problems that are otherwise quite mystifying.

There are many components that make up the middle tier and server tier of a SAS^® 9.4 deployment. There is also a variety of technologies that can be used to provide high availability of these components. This paper focuses on a small set of best practices recommended by SAS for a consistent high-availability strategy across the entire SAS 9.4 platform. We focus on two technologies: clustering, as well as the high-availability features of SAS^® Grid Manager. For the clustering, we detail newly introduced clustering capabilities in SAS 9.4 such as the middle-tier SAS^® Web Application Server and the server-tier SAS^® metadata clusters. We also introduce the small, medium, and large deployment scenarios or profiles, which make use of each of these technologies. These deployment scenarios reflect the typical customer's environment and address their high availability, performance, and scalability requirements.

You can provide access and visibility to SAS^® BI Dashboards, SAS^® Stored Processes, and SAS^® Visual Analytics through the use of SAS^® Web Parts for Microsoft SharePoint. In many organizations, the administrators who are responsible for SharePoint and SAS^® are different. This paper provides best practices for the deployment of SAS Web Parts for Microsoft SharePoint. Bridging the gap between SharePoint and SAS is especially important for people who are not familiar with SharePoint administration. This paper also provides tips for co-existence between SAS Web Parts for Microsoft SharePoint 6.1 and 5.1. (The 5.1 release is available in SAS^® 9.3. The 6.1 release is available in SAS^® 9.4.) Finally, this paper provides some guidance on DNS, permissions, and installation techniques the fine points that make or break your deployment!

SAS^® Visual Analytics and the SAS^® LASR™ Analytic Server provide many capabilities to analyze data fast. Depending on your organization, data can be loaded as a self-service operation. Or, your data can be large and shared with many people. And, as data gets large, effectively loading it and keeping it updated become important. This presentation discusses the range of data scenarios from self-service spreadsheets to very large databases, from single-subject data to large star schema topologies, and from single-use data to continually updated data that requires high levels of resilience and monitoring. Fast and easy access to big data is important to empower your organization to make better business decisions. Understanding how to have a responsive and reliable data tier on which to make these decisions is within your reach.

Discover how SAS^® leverages field marketing programs to support AllAnalytics.com, a sponsored third-party community. This paper explores the use of SAS software, including SAS^® Enterprise Guide^®, SAS^® Customer Experience Analytics, and SAS^® Marketing Automation to enable marketers to have better insight, better targeting, and better response from SAS programs.

The use of Cohen s kappa has enjoyed a growing popularity in the social sciences as a way of evaluating rater agreement on a categorical scale. The kappa statistic can be calculated as Cohen first proposed it in his 1960 paper or by using any one of a variety of weighting schemes. The most popular among these are the linear weighted kappa and the quadratic weighted kappa. Currently, SAS^® users can produce the kappa statistic of their choice through PROC FREQ and the use of relevant AGREE options. Complications arise however when the data set does not contain a completely square cross-tabulation of data. That is, this method requires that both raters have to have at least one data point for every available category. There have been many solutions offered for this predicament. Most suggested solutions include the insertion of dummy records into the data and then assigning a weight of zero to those records through an additional class variable. The result is a multi-step macro, extraneous variable assignments, and potential data integrity issues. The author offers a much more elegant solution by producing a segment of code which uses brute force to calculate Cohen s kappa as well as all popular variants. The code uses nested PROC SQL statements to provide a single conceptual step which generates kappa statistics of all types even those that the user wishes to define for themselves.

The life of a SAS^® program can be broken down into sets of changes made over time. Programmers are generally focused on the future, but when things go wrong, a look into the past can be invaluable. Determining what changes were made, why they were made, and by whom can save both time and headaches. This paper discusses version control and the current options available to SAS^® Enterprise Guide^® users. It then highlights the upcoming Program History feature of SAS Enterprise Guide. This feature enables users to easily track changes made to SAS programs. Properly managing the life cycle of your SAS programs will enable you to develop with peace of mind.

If you have an existing SAS^® Business Intelligence environment and you want to add SAS^® Visual Analytics, you need to make some architectural choices. SAS Visual Analytics and SAS Business Intelligence can share certain components, such as a SAS^® Metadata Server and the SAS^® Web Infrastructure Platform. Sharing metadata eliminates the need to create and maintain duplicate information, and it enables your users to take advantage of functionality that can be shared between SAS Visual Analytics and SAS Business Intelligence. Sharing the SAS Web Infrastructure Platform enables SAS middle-tier applications such as SAS^® Visual Analytics Services and SAS^® Web Report Studio to communicate with each other. Intended for SAS architects and administrators, this paper explores supported architecture for SAS Visual Analytics and SAS Business Intelligence. The paper then identifies areas where the architecture can be shared as well as where resources should be kept separate. In addition, the paper offers recommendations and other considerations to keep in mind when you are managing shared resources.

The big questions in consumer research lead to statistical methods appropriate to them. 'What do consumers say?' is all about analyzing surveys and finding relationships between preferences and background attributes. 'What do consumers think? is about looking at higher-level structures like preference mappings that can be derived from ratings. 'What will consumers pay?' is about conducting choice experiments to pin down the way consumers trade off among features and with prices, with the willingness to pay. 'How do you trigger purchases?' is about experiments that determine which interventions work, and how to target them to potential consumers, with uplift modeling. The SAS product JMP^® version 11 was released last fall with a new group of modeling tools to address these and other questions in consumer research. Traditionally JMP has specialized in engineering tools, but consumer research is an important part of engineering, in product planning, to make sure you produce the products with the attributes consumers want.

The INTCK function is used to obtain the number of time intervals between two dates. The INTCK function comes with arguments and argument-modifiers to enable us to perform a variety of date-related manipulations. This paper deals with a real-time simple usage of the INTCK function to calculate frequency of days of the week between the start and end day of a trip. The INTCK function with its arguments can directly calculate the number of days of the week as illustrated in this paper. The same usage of the INTCK function using PROC SQL is also presented in this paper. All the codes executed and presented in this paper involve Base SAS^® Release 9.3 only.

SAS^® Visual Analytics Designer enables you to create reports with different layouts. There are several basic graph objects that you can include in these reports. What if you wanted to create a report that wasn't possible with one of the out-of-the-box graph objects? No worries! The new SAS^® Visual Analytics Graph Builder available from the SAS^® Visual Analytics home page lets you create a custom graph object using built-in sample data. You can then include these graph objects in SAS Visual Analytics Designer and generate reports using them. Come see how you can create custom graph objects such as stock plots, butterfly charts, and more. These custom objects can be easily shared with others for use in SAS Visual Analytics Designer.

The evolution of the mobile landscape has created a shift in the workforce that now favors mobile devices over traditional desktops. Considering that today's workforce is not always in the office or at their desks, new opportunities have been created to deliver report content through innovative mobile experiences. SAS^® Mobile BI for both iOS and Android tablets compliments the SAS^® Visual Analytics offering by providing anytime, anywhere access to reports containing information that consumers need. This paper presents best practices and tips on how to optimize reports for mobile users, taking into consideration the constraints of limited screen real estate and connectivity, as well as answers a few frequently asked questions. Discover how SAS Mobile BI captures the power of mobile reporting to prepare for the vast growth that is predicted in the future.

Vistaprint saw the opportunity in the printing market to get more out of high-volume printing by grouping similar orders in large groups. They heavily rely on technology to handle design, printing, and order handling and use the Internet as a medium. With their successful expansion across the world, the issue they were facing was a lot of one-time buyers and a lot of registered users who didn't finish the check-out. The need to implement a retention strategy was the next logical step, for which they chose SAS^® Campaign Management. In this session, Vistaprint explains how they use campaign management for retention and how the project was addressed. They will also touch on how the concept of high performance could open up new possibilities for them.

