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 SAS^® Data Quality Server allows SAS^® programmers to integrate the power of DataFlux^® into their data cleaning programs. The power of SAS Data Quality Server enables programmers to efficiently identify matching records across different datasets when exact matches are not present. During a recent educational research project, the DQMATCH function proved very capable when trying to link records from disparate data sources. Two key insights led to even greater success in linking records. The first insight was acknowledging that the hierarchical structure of data can greatly improve success in matching records. The second insight was that the names of individuals can be restructured to improve the chances of successful matches. This paper provides an overview of how these insights were implemented using the DQMATCH function to link educational data from multiple sources.

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.

Graphic software users are confronted with what I call Options Over-Choice, and with defaults that are designed to easily give you a result, but not necessarily the best result. This presentation and paper focus on guidelines for communication-effective data visualization. It demonstrates their practical implementation, using graphic examples likely to be adaptable to your own work. Code is provided for the examples. Audience members will receive the latest update of my tip sheet compendium of graphic design principles. The examples use SAS^® tools (traditional SAS/GRAPH^® or the newer ODS graphics procedures that are available with Base SAS^®), but the design principles are really software independent. Come learn how to use data visualization to inform and influence, to reveal and persuade, using tips and techniques developed and refined over 34 years of working to get the best out of SAS^® graphic software tools.

Patient safety in a neonatal intensive care unit (NICU) as in any hospital unit is critically dependent on appropriate staffing. We used SAS^® Simulation Studio to create a discrete-event simulation model of a specific NICU that can be used to predict the number of nurses needed per shift. This model incorporates the complexities inherent in determining staffing needs, including variations in patient acuity, referral patterns, and length of stay. To build our model, the group first estimated probability distributions for the number and type of patients admitted each day to the unit. Using both internal and published data, the team also estimated distributions for various NICU-specific patient morbidities, including type and timing of each morbidity event and its temporal effect on a patient s acuity. We then built a simulation model that samples from these input distributions and simulates the flow of individual patients through the NICU (consisting of critical-care and step-down beds) over a one-year time period. The general basis of our model represents a method that can be applied to any unit in any hospital, thereby providing clinicians and administrators with a tool to rigorously and quantitatively support staffing decisions. With additional refinements, the use of such a model over time can provide significant benefits in both patient safety and operational efficiency.

Merging or joining data sets is an integral part of the data consolidation process. Within SAS^®, there are numerous methods and techniques that can be used to combine two or more data sets. We commonly think that within the DATA step the MERGE statement is the only way to join these data sets, while in fact, the MERGE is only one of numerous techniques available to us to perform this process. Each of these techniques has advantages, and some have disadvantages. The informed programmer needs to have a grasp of each of these techniques if the correct technique is to be applied. This paper covers basic merging concepts and options within the DATA step, as well as a number of techniques that go beyond the traditional MERGE statement. These include fuzzy merges, double SET statements, and the use of key indexing. The discussion will include the relative efficiencies of these techniques, especially when working with large data sets.

Cross-visit checks are a vital part of data cleaning for longitudinal studies. The nature of longitudinal studies encourages repeatedly collecting the same information. Sometimes, these variables are expected to remain static, go away, increase, or decrease over time. This presentation reviews the na ve and the better approaches at handling one-variable and two-variable consistency checks. For a single-variable check, the better approach features the new ALLCOMB function, introduced in SAS^® 9.2. For a two-variable check, the better approach uses the .first pseudo-class to flag inconsistencies. This presentation will provide you the tools to enhance your longitudinal data cleaning process.

Response rates, churn models, customer lifetime value today's marketing departments are more analytically driven than ever. Marketers have had their heads down developing analytic capabilities for some time. The results have been game-changing. But it's time for marketers to look up and discover which analytic results from other departments can enhance the analytics of marketing. What if you knew the demand forecast for your products? What could you do? What if you understood the price sensitivity for your products? How would this impact the actions that your marketing team takes? Using the hospitality industry as an example, we explore how marketing teams can use the analytic outputs from other departments to get better results overall.

This paper illustrates some SAS^® graphs that can be useful for variable selection in predictive modeling. Analysts are often confronted with hundreds of candidate variables available for use in predictive models, and this paper illustrates some simple SAS graphs that are easy to create and that are useful for visually evaluating candidate variables for inclusion or exclusion in predictive models. The graphs illustrated in this paper are bar charts with confidence intervals using the GCHART procedure and comparative histograms using the UNIVARIATE procedure. The graphs can be used for most combinations of categorical or continuous target variables with categorical or continuous input variables. This paper assumes the reader is familiar with the basic process of creating predictive models using multiple (linear or logistic) regression.

Determining what, when, and how to migrate SAS^® software from one major version to the next is a common challenge. SAS provides documentation and tools to help make the assessment, planning, and eventual deployment go smoothly. We describe some of the keys to making your migration a success, including the effective use of the SAS^® Migration Utility, both in the analysis mode and the execution mode. This utility is responsible for analyzing each machine in an existing environment, surfacing product-specific migration information, and creating packages to migrate existing product configurations to later versions. We show how it can be used to simplify each step of the migration process, including recent enhancements to flag product version compatibility and incompatibility.

