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Title Date Added Company

What's New in SAS Web Report Studio 4.2 2008-03-24 SAS Institute

Web Report Studio is a web-based reporting tool with many capabilities added over the course of three major releases. The upcoming release adds many new features driven by requests from users. This paper organizes the key capabilities in Web Report Studio 4.2 into categories. Virtually all features affect the user interface, and the first section describes key UI enhancements that are designed to improve the user experience. New prompting makes reporting more flexible for consumers and can minimize the number of reports needed. OLAP cubes enable fast navigation of the data, and enhancements will get the specific rollups one need. Visualization helps one understand report information quickly, and Web Report Studio 4.2 includes a number of graph enhancements, such as annotated reference lines.

Tags: Business Intelligence - Data Warehousing, Web Reporting

Controlling OLAP Applications End to End 2008-03-24 SAS Institute

In SAS 9.2, there are several new features that help administrators to secure and control the use of OLAP Cubes in a reporting environment. This paper highlights the following new and existing features: ability to include or exclude members from aggregated values (parent values), member Level Security user interface, subsetting report data using Information Map Filters and roles controlling report functionality.

Tags: Database Management, Business Intelligence - Data Warehousing

What's New in SAS OLAP Cube Studio 4.2 2008-03-24 SAS Institute

With the release of SAS 9.2, new functionality and enhancements have been added to SAS OLAP Cube Studio 4.2. New features include the ability to: update a cube (incremental update), view an input data set within SAS OLAP Cube Studio, view a cube to validate the build process, set cube security in SAS OLAP Cube Studio including a UI for member level security and automatically generate suggested time hierarchies based on a single date column. This paper will highlight and demonstrate the new functionality and the benefits that the user will have with SAS OLAP Cube Studio 4.2.

Tags: Business Intelligence - Data Warehousing

Avoid Growing Pains: New Cube Update Features You Should Know About 2008-03-12 SAS Institute

After a SAS OLAP cube is created, it is possible to update the data for the cube without completely recreating it. The SAS 9.2 OLAP Server enables one to incrementally update SAS OLAP cubes. An incremental update involves adding cell data and members to an existing SAS OLAP cube. Incremental updates of a cube are generally faster than rebuilding the cube from the combined set of input data and update data. There are several decisions one must make before one creates the cube that will make the incremental update process run smoother. One will need to decide how to structure the cube and the input data as well as choose a method of update.

Tags: Database Management, Business Intelligence - Data Warehousing

It's 9:00am - Do You Know Where Your Critical Talent Is?: Retention Analytics for Human Capital Management 2008-02-18 SAS Institute

Employee retention is an increasingly serious issue in many business sectors. Understanding which factors cause employees to leave and which actions retain them is an important Business Intelligence application. This paper demonstrates analytic methods to address this problem. Data mining and predictive modeling can be used to improve retention of critical employees. The business user can use the retention analysis to generate reports that show how the loss of critical skills would affect an organization. Reports identify job groups, geographical regions, or organizational areas that have higher risk for employee voluntary termination. Additionally, the influential drivers to high-risk groups are identified to suggest the best course of action to reduce the risk.

Tags: HR, Human Capital Management

Two-Stage Variable Clustering for Large Data Sets 2008-02-15 SAS Institute

In data mining, principal component analysis is a popular dimension reduction technique. It also provides a good remedy for the multicollinearity problem, but its interpretation of input space is not as good. To overcome the interpretation problem, principal components (cluster components) are obtained through variable clustering, which was implemented with PROC VARCLUS. The procedure uses oblique principal components analysis and binary iterative splits for variable clustering, and it provides non-orthogonal principal components. Even if this procedure sacrifices the orthogonal property among principal components, it provides good interpretable principal components and well-explained cluster structures of variables. However, the PROC VARCLUS implementation is inefficient to deal with high-dimensional data. This paper introduces the two-stage, variable clustering technique for large data sets.

Business Activity Monitoring: Process Control for the Enterprise 2008-02-12 SL Corporation

As the nature of real-time information has evolved, the requirements for analysis and visualization of data has become more complex and sophisticated. There are key lessons to be learned from the process control industry's decades of experience in dealing with real-time mission-critical data.

This white paper discusses how lessons can be taken from traditional process monitoring applications and applied in today's more complex multi-dimensional environments in order to deliver more effective and successful business activity monitoring solutions for the enterprise.