Ebony and Ivory was a number one song by Paul McCartney and Stevie Wonder about making music together, proper integration, unity, and harmony on a deeper level. With SAS^® Visual Analytics, current Enterprise Business Intelligence (BI) customers can rest assured that their years of existing BI work and content can coexist until they can fully transition over to SAS Visual Analytics. This presentation covers 10 inter-operability integration points between SAS^® BI and SAS Visual Analytics.

With the introduction of new features in SAS^® 9.4 Grid Manager, administrators of SAS solutions have even better capabilities for effectively managing the use of SAS^® Enterprise Guide^® in a grid environment. In this paper, we explain and demonstrate proven practices for configuring the SAS 9.4 Grid Manager environment, leveraging grid options sets and grid-spawned SAS^® Workspace Servers. We walk through the options provided by SAS Enterprise Guide that make the most effective use of the grid environment.

Typically, it takes a system administrator to understand the graphic data results that are generated in the Microsoft Windows Performance Monitor. However, using SAS/GRAPH^® software, you can customize performance results in such a way that makes the data easier to read and understand than the data that appears in the default performance monitor graphs. This paper uses a SAS^® data set that contains a subset of the most common performance counters to show how SAS programmers can create an improved, easily understood view of the key performance counters by using SAS/GRAPH software. This improved view can help your organization reduce resource bottlenecks on systems that range from large servers to small workstations. The paper begins with a concise explanation of how to collect data with Windows Performance Monitor. Next, examples are used to illustrate the following topics in detail: converting and formatting a subset of the performance-monitor data into a data set using a SAS program to generate clearly labeled graphs that summarize performance results analyzing results in different combinations that illustrate common resource bottlenecks

Potential of One, Power of All. That has a really nice ring to it, especially as it pertains to accessing all of your corporate data through one single data access point. It means the potential of having a single source for all of your data connections from throughout the enterprise. It also means that the complexities of connecting to these data assets from the various source systems throughout the enterprise are hidden from the end user. With this, however, comes the possibility of placing personally identifiable information in the hands of a user who should not have access to it. The bottom line is that there is risk and uncertainty with allowing users to have access to data that is disallowed by your existing data governance strategy. Blocking these data elements from specific users or groups of users is a challenge that many corporations face today, whether it is secure financial information, confidential personnel records, or personal medical information protected by strict regulations. How do you surface All necessary data to All necessary users, while at the same time maintaining the security of the data? SAS^® Federation Server Manager is an easy-to-use interface that allows the data administrator to manage your data assets in such a way that it alleviates this risk by controlling access to critical data elements and maintaining the proper level of data disclosure control. This session focuses on how to employ various data access control strategies from within SAS Federation Server Manager.

Each month, our project team delivers updated 5-Star ratings for 15,700+ nursing homes across the United States to Centers for Medicare and Medicaid Services. There is a wealth of data (and processing) behind the ratings, and this data is longitudinal in nature. A prior paper in this series, 'Programming the Provider Previews: Extreme SAS^® Reporting,' discussed one aspect of the processing involved in maintaining the Nursing Home Compare website. This paper will discuss two other aspects of our processing: creating an annual data Compendium and extending the 5-star processing to accommodate several different output formats for different purposes. Products used include Base SAS^®, SAS/STAT^®, ODS Graphics procedures, and SAS/GRAPH^®. New annotate facilities in both SAS/GRAPH and the ODS Graphics procedures will be discussed. This paper and presentation will be of most interest to SAS programmers with medium to advanced SAS skills.

Data is often stored in highly normalized ( tall and skinny ) structures that are not convenient for analysis. The SAS^® programmer frequently needs to transform the data to arrange relevant variables together in a single row. Sometimes this is a simple matter of using the TRANSPOSE procedure to flip the values of a single variable into separate variables. However, when there are multiple variables to be transposed to a single row, it might require multiple transpositions to obtain the desired result. This paper describes five different ways to achieve this flip-flop, explains how each method works, and compares the usefulness of each method in various situations. Emphasis is given to achieving a data-driven solution that minimizes hard-coding based on prior knowledge of the possible values each variable can have and that improves maintainability and reusability of the code. The intended audience is novice and intermediate SAS programmers who have a basic understanding of the DATA step and the TRANSPOSE procedure.

In this interconnected world, it is becoming ever more important to understand not just details about your data, but also how different parts of your data are related to each other. From social networks to supply chains to text analytics, network analysis is becoming a critical requirement and network visualization is one of the best ways to understand the results. The new SAS^® Visual Analytics network visualization shows links between related nodes as well as additional attributes such as color, size, or labels. This paper explains the basic concepts of networks as well as provides detailed background information on how to use network visualizations within SAS Visual Analytics.

This paper shares our experience integrating two leading data analytics and Geographic Information Systems (GIS) software products SAS^® and ArcGIS to provide integrated reporting capabilities. SAS is a powerful tool for data manipulation and statistical analysis. ArcGIS is a powerful tool for analyzing data spatially and presenting complex cartographic representations. Combining statistical data analytics and GIS provides increased insight into data and allows for new and creative ways of visualizing the results. Although products exist to facilitate the sharing of data between SAS and ArcGIS, there are no ready-made solutions for integrating the output of these two tools in a dynamic and automated way. Our approach leverages the individual strengths of SAS and ArcGIS, as well as the report delivery infrastructure of SAS^® Information Delivery Portal.

Have you ever seen SAS^® Visual Analytics reports that are somehow more elegant than a standard report? Which qualities make reports easier to navigate, more appealing to the eye, or reveal insights more quickly? These quick tips will reveal several SAS Visual Analytics report design characteristics to help make your reports stand out from the pack. We cover concepts like color palettes, content organization, interactions, labeling, and branding, just to name a few.

With the ever increasing proliferation of disparate complex data being collected and stored, it has never been more important that this information is accurate, clean, integrated, and often times in compliance with an expanding set of government regulations. This means that the data must be cleaned and standardized, duplicates must be identified and removed, and the individual data must be able to be joined or merged together in some way. However, it is often the case that this data does not have the same variables or values to make this possible with a simple Join or Merge. To that end, one has to employ a set of fuzzy logics or fuzzy matching. Simply put, fuzzy matching is the implementation of algorithmic processes (fuzzy logic) to determine the similarity between elements of data such as business names, people names, or address information. Fuzzy logic is used to predict the probability of data with non-exact matches to help in data cleansing, deduplication, or matching of disparate data sets. This paper shows the basics of using fuzzy logic by using SAS^® functions, COMPLEV, multiple variables matches, and a modified Porter stemming algorithm.

Missing data is an ever-present issue, and analysts should exercise proper care when dealing with it. Depending on the data and the analytical approach, this problem can be addressed by simply removing records with missing data. However, in most cases, this is not the best approach. In fact, this can potentially result in inaccurate or biased analyses. The SAS^® programming language offers many DATA step processes and functions for handling missing values. However, some analysts might not like or be comfortable with programming. Fortunately, SAS^® Enterprise Guide^® can provide those analysts with a number of simple built-in tasks for discovering missing data and diagnosing their distribution across fields. In addition, various techniques are available in SAS Enterprise Guide for imputing missing values, varying from simple built-in tasks to more advanced tasks that might require some customized SAS code. The focus of this presentation is to demonstrate how SAS Enterprise Guide features such as Query Builder, Filter and Sort Wizard, Describe Data, Standardize Data, and Create Time Series address missing data issues through the point-and-click interface. As an example of code integration, we demonstrate the use of a code node for more advanced handling of missing data. Specifically, this demonstration highlights the power and programming simplicity of PROC EXPAND (SAS/ETS^® software) in imputing missing values for time series data.