Despite its popularity in recent years, .NET development has yet to enjoy the quality, level, and depth of statistical support that has always been provided by SAS^®. And yet, many .NET applications could benefit greatly from the power of SAS and, likewise, some SAS applications could benefit from friendly graphical user interfaces (GUIs) supported by Microsoft s .NET Framework. What the author sets out to do here is to 1) outline the basic mechanics of automating SAS with .NET, 2) provide a framework and specific strategies for maintaining parallelism between the two platforms at runtime, and 3) sketch out put some simple applications that provide an exciting combination of powerful SAS analytics and highly accessible GUIs. The mechanics of automating SAS with .NET will be covered briefly. Attendees will learn the required objects and methods needed to pass information between the two platforms. The attendees will learn some strategies for organizing their projects and for writing SAS code that lends itself to automation. This will include embedding SAS scripts within a .NET project and managing communications between the two platforms. Specifically, the log and listing output will be captured and handled by .NET, and user actions will be interpreted and sent to the SAS engine. Example applications used throughout the session include a tool that converts between SAS variable types through simple drag-and-drop and an application that analyzes the growth of the user s computer hard drive.

U.S. educators face a critical new imperative: to prepare all students for work and civic roles in a globalized environment in which success increasingly requires the ability to compete, connect, and cooperate on an international scale. The Asia Society and the Longview Foundation are collaborating on a project to show both the need for and supply of globally competent graduates. This presentation shows you how SAS assisted these organizations with a solution that leverages SAS^® visualization technologies in order to produce a heatmap application. The heatmap application surfaces data from over 300 indicators and surfaces over a quarter million data points in a highly iterative heatmap application. The application features a drillable map that shows data at the state level as well as at the county level for all 50 states. This endeavor involves new SAS^® 9.4 technology to both combine the data and to create the interface. You'll see how SAS procedures, such as PROC JSON, which came out in SAS 9.4, were used to prepare the data for the web application. The user interface demonstrates how SAS/GRAPH^® output can be combined with popular JavaScript frameworks like Dojo and Twitter Bootstrap to create an HTML5 application that works on desktop, mobile, and tablet devices.

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.

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.

Mobile devices are taking over conventional ways of sharing and presenting information in today s businesses and working environments. Accessibility to this information is a key factor for companies and institutions in order to reach wider audiences more efficiently. SAS^® software provides a powerful set of tools that allows developers to fulfill the increasing demand in mobile reporting without needing to upgrade to the latest version of the platform. Here at University of Central Florida (UCF), we were able to create reports targeting our iPad consumers at our executive level by using the SAS^® 9.2 Enterprise Business Intelligence environment, specifically SAS^® Web Report Studio 4.3. These reports provide them with the relevant data for their decision-making process. At UCF, the goal is to provide executive consumers with reports that fit on one screen in order to avoid the need of scrolling and that are easily exportable to PDF. This is done in order to respond to their demand to be able to accomodate their increasing use of portable technology to share sensitive data in a timely manner. The technical challenge is to provide specific data to those executive users requesting access through their iPad devices. Compatibility issues arise but are successfully bypassed. We are able to provide reports that fit on one screen and that can be opened as a PDF if needed. These enhanced capabilities were requested and well received by our users. This paper presents techniques we use in order to create mobile reports.

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.

Guidelines from the International Conference on Harmonisation (ICH) suggest that clinical trial data should be actively monitored to ensure data quality. Traditional interpretation of this guidance has often led to 100 percent source data verification (SDV) of respective case report forms through on-site monitoring. Such monitoring activities can also identify deficiencies in site training and uncover fraudulent behavior. However, such extensive on-site review is time-consuming, expensive and, as is true for any manual effort, limited in scope and prone to error. In contrast, risk-based monitoring makes use of central computerized review of clinical trial data and site metrics to determine whether sites should receive more extensive quality review through on-site monitoring visits. We demonstrate a risk-based monitoring solution within JMP^® Clinical to assess clinical trial data quality. Further, we describe a suite of tools used for identifying potentially fraudulent data at clinical sites. Data from a clinical trial of patients who experienced an aneurysmal subarachnoid hemorrhage provide illustration.

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)

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.

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.

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.

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.

Changes in health insurance and other industries often have a spatial component. Maps can be used to convey this type of information to the user more quickly than tabular reports and other non-graphical formats. SAS^® provides programmers and analysts with the tools to not only create professional and colorful maps, but also the ability to display spatial data on these maps in a meaningful manner that aids in the understanding of the changes that have transpired. This paper illustrates the creation of a number of different maps for displaying change over time with examples from the health insurance arena.

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.

As a retailer, your bottom line is determined by supply and demand. Are you supplying what your customer is demanding? Or do they have to go look somewhere else? Accurate allocation and size optimization mean your customer will find what they want more often. And that means more sales, higher profits, and fewer losses for your organization. In this session, Linda Canada will share how DSW went from static allocation models without size capability to precision allocation using intelligent, dynamic models that incorporate item plans and size optimization.

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.

In the past, calibration was done by using extremely complicated macros in Base SAS^® to create a Microsoft Excel workbook with multiple linked spreadsheets. This process made it hard to audit, was not reliably replicable, and was open to user error. The task was to create a replicable, auditable, and locked down application that allowed the user to change certain parameters and see the impact of those changes without needing to code. SAS^® Stored Processes are used to generate a screen that is split into three sections: one shows static reporting, the second is a data-driven custom input form, and the third shows test results. The initial screen uses a standard stored process that enables the user to select the model and time period. Macro variables are passed through to subset data. The Static reports are created from a stored process that executes two REPORT procedures that subset the data based on the passed parameters. The form is built using SAS^® to generate HTML and is data driven. The Update button at the end of the form executes a stored process that collects the data that the user has entered into the form and updates a database. After the rates have been updated, they are used to generate test results using PROC REPORT.

SAS^® Merchandise Planning introduces key changes with the recent 6.4 release and the upcoming 6.5 release. This session highlights the integration to SAS^® Visual Analytics, the analytic infrastructure that enables users to integrate analytic results into their planning decisions, as well as multiple usability enhancements. Included is a look at the first of the packaged analytics that include the Recommended Assortment analytic.

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.