Tags: Data Quality, Data Visualization, Data Center, High Performance Computing, Knowledge and Data Management, Decision Support - DW Front End, Business Intelligence - Data Warehousing

Intrusion Detection Using Data Mining Along Fuzzy Logic and Genetic Algorithms 2008-02-01 Nagarjuna University

Intrusion Detection is one of the important area of research. The work discussed in this paper has explored the possibility of integrating the fuzzy logic with Data Mining methods using Genetic Algorithms for intrusion detection. The reasons for introducing fuzzy logic is two fold, the first being the involvement of many quantitative features where there is no separation between normal operations and anomalies. Thus fuzzy association rules can be mined to find the abstract correlation among different security features. An architecture for Intrusion Detection methods has been proposed by using Data Mining algorithms to mine fuzzy association rules by extracting the best possible rules using Genetic Algorithms.

Tags: Security Tools, Intrusion Detection Systems

SAS Enterprise Miner Performance on IBM System p 570 2008-01-01 IBM

Turning increasing amounts of raw data into useful information have become increasingly important in today's highly competitive business environment, increasing the demand for predictive, analytics data mining solutions. SAS Enterprise Miner (EM), powerful and comprehensive data mining software, includes services and training to help organizations get started right away exploring large quantities of data to discover relationships and patterns that lead to proactive decision making. IBM's unmatched expertise in hardware and software technology, along with services, enables the SAS Enterprise Miner solution for AIX 5L on IBM POWER processor-based systems offers significant benefits. It can be deployed on an infrastructure that is designed to improve reliability, performance and scalability across diverse data mining projects commonly used in real-life production environments.

Tags: UNIX

Data Center Workload Monitoring, Analysis, and Emulation 2007-12-01 Duke University

Over the last ten years the author has witnessed a shift from large mainframe computing to commodity, off-the-shelf clusters of servers. Today's data centers contain thousands or tens of thousands of servers, providing services and computation for tens or hundreds of thousands of users. In addition to traditional IT challenges such as server management, security, and performance, data center owners now must deal with power and thermal issues, previously the domain of facilities management. These trends will continue to accelerate as organizations acquire bladed servers and consolidate multiple, smaller clusters into centrally-located data centers. However, in spite of these trends, there has been no corresponding change in emphasis in the methods and toolkits that target system instrumentation, analysis, management, replay, and emulation.