When first presented with SAS^® Enterprise Guide^®, many existing SAS^® programmers don't know where to begin. They want to understand, 'What's in it for me?' if they switch over. These longtime users of SAS are accustomed to typing all of their code into the Program Editor window and clicking Submit. This beginning tutorial introduces SAS Enterprise Guide 6.1 to old and new users of SAS who need to code. It points out advantages and tips that demonstrate why a user should be excited about the switch. This tutorial focuses on the key points of a session involving coding and introduces new features. It covers the top three items for a user to consider when switching over to a server-based environment. Attendees will return to the office with a new motivation and confidence to start coding with SAS Enterprise Guide.

Do you have data in SharePoint that you would like to run analysis on with SAS^®? This workshop teaches you how to create a custom task in SAS^® Enterprise Guide^® in order to find, retrieve, and format that data into a SAS data set for use in your SAS programs.

This presentation is for users who are familiar with SAS^® Enterprise Guide^® but might not be aware of the many useful new features added in versions 4.2 and beyond. For example, SAS Enterprise Guide allows you to: Format your SAS^® source code to make it easier to read. Easily schedule a project to run at a given time. Work with OLAP data in your enterprise. We will overview these and other features to help you become even more productive using this powerful application.

The DATA step has served SAS^® programmers well over the years, and although it is powerful, it has not fundamentally changed. With DS2, SAS has introduced a significant alternative to the DATA step by introducing an object-oriented programming environment. In this paper, we share our experiences with getting started with DS2 and learning to use it to access, manage, and share data in a scalable, threaded, and standards-based way.

SAS^® solutions are tightly integrated with the scheduling capabilities provided by SAS^® Grid Manager and Platform Suite for SAS^®. Many organizations require that their corporate scheduler be used to control SAS processing within the enterprise. Historically this has been a laborious process, requiring duplication of job and flow information using manual forms and cumbersome change management. This paper provides proven techniques and methods that enable tight integration between the corporate scheduler and SAS without the administrative overhead. Platform Suite for SAS can be used to create flows which are then executed by the corporate scheduler. The business unit can tweak the flow without reference to the enterprise scheduling team. The approaches discussed are: Using the corporate scheduler to: Trigger SAS flows and to respond to flow return codes Restart a SAS flow that has exited due to error conditions Enable and disable LSF queues, allowing jobs that have been queued up to run within a time window that is managed on external dependencies rather than time How to configure your SAS environment to leverage the provided capabilities Real-world use cases to highlight the features and benefits of this approach The contents of this paper is of interest to SAS administrators and IT personnel responsible for enterprise scheduling. Full code and deployment instructions will be made available.

Organizations today make numerous decisions within their businesses that affect almost every aspect of their daily operations. Many of these decisions are now automatically generated by sophisticated enterprise decision management systems. These decisions include what offers to make to customers, sales transaction processing, payment processing, call center interactions, industrial maintenance, transportation scheduling, and thousands of other applications that all have a significant impact on the business bottom line. Concurrently, many of these same companies have developed or are now developing analytics that provide valuable insight into their customers, their products, and their markets. Unfortunately, many of the decision systems cannot maximize the power of analytics in the business processes at the point where the decisions are made. SAS^® Decision Manager is a new product that integrates analytical models with business rules and deploys them to operational systems where the decisions are made. Analytically driven decisions can be monitored, assessed, and improved over time. This paper describes the new product and its use and shows how models and business rules can be joined into a decision process and deployed to either batch processes or to real-time web processes that can be consumed by business applications.

SAS^® 9.4 has improved clustering capabilities that allow for scalability and failover for middle-tier servers and the metadata server. In this presentation, we share our experiences with high-availability and failover testing done prior to SAS 9.4 availability. We discuss what we tested and lessons learned (good and bad) while doing the testing.

Ensemble models combine two or more models to enable a more robust prediction, classification, or variable selection. This paper describes three types of ensemble models: boosting, bagging, and model averaging. It discusses go-to methods, such as gradient boosting and random forest, and newer methods, such as rotational forest and fuzzy clustering. The examples section presents a quick setup that enables you to take fullest advantage of the ensemble capabilities of SAS^® Enterprise Miner^™ by using existing nodes, Start Groups and End Groups nodes, and custom coding.

SAS^® Environment Manager is included with the release of SAS^® 9.4. This exciting new product enables administrators to monitor the performance and operation of their SAS^® deployments. What very few people are aware of is that the data collected by SAS Environment Manager is stored in a centralized data mart that's designed to help administrators better understand the behavior and performance of the components of their SAS solution stack. This data mart could also be used to help organizations to meet their ITIL reporting and measurement requirements. In addition to the information about alerts, events, and performance metrics collected by the SAS Environment Manager agent technology, this data mart includes the metadata audit and content usage data previously available only from the SAS^® Audit, Performance and Measurement Package.

Today's business needs require 24/7 access to your data in order to perform complex queries to your analytical store. In addition, you might need to periodically update your analytical store to append new data, delete old data, or modify some existing data. Historically, the size of your analytical tables or the window in which the table must be updated can cause unacceptable downtime for queries. This paper describes how you can use new SAS^® Scalable Performance Data Server 5.1 cluster table features to simulate transaction isolation in order to modify large sections of your cluster table. These features are optimized for extremely fast operation and can be done without affecting any on-going queries. This provides both the continuous query access and periodic update requirements for your analytical store that your business model requires.

Data governance combines the disciplines of data quality, data management, data policy management, business process management, and risk management into a methodology that ensures important data assets are formally managed throughout an enterprise. SAS^® has developed a cohesive suite of technologies that can be used to implement efficient and effective data governance initiatives, thereby improving an enterprise s overall data management efficiency. This paper discusses data governance use cases and challenges, and provides an example of how to manage the data governance lifecycle to ensure success.

SAS^® has a large portfolio of Java EE applications. In releases previous to SAS^® 9.4, SAS provides support for configuring, deploying, and running these applications in Oracle WebLogic, IBM WebSphere, or Red Hat JBoss. Beginning with SAS^® 9.4, SAS has updated the middle-tier architecture to deliver and run these web applications exclusivcely in the SAS^® Web Application Server (a specialized, extended configuration of Pivotal tc Server), rather than the other thrid-party web application servers. This paper discusses the motivation, technology selections, and architecture on which this change is based. It also describes the advantages that the new approach presents to customers, including increased automation of installation and configuration tasks, and improved system administration.

While survey researchers make great attempts to standardize their questionnaires including the usage of ratings scales in order to collect unbiased data, respondents are still prone to introducing their own interpretation and bias to their responses. This bias can potentially affect the understanding of commonly investigated drivers of customer satisfaction and limit the quality of the recommendations made to management. One such problem is scale use heterogeneity, in which respondents do not employ a panoramic view of the entire scale range as provided, but instead focus on parts of the scale in giving their responses. Studies have found that bias arising from this phenomenon was especially prevalent in multinational research, e.g., respondents of some cultures being inclined to use only the neutral points of the scale. Moreover, personal variability in response tendencies further complicates the issue for researchers. This paper describes an implementation that uses a Bayesian hierarchical model to capture the distribution of heterogeneity while incorporating the information present in the data. More specifically, SAS^® PROC MCMC is used to carry out a comprehensive modeling strategy of ratings data that account for individual level scale usage. Key takeaways include an assessment of differences between key driver analyses that ignore this phenomenon versus the one that results from our implementation. Managerial implications are also emphasized in light of the prevalent use of more simplistic approaches.

Over the past decade, sports analytics has seen an explosion in research and model development to calculate wins, reaching cult popularity with the release of the film 'Moneyball.' The purpose of this paper is to explore the methodology of solving a real-life Moneyball problem in basketball. An optimal basketball lineup will be selected in an attempt to maximize the total points per game while maximizing court coverage. We will briefly review some of the literature that has explored this type of problem, traditionally called the maximum coverage problem (MCP) in operations research. An exploratory data analysis will be performed, including visualizations and clustering in order to prep the modeling dataset for optimization. Finally, SAS^® will be used to formulate an MCP problem, and additional constraints will be added to run different business scenarios.

