Encyclopedia of Computer Science and Technology

decision support system 139data warehouseModern business organizations create and store a tremendousamount of data in the form of transactions that becomedatabase records. Increasingly, however, businesses arerelying on their ability to use data that was collected for onepurpose (such as sales, customer service, and inventory)for purposes of marketing research, planning, or decisionsupport. For example, transaction data might be revisitedwith a view to identifying the common characteristics ofthe firm’s best customers or determining the best way tomarket a particular type of product. In order to conductsuch research or analysis, the data collected in the course ofbusiness must be stored in such a way that it is both accurateand flexible in terms of the number of different ways inwhich it can be queried. The idea of the data warehouse isto provide such a repository for data.When data is used for particular purposes such as salesor inventory control, it is usually structured in recordswhere certain fields (such as stock number or quantity)are routinely processed. It is not so easy to ask a differentquestion such as “which customers who bought thisproduct from us also bought this other product within sixmonths of their first purchase?” One way to make it easierto query data in new ways is to store the data not in recordsbut in arrays where, for example, one dimension mightbe product numbers and another categories of customers.This approach, called Online Analytical Processing (OLAP)makes it possible to extract a large variety of relationshipswithout being limited by the original record structure.ImplementationThe key in designing a data warehouse is to provide a waythat researchers using analytical tools (such as statisticsprograms) can access the raw data in the underlying database.Software using query languages such as SQL canserve as such a link. Thus, the researcher can define a queryThe general process of warehousing data. The data warehouse addsvalue to the data by further structuring it so relationships can beexplored by analysts.using the many dimensions of the data array, and the OLAPsoftware (also called middleware) translates this query intothe appropriate combination of queries against the underlyingrelational database.The data warehouse is closely related to the conceptof data mining. In fact, data mining can be viewed as theexploitation of the collection of views, queries, and otherelements that can be generated using the data warehouse asthe infrastructure (see data mining).Further ReadingData Warehousing Information Center. Available online. URL:http://www.dwinfocenter.org/. Accessed July 8, 2007.DM Review/dataWarehouse.com Available online. URL: http://www.datawarehouse.com/. Accessed July 8, 2007.Inmon, W. H. Building the Data Warehouse. 4th ed. Indianapolis:Wiley, 2005.Kimball, Ralph, and Margy Ross. The Data Warehouse Toolkit: TheComplete Guide to Dimensional Modeling. 2nd ed. Indianapolis:Wiley, 2002.DBMS See database management system.decision support systemA decision support system (DSS) is a computer applicationthat focuses on providing access to or analysis of thekey information needed to make decisions, particularly inbusiness. (It can be thought of as a more narrowly focusedapproach to computer assistance to management—see managementinformation system.)The development of DSS has several roots reaching backto the 1950s. This includes operational analysis and the theoryof organizations and the development of the first interactive(rather than batch-processing) computer systems.Indeed, the SAGE automated air defense system developedstarting in the 1950s could be described as a military DSS.The system presented real-time information (radar plots)and enabled the operator to select and focus on particularelements using a light pen. By the 1960s more-systematicresearch on DSS was underway and included the provocativeidea of “human-computer symbiosis” for problem solving(see Licklider, J. C. R.).The “back end” of a DSS is one or more large databases(see data warehouse) that might be compiled from transactionrecords, statistics, online news services, or other sources.The “middle” of the DSS process includes the ability to analyzethe data (online analytical processing, or OLAP; see alsodata mining). Other elements that might be included in aDSS are rules-based systems (see expert system) and interactivemodels (see simulation). These elements can help theuser explore alternatives and “what if” scenarios.The structure of a DSS is sometimes described as modeldriven (generally using a small amount of selected data),data driven (based on a large collection of historical data),knowledge driven (perhaps using an expert system), orcommunications driven (focusing on use of collaborativesoftware—see groupware, as well as more recent developments)(see wikis and Wikipedia).