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 TitleDate AddedCompany
whitepaper Data Mining to Predict the Occurrence of Resistant Infection2008-03-13 SAS Institute
  In order to demonstrate the use of predictive modeling in SAS Enterprise Miner, the paper will examine the problem of resistant infection in the hospital using data from the National Inpatient Sample. The National Inpatient Sample contains approximately 8 million records and represents 37 states. A sample of 800,000 inpatient events or about 10% of the records will be used to investigate the problem. Of 800,000 events, 6000 or 0.75% of the records had a diagnosis of resistant infection. Using data visualization techniques and predictive modeling as well as SAS Text Miner, the paper found the infection treated with an infusion of antibiotics in only 197 patients, with an infusion of linezolid in 95 patients, indicating the use of antibiotics is under-reported, or under-utilized.

Tags: Data Mining - Analysis		   
whitepaper Tailoring the Use of SAS Enterprise Miner2008-03-13 SAS Institute
  A growing number of SAS users with different goals and skill levels need access to data mining functionality. The new generation of SAS Enterprise Miner 5 and the SAS stored process facility provide an easy way to tailor data mining functionality to the user's needs. The flexible architecture of SAS Enterprise Miner and the integration of Enterprise Miner into the SAS Enterprise Intelligence architecture allow the product users to create data mining projects for interactive or batch execution and share projects with other users. The software's Extension facility allows users to build specific functions that are fully integrated into the Enterprise Miner workbench.

Tags: Data Mining - Analysis		   
whitepaper A SAS Text Mining Approach to Predicting the Resolvability of Disputes Between eBay's Sellers and Buyers2008-03-13 Texas Tech University
  A well functioning reputation and feedback system is foundational in consumer-to-consumer electronic commerce, an example of which is the electronic auction. One's interest in this paper is the analysis of the predictive power of both buyer and seller comments in determining the resolvability of transaction disputes in online auctions. Using data gathered from the eBay, Inc. reputation system, the paper analyzes buyer and seller comments using the versatile SAS Enterprise Miner software Text Miner node. In the analysis the paper employs a binary target variable "Resolvable" to indicate whether an auction dispute has the possibility of being resolved to the satisfaction of buyer and seller.

Tags: Data Mining - Analysis		   
whitepaper Text and Data Mining to Investigate Expenditures on Prescribed Medicines2008-03-13 SAS Institute
  This Prescribed Medicines File from MEPS (Medical Expenditure Panel Survey) provides detailed information on household-reported prescribed medicines for a nationally representative sample of the civilian, non-institutionalized population of the United States and can be used to make estimates of prescribed medicine utilization and expenditures. For this study, the paper focused on the total cost, self-pay, Medicare cost, Medicaid cost and private insurance. It is the purpose of this study to determine the relationship between medication and patient condition, and to examine cost and patient condition in the analysis of these data. The paper uses Text Miner to examine combinations of medications used in relationship to patient therapeutics. In addition, the paper uses time series methods to investigate costs in relationship to patient therapeutics.

Tags: Programming Languages, Data Mining - Analysis		   
whitepaper Going Beyond Simple Information Maps to Improve Access to Data Sources2008-03-13 SAS Institute
  SAS Information Maps provide users with easy access to rather simply-defined data sources. Although they can be directed to external relational databases, performance can be an issue due to the way in which queries are built and passed via the LIBNAME engine within the SAS/Access products. In addition, sometimes it is necessary to perform nested queries to databases in order to provide more meaningful information in reports. This paper will explore options to assure performance and go beyond simple table joins to provide easier access to end users.

Tags: Business Intelligence - Data Warehousing		   
whitepaper SAS for Real-Time Applications2008-03-13 SAS Institute
  The SAS language is well suited for expressing program logic; people have been using it for over 30 years. There are many applications for building predictive models and then deploying these models to provide scores "On demand" as integrated components in a larger system. The paper describes and explores several patterns for using SAS in a real-time or near-real-time context, and describes the performance characteristics of each pattern. 1. Stored Processes; 2. Web Services; 3. Raw Sockets; 4. Message Queues.

Tags: Business Intelligence - Data Warehousing, Application Development		   
whitepaper New Architecture and Speed With a Netezza Data Warehouse Appliance2008-03-13 SAS Institute
  One of the biggest challenges organizations have is how they store and retrieve for analysis large amounts of data. This problem has plagued industries for years. As data sizes grow into Terabytes and Petabytes, organizations continue to face these challenges as users want to analyze and report on 'The whole data'. SAS has partnered with Netezza, a Data Warehouse Appliance vendor. This vendor is a pioneer of the self-contained warehouse appliance, which contains a database, storage and SQL processing. Each appliance contains 108 computers and storage disks to offer intelligent, massive parallel processing with load rates of 500GB per hour, all at an economical price in comparison to Teradata.

Tags: Data Mining - Analysis, Business Intelligence - Data Warehousing		   
whitepaper Academic Business Intelligence System Development Using SAS Tools2008-03-13 Universiti Utara Malaysia
  Managing an organization requires access to information in order to monitor the organization activities and assess its performance. Indeed, the increasing demand for information plus the growing data volumes and customer populations can pose problems to the organization. A way to tackle these issues can be found in Business Intelligence (BI) solutions, which provide organizations with timely and integrated information that is crucial to the understanding of the business environment and customer needs. These solutions, usually in the form of BI systems allow the organizations to gather, store, access and analyze corporate data sources for business planning and decision-making. Academic institutions, an example of these organizations, require information too for planning their academic resources and achieving academic excellence.

Tags: Business Intelligence - Data Warehousing		   
whitepaper Use of Text Mining to Predict Patient Compliance2008-03-13 SAS Institute
  The purpose of this study is to examine standards of care in the Dental School of the University of Louisville. The central theme is to consider issue of compliance on behalf of the patients and how to define it in an unbiased way. The paper will examine the relationship of visit intervals, treatment needs, and patient compliance. With the use of SAS 9.1.3 software, data mining techniques such as clustering, kernel density, linear models and mixed models estimation will be used to define and analyze compliance. Statistical methods such as text mining were used to examine the severity of patient conditions. Confidence interval estimation and bootstrapping were explored to assist with the allocation of patients to compliance levels.

Tags: Data Mining - Analysis, Security Standards		   
whitepaper Lithium Battery Analysis: Probability of Failure Assessment Using Logistic Regression2008-03-13 SAS Institute
  Fourteen-hundred rows by 53 columns of vendor cell acceptance data were processed though Logistic Regression using SAS Enterprise Miner (EM) to find any significant correlation between 52 test output parameters (independent variables) and the pass/fail outcome for each of the 1,400 battery cells tested. The goal was to find helpful predictors for detecting "Good" or "Bad" cells in the form of a best Logistic Regression model. Data from five cells selected by Johnson Space Center's (JSC's) Energy Systems Division (ESD) were processed through three model options (Option1, Option2, and Option3) to determine the best model and to indicate a known cell that failed. The output from the best model showed good acceptability statistics and indicated the failed cell was less acceptable than the other cells.

Tags: Data Mining - Analysis