More organizations are understanding the importance of geo-tagged data and the need for tools that can successfully combine location data with business metrics to provide intelligent outputs that are beyond a simple map. SAS^® Visual Analytics provides a robust and powerful platform for achieving location intelligence performed with a combination of SAS^® Analytics and GIS mapping technologies such as that offered by Esri. This paper describes the essentials for achieving location intelligence and demonstrates with industry examples how SAS Visual Analytics makes it possible.

SAS/OR^® software for operations research includes mathematical optimization, discrete-event simulation, and project and resource scheduling capabilities. This paper surveys a number of its new features that better equip you to address decision-making challenges such as planning, resource management, and asset allocation. Optimization performance improvements help you solve larger, more detailed problems more quickly. Improvements encompass linear, mixed integer linear, and nonlinear optimization, and include multithreading of the mixed integer linear solver and major improvements in the performance and functionality of the decomposition algorithm for linear and mixed integer linear optimization. The OPTMODEL procedure for optimization modeling adds direct access to the same set of efficient network optimization algorithms available via the OPTNET procedure in SAS/OR, enabling you to embed network optimization as a component of larger solution processes. Other new features enable you to execute multiple optimizations in parallel and use the FCMP procedure to define functions. The OPTLSO procedure for global and local search optimization adds the ability to work with multiple objective functions and produce a set of Pareto-optimal solutions. This approach enables you to manage the trade-offs that arise between competing objectives and adds to the range of optimization problems that you can solve using PROC OPTLSO. Another new feature is support for the READ_ARRAY function in PROC FCMP, with which you can much more easily input array-structured data to be used in function definitions. Finally, SAS^® Simulation Studio for discrete-event simulation enhances its graphical interface to better support customization and increase ease of use.

Most programmers are familiar with the directive Know your data. But not everyone knows about all the data and metadata that a SAS^® data set holds or understands what to do with this information. This presentation talks about the majority of these attributes, how to obtain them, why they are important, and what you can do with them. For example, data sets that have been around for a while might have an inordinate number of deleted observations that you are carrying around unnecessarily. Or you might be able to quickly check to determine whether the data set is indexed and if so, by what variables in order to increase your program s performance. Also, engine-dependent data such as owner name and file size is found in PROC CONTENTS output, which is useful for understanding and managing your data. You can also use ODS output in order to use the values of these many attributes programmatically. This presentation shows you how.

In business environments, a common obstacle to effective data-informed decision making occurs when key stakeholders are reluctant to embrace statistically derived predicted values or forecasts. If concerns regarding model inputs, underlying assumptions, and limitations are not addressed, decision makers might choose to trust their gut and reject the insight offered by a statistical model. This presentation explores methods for converting potential critics into partners by proactively involving them in the modeling process and by incorporating simple inputs derived from expert judgment, focus groups, market research, or other directional qualitative sources. Techniques include biasing historical data, what-if scenario testing, and Monte Carlo simulations.

This poster shows the audience step-by-step how to connect to a database without registering the connection in either the Windows ODBC Administrator tool or in the Windows Registry database. This poster also shows how the connection can be more flexible and better managed by building it into a SAS^® macro.

When creating an OLAP cube, you have the option of specifying a drill-through table, also known as a Show Details table. This quick tip discusses the implications of using your detail table as your drill-through table and explores some viable alternatives.

Have you ever asked, Why doesn't my PDF output look just like my HTML output? This paper explains the power and differences of each destination. You ll learn how each destination works and understand why the output looks the way it does. Learn tips and tricks for how to modify your SAS^® code to make each destination look more like the other. The tips span from beginner to advanced in all areas of reporting. Each destination is like a superhero, helping you transform your reports to meet all your needs. Learn how to use each ODS destination to the fullest extent of its powers.

ODS is a power tool for generating HTML-based reports. Quite often, however, there are exacting requirements for report content, layout, and placement that can be done with HTML (and especially HTML5) that can t be done with ODS. This presentation shows several examples that use PROC STREAM and SAS^® Server Pages in a batch (for example, scheduled tasks, using SAS^® Display Manager, using SAS^® Enterprise Guide^®) to generate such custom reports. And yes, despite the name SAS Server Pages, this technology, including the use of jQuery widgets, does apply to batch environments. This paper describes and shows several examples that are similar to those presented in the SAS^® Press book SAS Server Pages: Generating Dynamic Content (http://support.sas.com/publishing/authors/extras/64993b.html) and on the author s blog Jurassic SAS in the BI/EBI World (http://hcsbi.blogspot.com/): creating a custom calendar; a sample mail-merge application; generating a custom Microsoft Excel-based report; and generating an expanding drill-down table.

Quite often when building web applications that use either the SAS^® Stored Process Server or the SAS/IntrNet^® Applications Dispatcher, it is necessary to create a custom user interface to prompt for the needed parameters. For example, generating a custom user interface can be accomplished by chaining stored processes together. The first stored process generates the user interface where the user selects the desired options and uses PROC STREAM to process and input SAS^® Server Pages to display the user interface. The second (or later) stored process in the chain generates the desired output. This paper describes and shows several examples similar to those presented in the SAS^® Press book SAS Server Pages: Generating Dynamic Content (http://support.sas.com/publishing/authors/extras/64993b.html) and on the author s blog Jurassic SAS in the BI/EBI World (http://hcsbi.blogspot.com/).

Power analysis helps you plan a study that has a controlled probability of detecting a meaningful effect, giving you conclusive results with maximum efficiency. SAS/STAT^® provides two procedures for performing sample size and power computations: the POWER procedure provides analyses for a wide variety of different statistical tests, and the GLMPOWER procedure focuses on power analysis for general linear models. In SAS/STAT 13.1, the GLMPOWER procedure has been updated to enable power analysis for multivariate linear models and repeated measures studies. Much of the syntax is similar to the syntax of the GLM procedure, including both the new MANOVA and REPEATED statements and the existing MODEL and CONTRAST statements. In addition, PROC GLMPOWER offers flexible yet parsimonious options for specifying the covariance. One such option is the two-parameter linear exponent autoregressive (LEAR) correlation structure, which includes other common structures such as AR(1), compound symmetry, and first-order moving average as special cases. This paper reviews the new repeated measures features of PROC GLMPOWER, demonstrates their use in several examples, and discusses the pros and cons of the MANOVA and repeated measures approaches.

Sparse data sets are common in applications of text and data mining, social network analysis, and recommendation systems. In SAS^® software, sparse data sets are usually stored in the coordinate list (COO) transactional format. Two major drawbacks are associated with this sparse data representation: First, most SAS procedures are designed to handle dense data and cannot consume data that are stored transactionally. In that case, the options for analysis are significantly limited. Second, a sparse data set in transactional format is hard to store and process in distributed systems. Most techniques require that all transactions for a particular object be kept together; this assumption is violated when the transactions of that object are distributed to different nodes of the grid. This paper presents some different ideas about how to package all transactions of an object into a single row. Approaches include storing the sparse matrix densely, doing variable selection, doing variable extraction, and compressing the transactions into a few text variables by using Base64 encoding. These simple but effective techniques enable you to store and process your sparse data in better ways. This paper demonstrates how to use SAS^® Text Miner procedures to process sparse data sets and generate output data sets that are easy to store and can be readily processed by traditional SAS modeling procedures. The output of the system can be safely stored and distributed in any grid environment.

Many SAS^® procedures use classification variables when they are processing the data. These variables control how the procedure forms groupings, summarizations, and analysis elements. For statistics procedures, they are often used in the formation of the statistical model that is being analyzed. Classification variables can be explicitly specified with a CLASS statement, or they can be specified implicitly from their usage in the procedure. Because classification variables have such a heavy influence on the outcome of so many procedures, it is essential that the analyst have a good understanding of how classification variables are applied. Certainly there are a number of options (system and procedural) that affect how classification variables behave. While you may be aware of some of these options, a great many are new, and some of these new options and techniques are especially powerful. You really need to be open to learning how to program with CLASS.