Tags: Data Center

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Title	Date Added	Company
What's New in SAS Web Report Studio 4.2	2008-03-24	SAS Institute
Web Report Studio is a web-based reporting tool with many capabilities added over the course of three major releases. The upcoming release adds many new features driven by requests from users. This paper organizes the key capabilities in Web Report Studio 4.2 into categories. Virtually all features affect the user interface, and the first section describes key UI enhancements that are designed to improve the user experience. New prompting makes reporting more flexible for consumers and can minimize the number of reports needed. OLAP cubes enable fast navigation of the data, and enhancements will get the specific rollups one need. Visualization helps one understand report information quickly, and Web Report Studio 4.2 includes a number of graph enhancements, such as annotated reference lines. Tags: Business Intelligence - Data Warehousing, Web Reporting
Controlling OLAP Applications End to End	2008-03-24	SAS Institute
In SAS 9.2, there are several new features that help administrators to secure and control the use of OLAP Cubes in a reporting environment. This paper highlights the following new and existing features: ability to include or exclude members from aggregated values (parent values), member Level Security user interface, subsetting report data using Information Map Filters and roles controlling report functionality. Tags: Database Management, Business Intelligence - Data Warehousing
What's New in SAS OLAP Cube Studio 4.2	2008-03-24	SAS Institute
With the release of SAS 9.2, new functionality and enhancements have been added to SAS OLAP Cube Studio 4.2. New features include the ability to: update a cube (incremental update), view an input data set within SAS OLAP Cube Studio, view a cube to validate the build process, set cube security in SAS OLAP Cube Studio including a UI for member level security and automatically generate suggested time hierarchies based on a single date column. This paper will highlight and demonstrate the new functionality and the benefits that the user will have with SAS OLAP Cube Studio 4.2. Tags: Business Intelligence - Data Warehousing
Avoid Growing Pains: New Cube Update Features You Should Know About	2008-03-12	SAS Institute
After a SAS OLAP cube is created, it is possible to update the data for the cube without completely recreating it. The SAS 9.2 OLAP Server enables one to incrementally update SAS OLAP cubes. An incremental update involves adding cell data and members to an existing SAS OLAP cube. Incremental updates of a cube are generally faster than rebuilding the cube from the combined set of input data and update data. There are several decisions one must make before one creates the cube that will make the incremental update process run smoother. One will need to decide how to structure the cube and the input data as well as choose a method of update. Tags: Database Management, Business Intelligence - Data Warehousing
It's 9:00am - Do You Know Where Your Critical Talent Is?: Retention Analytics for Human Capital Management	2008-02-18	SAS Institute
Employee retention is an increasingly serious issue in many business sectors. Understanding which factors cause employees to leave and which actions retain them is an important Business Intelligence application. This paper demonstrates analytic methods to address this problem. Data mining and predictive modeling can be used to improve retention of critical employees. The business user can use the retention analysis to generate reports that show how the loss of critical skills would affect an organization. Reports identify job groups, geographical regions, or organizational areas that have higher risk for employee voluntary termination. Additionally, the influential drivers to high-risk groups are identified to suggest the best course of action to reduce the risk. Tags: HR, Human Capital Management
Two-Stage Variable Clustering for Large Data Sets	2008-02-15	SAS Institute
In data mining, principal component analysis is a popular dimension reduction technique. It also provides a good remedy for the multicollinearity problem, but its interpretation of input space is not as good. To overcome the interpretation problem, principal components (cluster components) are obtained through variable clustering, which was implemented with PROC VARCLUS. The procedure uses oblique principal components analysis and binary iterative splits for variable clustering, and it provides non-orthogonal principal components. Even if this procedure sacrifices the orthogonal property among principal components, it provides good interpretable principal components and well-explained cluster structures of variables. However, the PROC VARCLUS implementation is inefficient to deal with high-dimensional data. This paper introduces the two-stage, variable clustering technique for large data sets.
Business Activity Monitoring: Process Control for the Enterprise	2008-02-12	SL Corporation
As the nature of real-time information has evolved, the requirements for analysis and visualization of data has become more complex and sophisticated. There are key lessons to be learned from the process control industry's decades of experience in dealing with real-time mission-critical data. This white paper discusses how lessons can be taken from traditional process monitoring applications and applied in today's more complex multi-dimensional environments in order to deliver more effective and successful business activity monitoring solutions for the enterprise. Tags: Data Quality, Data Visualization, Data Center, High Performance Computing, Knowledge and Data Management, Decision Support - DW Front End, Business Intelligence - Data Warehousing
Intrusion Detection Using Data Mining Along Fuzzy Logic and Genetic Algorithms	2008-02-01	Nagarjuna University
Intrusion Detection is one of the important area of research. The work discussed in this paper has explored the possibility of integrating the fuzzy logic with Data Mining methods using Genetic Algorithms for intrusion detection. The reasons for introducing fuzzy logic is two fold, the first being the involvement of many quantitative features where there is no separation between normal operations and anomalies. Thus fuzzy association rules can be mined to find the abstract correlation among different security features. An architecture for Intrusion Detection methods has been proposed by using Data Mining algorithms to mine fuzzy association rules by extracting the best possible rules using Genetic Algorithms. Tags: Security Tools, Intrusion Detection Systems
SAS Enterprise Miner Performance on IBM System p 570	2008-01-01	IBM
Turning increasing amounts of raw data into useful information have become increasingly important in today's highly competitive business environment, increasing the demand for predictive, analytics data mining solutions. SAS Enterprise Miner (EM), powerful and comprehensive data mining software, includes services and training to help organizations get started right away exploring large quantities of data to discover relationships and patterns that lead to proactive decision making. IBM's unmatched expertise in hardware and software technology, along with services, enables the SAS Enterprise Miner solution for AIX 5L on IBM POWER processor-based systems offers significant benefits. It can be deployed on an infrastructure that is designed to improve reliability, performance and scalability across diverse data mining projects commonly used in real-life production environments. Tags: UNIX
Data Center Workload Monitoring, Analysis, and Emulation	2007-12-01	Duke University
Over the last ten years the author has witnessed a shift from large mainframe computing to commodity, off-the-shelf clusters of servers. Today's data centers contain thousands or tens of thousands of servers, providing services and computation for tens or hundreds of thousands of users. In addition to traditional IT challenges such as server management, security, and performance, data center owners now must deal with power and thermal issues, previously the domain of facilities management. These trends will continue to accelerate as organizations acquire bladed servers and consolidate multiple, smaller clusters into centrally-located data centers. However, in spite of these trends, there has been no corresponding change in emphasis in the methods and toolkits that target system instrumentation, analysis, management, replay, and emulation. Tags: Data Center