Do you find it difficult to dress up your graphs for your reports or presentations? SAS^® 9.4 introduced new capabilities in ODS Graphics that give you the ability to style your graphs without creating or modifying ODS styles. Some of the new capabilities include the following: a new option for controling how ODS styles are applied graph syntax for overriding ODS style attributes for grouped plots the ability to define font glyphs and images as plot markers enhanced attribute map support In this presentation, we discuss these new features in detail, showing examples in the context of Graph Template Language and ODS Graphics procedures.

The ZIP access method is new with SAS^® 9.4. This paper provides several examples of reading from and writing to ZIP files using this access method, including the use of the DATA step directory management macros and the new MEMVAR= option.

SAS/STAT^® 13.1 brings valuable new techniques to all sectors of the audience forSAS statistical software. Updates for survival analysis include nonparametricmethods for interval censoring and models for competing risks. Multipleimputation methods are extended with the addition of sensitivity analysis.Bayesian discrete choice models offer a modern approach for consumer research.Path diagrams are a welcome addition to structural equation modeling, and itemresponse models are available for educational assessment. This paper providesoverviews and introductory examples for each of the new focus areas in SAS/STAT13.1. The paper also provides a sneak preview of the follow-up release,SAS/STAT 13.2, which brings additional strategies for missing data analysis andother important updates to statistical customers.

SAS^® 9.4 introduces several new software products to better support SAS^® web applications. These products include SAS^® Web Server, SAS^® Web Application Server (with the availability of out-of-the-box clustering), and SAS^® Environment Manager. Even though these products have been tuned and tested for SAS 9.4 web applications, advanced users might want to know the tools and techniques that they can use to further monitor, manage, tune, and improve the performance of their environment. This paper discusses how customers can achieve that by exploring the following concepts, activities, techniques, and tools: using SAS Environment Manager to monitor run-time performance of middle-tier components using additional tools to monitor middle-tier components (Apache server-status, Java VisualVM, Java command-line tools, Java GC logging) identifying the potential bottlenecks and tuning suggestions identifying appropriate clustering strategy (single-server vs. multi-server for homogenous or heterogeneous clustering) suggesting the data to collect when analyzing performance (GC data, thread dumps, heapdumps, system resource utilization information, log files) discussing in-depth performance analysis tools (Thread Dump Analyzer, HPjmeter, Eclipse Memory Analyzer (MAT), IBM Support Assistant tools: GC and Memory Visualizer, Memory Analyzer, Thread, and Monitor Dump Analyzer)

SAS^® Enterprise Guide^® has become the place through which many SAS^® users access the power of SAS. Some like it, some loathe it, some have never known anything else. In my experience, the following attitudes prevail regarding the product: 1) I don't know what SAS is, but I can use a mouse and I know what my business needs are. 2) I've used SAS before, but now my company has moved to SAS Enterprise Guide and I love it! 3) I've used SAS before, but now my company has done something really stupid. SAS Enterprise Guide offers a place to learn as well as work. The product offers environments for point-and-click for those who want that, and a type-your-code-with-semi-colons environment for those who want that. Even better, a user can mix and match, using the best of both worlds. I show that SAS Enterprise Guide is a great place for building up business solutions using a step-by-step method, how we can make the best of both environments, and how we can dip our toes into parts of SAS that might have frustrated us in the past and made us run away and cry I ll do it in Excel! I demonstrate that there are some very nice aspects to SAS Enterprise Guide, out of the box, that are often ignored but that can improve the overall SAS experience. We look at my personal nemeses, SAS/GRAPH^® and PROC TABULATE, with a side-trip to the mysterious world that is ODS, or the Output Delivery System.

Changes in default behavior in the last few SAS^® releases have enabled faster processing of SAS formats, especially for SAS/ACCESS^® customers. But, as with any performance enhancement, your results may vary. This presentation teaches you: the differences between two important SAS format optimizations how to tell which optimization is in effect a simple method to get the behavior you want The target audience for this presentation is SAS/ACCESS customers, particularly those who have also licensed SAS^® In-Database Code Accelerator for Teradata or SAS^® In-Database Code Accelerator for Greenplum.

As organizations deploy SAS^® applications to produce the analytical results that are critical for solid decision making, they are turning to distributed grid computing operated by SAS^® Grid Manager. SAS Grid Manager provides a flexible, centrally managed computing environment for processing large volumes of data for analytical applications. Exceptional storage performance is one of the most critical components of implementing SAS in a distributed grid environment. When the storage subsystem is not designed properly or implemented correctly, SAS applications do not perform well, thereby reducing a key advantage of moving to grid computing. Therefore, a well-architected SAS environment with a high-performance storage environment is integral to clients getting the most out of their investment. This paper introduces concepts from software storage virtualization in the cloud for the generalized SAS Grid Manager architecture, highlights platform and enterprise architecture considerations, and uses the most popularly selected distributed file system, IBM GPFS, as an example. File system scalability considerations, configuration details, and tuning suggestions are provided in a manner that can be applied to a client s own environment. A summary checklist of important factors to consider when architecting and deploying a shared, distributed file system is provided.

There are exciting new capabilities available from SAS^® High-Performance Analytics and SAS^® Visual Analytics. Current customers seek a deployment strategy that enables gradual migration to the new technologies. Such a strategy would mitigate the need for 'rip and replace' and would enable resource utilization to evolve along a continuum rather than partitioning resources, which would result in underused computing or storage hardware. New customers who deploy a combination of SAS^® Grid Manager, SAS High-Performance Analytics, and SAS Visual Analytics seek to reduce the cost of computing resources and reduce data duplication and data movement by deploying these solutions on the same pool of hardware. When sharing hardware, it is important to implement resource management in order to help guarantee that resources are available for critical applications and processes. This session discusses various methods for managing hardware resources in a multi-application environment. Specific strategies are suggested, along with implementation suggestions.

This discussion uses SAS^® Office Analytics as an example to demonstrate the importance of preparing for the SAS^® installation. There are many nuances as well as requirements that need to be addressed before you do an installation. These requirements are basically similar, yet they differ according to the target installation operating system. In other words, there are some differences in preparation routines for Windows and *Nix flavors. Our discussion focuses on these three topics: 1. Pre-installation considerations such as sizing, storage, proper credentials, and third-party requirements; 2. Installation steps and requirements; and 3. Post-installation configuration. In addition to preparation, this paper also discusses potential issues and pitfalls to watch out for, as well as best practices.

Are you wondering what is causing your valuable machine asset to fail? What could those drivers be, and what is the likelihood of failure? Do you want to be proactive rather than reactive? Answers to these questions have arrived with SAS^® Predictive Asset Maintenance. The solution provides an analytical framework to reduce the amount of unscheduled downtime and optimize maintenance cycles and costs. An all new (R&D-based) version of this offering is now available. Key aspects of this paper include: Discussing key business drivers for and capabilities of SAS Predictive Asset Maintenance. Detailed analysis of the solution, including: Data model Explorations Data selections Path I: analysis workbench maintenance analysis and stability monitoring Path II: analysis workbench JMP^®, SAS^® Enterprise Guide^®, and SAS^® Enterprise Miner^™ Analytical case development using SAS Enterprise Miner, SAS^® Model Manager, and SAS^® Data Integration Studio SAS Predictive Asset Maintenance Portlet for reports A realistic business example in the oil and gas industry is used.

The UNIX host group delivers many utilities that go unnoticed. What are these utilities, and what can they tell you about your SAS^® system? Are you having authentication problems? Are you unable to get a result from a workspace server? What hot fixes have you applied? These are subjects that come up during a tech support call. It would be good to have background information about these tools before you have to use them.

SAS^® Visual Analytics delivers the power of approachable in-memory analytics in an intuitive web interface. The scalable technology behind SAS Visual Analytics should not benefit just the analyst or data scientist in your organization but indeed everyone regardless of their analytical background. This paper outlines a framework for the creation of a cloud deployment of SAS Visual Analytics using the SAS^® 9.4 platform. Based on proven best practices and existing customer implementations, the paper focuses on architecture, processes, and design for reliability and scalable multi-tenancy. The framework enables your organization to move away from the departmental view of the world and to offer analytical capabilities for consumerization and collaboration across the enterprise.

Using Lilypond typesetting software, you can write publication-grade music scores. The input for Lilypond is a text file that can be written once and then transferred to SAS^® for patterned repetition, so that you can cycle through patterns that occur in music. The author plays a sequence of notes and then writes this into Lilypond code. The sequence starts in the key of C with only a two-note sequence. Then the sequence is extended to three-, four-, then five-note sequences, always contained in one octave. SAS is then used to write the same code for all other eleven keys and in seven scale modes. The method is very simple and not advanced programming. Lookup files are used in the programming, demonstrating efficient lookup techniques. The result is a lengthy book or exercise for practicing music in a PDF file, and a sound source file in midi format is created that you can hear. This method shows how various programming languages can be used to write other programming languages.

Statistical mediation analysis is common in business, social sciences, epidemiology, and related fields because it explains how and why two variables are related. For example, mediation analysis is used to investigate how product presentation affects liking the product, which then affects the purchase of the product. Mediation analysis evaluates the mechanism by which a health intervention changes norms that then change health behavior. Research on mediation analysis methods is an active area of research. Some recent research in statistical mediation analysis focuses on extracting accurate information from small samples by using Bayesian methods. The Bayesian framework offers an intuitive solution to mediation analysis with small samples; namely, incorporating prior information into the analysis when there is existing knowledge about the expected magnitude of mediation effects. Using diffuse prior distributions with no prior knowledge allows researchers to reason in terms of probability rather than in terms of (or in addition to) statistical power. Using SAS^® PROC MCMC, researchers can choose one of two simple and effective methods to incorporate their prior knowledge into the statistical analysis, and can obtain the posterior probabilities for quantities of interest such as the mediated effect. This project presents four examples of using PROC MCMC to analyze a single mediator model with real data using: (1) diffuse prior information for each regression coefficient in the model, (2) informative prior distributions for each regression coefficient, (3) diffuse prior distribution for the covariance matrix of variables in the model, and (4) informative prior distribution for the covariance matrix.

How does the SAS^® server architecture fit within your IT infrastructure? What functional aspects does the architecture support? This session helps attendees understand the logical server topology of the SAS technology stack: resource and process management in-memory architecture in-database processing The session also discusses process flows from data acquisition through analytical information to visual insight. IT architects, data administrators, and IT managers from all industries should leave with an understanding of how SAS has evolved to better fit into the IT enterprise and to help IT's internal customers make better decisions.

To get the full benefit from PROC REPORT, the savvy programmer needs to master ACROSS usage and the COMPUTE block. Timing issues with PROC REPORT and ABSOLUTE column references can unlock the power of PROC REPORT. This presentation shows how to make the most of ACROSS usage with PROC REPORT. Use PROC REPORT instead of multiple TRANSPOSE steps. Find out how to use character variables with ACROSS. Learn how to impact the column headings for ACROSS usage items. Learn how to use aliases. Find out how to perform rowwise trafficlighting and trafficlighting based on multiple conditions.

All successful organizations seek ways of communicating the identity of subject matter experts to employees. This information exists as common knowledge when an organization is first starting out, but the common knowledge becomes fragmented as the organization grows. SAS^® Text Analytics can be used on an organization's internal unstructured data to reunite these knowledge fragments. This paper demonstrates how to extract and surface this valuable information from within an organization. First, the organization s unstructured textual data are analyzed by SAS^® Enterprise Content Categorization to develop a topic taxonomy that associates subject matter with subject matter experts in the organization. Then, SAS Text Analytics can be used successfully to build powerful semantic models that enhance an organization's unstructured data. This paper shows how to use those models to process and deliver real-time information to employees, increasing the value of internal company information.

Business analysts commonly use Microsoft Excel with the SAS^® System to answer difficult business questions. While you can use these applications independently of each other to obtain the information you need, you can also combine the power of those applications, using the SAS Output Delivery System (ODS) tagsets, to completely automate the process. This combination delivers a more efficient process that enables you to create fully functional and highly customized Excel worksheets within SAS. This paper starts by discussing common questions and problems that SAS Technical Support receives from users when they try to generate Excel worksheets. The discussion continues with methods for automating Excel worksheets using ODS tagsets and customizing your worksheets using the CSS style engine and extended tagsets. In addition, the paper discusses tips and techniques for moving from the current MSOffice2K and ExcelXP tagsets to the new Excel destination, which generates output in the native Excel 2010 format.

Security-conscious organizations have rigorous IT regulations, especially when company data is available on the move. This paper explores the options available to secure a deployment of SAS^® Mobile BI with SAS^® Visual Analytics. The setup ensures encrypted communication from remote mobile clients all the way to backend servers. Additionally, the integration of SAS Mobile BI with third-party Mobile Device Management (MDM) software and Virtual Private Network (VPN) technology enable you to place several layers of security and access control to your data. The paper also covers the out-of-the box security features of the SAS Mobile BI and SAS Visual Analytics administration applications to help you close the loop on all possible areas of exploitation.

Even if you are familiar with security considerations for SAS^® BI deployments, such as metadata and file system permissions, there are additional security aspects to consider when securing any environment that includes SAS^® Visual Analytics. These include files and permissions to the grid machines in a distributed environment, permissions on the SAS^® LASR™ Analytic Servers, and interactions with existing metadata types. We approach these security aspects from the perspective of an administrator who is securing the environment for himself, a data builder, and a report consumer.

Multiple imputation, a popular strategy for dealing with missing values, usually assumes that the data are missing at random (MAR). That is, for a variable X, the probability that an observation is missing depends only on the observed values of other variables, not on the unobserved values of X. It is important to examine the sensitivity of inferences to departures from the MAR assumption, because this assumption cannot be verified using the data. The pattern-mixture model approach to sensitivity analysis models the distribution of a response as the mixture of a distribution of the observed responses and a distribution of the missing responses. Missing values can then be imputed under a plausible scenario for which the missing data are missing not at random (MNAR). If this scenario leads to a conclusion different from that of inference under MAR, then the MAR assumption is questionable. This paper reviews the concepts of multiple imputation and explains how you can apply the pattern-mixture model approach in the MI procedure by using the MNAR statement, which is new in SAS/STAT^® 13.1. You can specify a subset of the observations to derive the imputation model, which is used for pattern imputation based on control groups in clinical trials. You can also adjust imputed values by using specified shift and scale parameters for a set of selected observations, which are used for sensitivity analysis with a tipping-point approach.

SAS^® Visual Analytics enables you to conduct ad hoc data analysis, visually explore data, develop reports, and then share insights through the web and mobile tablet apps. You can now also share your insights with colleagues using the SAS^® Office Analytics integration with Microsoft Excel, Microsoft Word, Microsoft PowerPoint, Microsoft Outlook, and Microsoft SharePoint. In addition to opening and refreshing reports created using SAS Visual Analytics, a new SAS^® Central view enables you to manage and comment on your favorite and recent reports from your Microsoft Office applications. You can also view your SAS Visual Analytics results in SAS^® Enterprise Guide^®. Learn more about this integration and what's coming in the future in this breakout session.

Distributing SAS^® software to a large number of machines can be challenging at best and exhausting at worst. Common areas of concern for installers are silent automation, network traffic, ease of setup, standardized configurations, maintainability, and simply the sheer amount of time it takes to make the software available to end users. We describe a variety of techniques for easing the pain of provisioning SAS software, including the new standalone SAS^® Enterprise Guide^® and SAS^® Add-in for Microsoft Office installers, as well as the tried and true SAS^® Deployment Wizard record and playback functionality. We also cover ways to shrink SAS Software Depots, like the new 'subsetting recipe' feature, in order to ease scenarios requiring depot redistribution. Finally, we touch on alternate methods for workstation access to SAS client software, including application streaming, desktop virtualization, and Java Web Start.

Businesses today are inundated with unstructured data not just social media but books, blogs, articles, journals, manuscripts, and even detailed legal documents. Manually managing unstructured data can be time consuming and frustrating, and might not yield accurate results. Having an analyst read documents often introduces bias because analysts have their own experiences, and those experiences help shape how the text is interpreted. The fact that people become fatigued can also impact the way that the text is interpreted. Is the analyst as motivated at the end of the day as they are at the beginning? Data science involves using data management, analytical, and visualization strategies to uncover the story that the data is trying to tell in a more automated fashion. This is important with structured data but becomes even more vital with unstructured data. Introducing automated processes for managing unstructured data can significantly increase the value and meaning gleaned from the data. This paper outlines the data science processes necessary to ingest, transform, analyze, and visualize three Star Wars movie scripts: A New Hope, The Empire Strikes Back, and Return of the Jedi. It focuses on the need to create structure from unstructured data using SAS^® Data Management, SAS^® Text Miner, and SAS^® Content Categorization. The results are featured using SAS^® Visual Analytics.

One beautiful graph provides visual clarity of data summaries reported in tables and listings. Waterfall graphs show, at a glance, the increase or decrease of data analysis results from various industries. The introduction of SAS^® 9.2 ODS Statistical Graphics enables SAS^® programmers to produce high-quality results with less coding effort. Also, SAS programmers can create sophisticated graphs in stylish custom layouts using the SAS^® 9.3 Graph Template Language and ODS style template. This poster presents two sets of example waterfall graphs in the setting of clinical trials using SAS^® 9.3 and later. The first example displays colorful graphs using new SAS 9.3 options. The second example displays simple graphs with gray-scale color coding and patterns. SAS programmers of all skill levels can create these graphs on UNIX or Windows.

Contrasting two sets of textual data points out important differences. For example, consider social media data that have been collected on the race between incumbent Kay Hagan and challenger Thom Tillis in the 2014 election for the seat of US Senator from North Carolina. People talk about the candidates in different terms for different topics, and you can extract the words and phrases that are used more in messages about one candidate than about the other. By using SAS^® Sentiment Analysis on the extracted information, you can discern not only the most important topics and sentiments for each candidate, but also the most prominent and distinguishing terms that are used in the discussion. Find out if Republicans and Democrats speak different languages!

Before you can analyze your big data, you need to prepare the data for analysis. This paper discusses capabilities and techniques for using the power of SAS^® to prepare big data for analytics. It focuses on how a SAS user can write code that will run in a Hadoop cluster and take advantage of the massive parallel processing power of Hadoop.

Nowadays, in the Big Data era, Business Intelligence Departments collect, store, process, calculate, and monitor massive amounts of data. Nevertheless, sometimes hundreds of metrics built on the structured data are inefficient to explain why the offered deal sold better or worse than expected. The answer might be found in text data that every company owns and yet is not aware of its possible usage or neglects its value. This project shows text mining methods, implemented in SAS^® Text Miner 12.1, that enable the determination of a deal's success or failure factors based on in-house or Internet-scattered customers' views and opinions. The study is conducted on data gathered from Groupon Sp. z o.o. (Polish business unit) - e-commerce company, as it is assumed that the market is by and large a customer-driven environment.

Once upon a time, a writer compared a desert to a labyrinth. A desert has no walls or stairways, but you can still find yourself utterly lost in it. And oftentimes, when you think you found that oasis you were looking for, what you are really seeing is an illusion, a mirage. Similarly, logical fallacies and misleading data patterns can easily deceive the unaware data explorer. In this paper, we discuss how they can be recognized and neutralized with the power of the SAS^® Visual Analytics Explorer. Armed with this knowledge, you will be able to safely navigate the dunes to find true insights and avoid false conclusions.

We continually work with our hardware partners to establish best practices with regard to tuning the latest hardware components that are released each year. This paper goes over the latest tuning guidelines for your hardware infrastructure, including your host computer system, operating system, and complete I/O infrastructure (from the computer host and network adapters down through the physical storage). Our findings are published in SAS^® papers on the SAS website, support.sas.com, with updates posted to the SAS Administration blog.

HTML5 has become the de facto standard for web applications. As a result, the lingua franca object notation of the web services that the web applications call has switched from XML to JSON. JSON is remarkably easy to parse in JavaScript, but so far SAS doesn't have any native JSON parsers. The Facebook Graph API dropped XML support a few years ago. This paper shows how we can parse the JSON in SAS by calling an external script, using PROC GROOVY to parse it inside of SAS, or by parsing the JSON manually with a DATA step. We'll extract the data from the Facebook Graph API and import it into an OLAP data mart to report and analyze a marketing campaign's effectiveness.

SAS^® Management Console was designed to control and monitor virtually all of the parts and features of the SAS^® Intelligence Platform. However, administering even a small SAS^® Business Intelligence system can be a daunting task. This paper presents a few techniques that will help you simplify your administrative tasks and enable you and your user community to get the most out of your system. The SAS^® Metadata Server stores most of the information required to maintain and run the SAS Intelligence Platform, which is obviously the heart of SAS BI. It stores information about libraries, users, database logons, passwords, stored processes, reports, OLAP cubes, and a myriad of other information. Organization of this metadata is an essential part of an optimally performing system. This paper discusses ways of organizing the metadata to serve your organization well. It also discusses some of the key features of SAS Management Console and best practices that will assist the administrator in defining roles, promoting, archiving, backing up, securing, and simply just organizing the data so that it can be found and accessed easily by administrators and users alike.

No need to fret, Base SAS^® programmers. Converting to SAS^® Enterprise Guide^® is a breeze, and it provides so many advantages. Coding remote connections to SAS^® servers is a thing of the past. Generate WYSIWYG prompts to increase the usage of the SAS code and to create reports and SAS^® Stored Processes to share easily with people who don t use SAS Enterprise Guide. The first and most important thing, however, is to change the default options and preferences to tame SAS Enterprise Guide, making it behave similar to your Base SAS ways. I cover all of these topics and provide demos along the way.

Two new production features offered in the Output Delivery System (ODS) in SAS^® 9.4 are ODS LAYOUT and the ODS Report Writing Interface. This one-two punch gives you power and flexibility in structuring your SAS^® output. What are the strengths for each? How do they differ? How do they interact? This paper highlights the similarities and differences between the two and illustrates the advantages of using them together. Why go twelve rounds? Make your report a knockout with ODS LAYOUT and the Report Writing Interface.

When assisting SAS^® customers who are experiencing performance issues, we are often asked by the SAS users at a customer site for the top 10 guidelines to share with those who have taken on the role of system administrator or SAS administrator. This paper points you to where you can get more information regarding each of the guidelines and related details on the SAS website.

Do you often create SAS^® web applications? Do you need to update or retrieve values from a SAS data set and display them in a browser? Do you need to show the results of a SAS^® Stored Process in a browser? Are you finding it difficult to figure out how to pass parameters from a web page to a SAS Stored Process? If you answered yes to any of these questions, then look no further. Techniques shown in this paper include: How to take advantage of JavaScript and minimize PUT statements. How to call a SAS Stored Process from your web page by using JavaScript and XMLHTTPRequest. How to pass parameters from a web page to a SAS Stored Process and from a SAS Stored Process back to the web page. How to use simple Ajax to refresh and update a specific part of a web page without the need to reload the entire page. How to apply Cascading Style Sheets (CSS) on your web page. How to use some of the latest HTML5 features, like drag and drop. How to display run-time graphs in your web page by using STATGRAPH and PROC SGRENDER. This paper contains sample code that demonstrates each of the techniques.

You don't have to be with the CIA to discover why your SAS^® stored process is producing clandestine results. In this talk, you will learn how to use prompts to get the results you want, work with the metadata to ensure correct results, and even pick up simple coding tricks to improve performance. You will walk away with a new decoder ring that allows you to discover the secrets of the SAS logs!

SAS^® provides a wide variety of products and solutions that address analytics, data management, and reporting. It can be challenging to understand how the data and processes in a SAS deployment relate to each other and how changes in your processes affect downstream consumers. This paper presents visualization and reporting tools for lineage and impact analysis. These tools enable you to understand where the data for any report or analysis originates or how data is consumed by data management, analysis, or reporting processes. This paper introduces new capabilities to import metadata from third-party systems to provide lineage and impact analysis across your enterprise.

SAS^® Visual Analytics is a unique tool that provides both exploratory and predictive data analysis capabilities. As the visual part of the name suggests, the rendering of this analysis in the form of visuals (crosstabs, line charts, histograms, scatter plots, geo maps, treemaps, and so on) make this a very useful tool. Join me as I walk you down the path of exploring the capabilities of SAS Visual Analytics 6.3, starting with data stored in a desktop application as multiple Microsoft Excel files. Together, we import the data into SAS Visual Analytics, prepare the data using the data builder, load the data into SAS^® LASR™ Analytic Server, explore data, and create reports.

When deploying SAS^® code into a production environment, a programmer should ensure that the code satisfies the following key criteria: The code runs without errors. The code performs operations consistent with the agreed upon business logic. The code is not dependent on manual human intervention. The code performs necessary checks in order to provide sufficient quality control of the deployment process. Base SAS^® programming offers a wide range of techniques to support the last two aforementioned criteria. This presentation demonstrates the use of SAS^® macro variables in combination with simple macro programs to perform a number of routine automated tasks that are often part of the production-ready code. Some of the examples to be demonstrated include the following topics: How to check that required key parameters for a successful program run are populated in the parameters file. How to automatically copy the content of the permanent folder to the newly created backup folder. How to automatically update the log file with new run information. How to check whether a data set already exists in the library.

Everyone has heard about SAS^® Cloud. Now come learn how you can build and manage your own cloud using the same SAS^® virtual application (vApp) technology.

This session demonstrates how to use Base SAS^® tools to add functional, reusable extensions to the SAS^® system. Learn how to do the following: Write user-defined macro functions that can be used inline with any other SAS code. Use PROC FCMP to write and store user-defined functions that can be used in other SAS programs. Write DS2 user-defined methods and store them in packages for easy reuse in subsequent DS2 programs.

Have you found OS file permissions to be insufficient to tailor access controls to meet your SAS^® data security requirements? Have you found metadata permissions on tables useful for restricting access to SAS data, but then discovered that SAS programmers can avoid the permissions by issuing LIBNAME statements that do not use the metadata? Would you like to ensure that users have access to only particular rows or columns in SAS data sets, no matter how they access the SAS data sets? Metadata-bound libraries provide the ability to authorize access to SAS data by authenticated Metadata User and Group identities that cannot be bypassed by SAS programmers who attempt to avoid the metadata with direct LIBNAME statements. They also provide the ability to limit the rows and columns in SAS data sets that an authenticated user is allowed to see. The authorization decision is made in the bowels of the SAS^® I/O system, where it cannot be avoided when data is accessed. Metadata-bound libraries were first implemented in the second maintenance release of SAS^® 9.3 and were enhanced in SAS^® 9.4. This paper overviews the feature and discusses best practices for administering libraries bound to metadata and user experiences with bound data. It also discusses enhancements included in the first maintenance release of SAS 9.4.

Comprehensive cancer centers have been mandated to engage communities in their work; thus, measurement of community engagement is a priority area. Siteman Cancer Center s Program for the Elimination of Cancer Disparities (PECaD) projects seek to align with 11 Engagement Principles (EP) previously developed in the literature. Participants in a PECaD pilot project were administered a survey with questions on community engagement in order to evaluate how well the project aligns with the EPs. Internal consistency is examined using PROC CORR with the ALPHA option to calculate Cronbach s alpha for questions that relate to the same EP. This allows items that have a lack of internal consistency to be identified and to be edited or removed from the assessment. EP-specific scores are developed on quantity and quality scales. Lack of internal consistency was found for six of the 16 EP s examined items (alpha<.70). After editing the items, all EP question groups had strong internal consistency (alpha>.85). There was a significant positive correlation between quantity and quality scores (r=.918, P<.001). Average EP-specific scores ranged from 6.87 to 8.06; this suggests researchers adhered to the 11 EPs between sometime and most of the time on the quantity scale and between good and very good on the quality scale. Examining internal consistency is necessary to develop measures that accurately determine how well PECaD projects align with EPs. Using SAS^® to determine internal consistency is an integral step in the development of community engagement scores.

Data quality is at the very heart of accurate, relevant, and trusted information, but traditional techniques that require the data to be moved, cleansed, and repopulated simply can't scale up to cover the ultra-jumbo nature of big data environments. This paper describes how SAS^® Data Quality accelerators for databases like Teradata and Hadoop deliver data quality for big data by operating in situ and in parallel on each of the nodes of these clustered environments. The paper shows how data quality operations can be easily modified to leverage these technologies. It examines the results of performance benchmarks that show how in-database operations can scale to meet the demands of any use case, no matter how big a big data mammoth you have.

In connection with the consolidation work at Nykredit, the data stored on the Nykredit z/OS SAS^® installation had to be migrated (copied) to the new x64 Windows SAS platform storage. However, getting an overview of these data on the z/OS mainframe can be difficult, and a series of questions arise during the process. For example: Who is responsible? How many bytes? How many rows and columns? When were the data created? And so on. With extensive use of filename FTP and looping, and extracting metadata, it is possible to get an overview of the data on the host presented in a Microsoft Excel spreadsheet.

The latest releases of SAS^® Data Integration Studio and SAS^® Data Management provide an integrated environment for managing and transforming your data to meet new and increasingly complex data management challenges. The enhancements help develop efficient processes that can clean, standardize, transform, master, and manage your data. The latest features include: capabilities for building complex job processes web and tablet environments for managing your data enhanced ELT transformation capabilities big data transformation capabilities for Hadoop integration with the SAS^® LASR™ platform enhanced features for lineage tracing and impact analysis new features for master data and metadata management This paper provides an overview of the latest features of the products and includes use cases and examples for leveraging product capabilities.

SAS^® has an amazing arsenal of tools to use and display geographic information that is relatively unknown and underutilized. This presentation will highlight both new and existing capacities for creating stunning, informative maps as well as using geographic data in other ways. SAS provided map data files, functions, format libraries and other geographic data files will be explored in detail. Custom mapping of geographic areas will be discussed. Maps produced will include use of both the annotate facility (including some new functions) and PROC GREPLAY. Products used are Base SAS^® and SAS/GRAPH^®. SAS programmers of any skill level will benefit from this presentation.

We receive a daily file with information about patients who use our drug. It s updated every day so that we have the most current information. Nearly every variable on a patient s record can be different from one day to the next. But what if you wanted to capture information that changed? For example, what if a patient switched doctors sometime along the way, and the original prescribing doctor is different than the patient's present doctor? With this type of daily file, that information is lost. To avoid losing these changes, you have to build a cumulative data set. I ll show you how to build it.