Decision Support Systems - Chapter 5: Data Management

Chapter 5 Data Management Decision Support Systems 1© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and LiangOutline1.Data, Information, Knowledge2.Data collection, problems and quality3.Database Management Systems in DSS4.Data warehousing5.OLAP6.Data Mining7.Data Visualization and Multidimensionality8.GIS2© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang1.Data, Information, KnowledgeDataItems that are the most elementary descriptions of things, events, activities, and transactionsMay be internal or externalInformationOrganized data that has meaning and valueKnowledgeProcessed data or information that conveys understanding, experience or learning applicable to a problem or activity3© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and LiangData SourcesInternal dataExternal dataWebGovernment reports and filesResearch institutesStatistic bureausLocal banksChambers of commercesCommercial databasesSell access to specialized databases4© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang2. Data collectionRaw data collected manually or by instrumentsQuality is criticalQuality determines usefulnessContextual data qualityIntrinsic data qualityAccessibility data qualityRepresentation data qualityOften neglected or casually handledProblems exposed when data is summarized5© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang6© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and LiangData qualityCleanse dataWhen populating warehouseData quality action planBest practices for data qualityMeasure resultsData integrity. There are five issues:UniformityVersionCompleteness checkConformity checkGenealogy or drill-down7© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and LiangData access and integrationData IntegrationAccess needed to multiple sourcesOften enterprise-wide Disparate and heterogeneous databasesXML becoming language standard8© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang3.Database Management SystemsDBMS is a software program. It is designed toSupplement operating systemManage dataQuery data and generate reportsEnsure data securityFor DSS application, DBMS combines with modeling language for construction of DSS9© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and LiangDatabase organization and structuresHierarchicalTop down, like inverted treeFields have only one “parent”, each “parent” can have multiple “children”FastNetwork Relationships created through linked lists, using pointers“Children” can have multiple “parents”Greater flexibility, substantial overheadRelationalFlat, two-dimensional tables with multiple access queriesExamines relations between multiple tablesFlexible, quick, and extendable with data independenceObject orientedData analyzed at conceptual levelInheritance, abstraction, encapsulation10© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang11© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and LiangDatabase Models, continuedMultimedia BasedMultiple data formatsJPEG, GIF, bitmap, PNG, sound, video, virtual realityRequires specific hardware for full feature availabilityDocument BasedDocument storage and managementIntelligent databasesArtificial Intelligence Technologies, ES, and ANN can make the access and manipulation of complex databases simpler.To enhance DBMS with Inference engines  intelligent datbases.12© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang4.Data WarehouseSubject-orientedScrubbed so that data from heterogeneous sources are standardizedTime-variant; no current statusNonvolatile Read onlySummarizedNot normalized; may be redundantData from both internal and external sources is presentMetadata includedData about dataBusiness metadataSemantic metadata13© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and LiangData warehouse architectureMay have one or more tiersDetermined by warehouse, data acquisition (back end), and client (front end)One tier, where all run on same platform, is rareTwo tier usually combines DSS engine (client) with warehouseMore economicalThree tier separates these functional parts14© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang15© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang16© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and LiangMigrating DataBusiness rulesStored in metadata repositoryApplied to data warehouse centrallyData extracted from all relevant sourcesLoaded through data-transformation tools or programsSeparate operation and decision support environmentsCorrect problems in quality before data storedCleanse and organize in consistent manner17© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and LiangData Warehouse DesignDimensional modelingRetrieval basedImplemented by star schemaCentral fact tableDimension tablesGrainHighest level of detailDrill-down analysis18© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and LiangData Warehouse DevelopmentData warehouse implementation techniques: Top downThe data warehouse is the center of the analytic environment. The design and implementation of all other aspects are based on it.Bottom upThe goal is to deliver business value by deploying multidimensional data marts quickly. Later these are organized into a data warehouse.HybridFederatedThis approach creates and maintains a logical view of a single warehouse whereas the data reside in separate systems.19© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and LiangData Warehouse DevelopmentProjects may be data-centric or application-centricA data-centric warehouse is based upon a data model that is independent of any applications.An application-centric warehouse is one initially designed to support a single initiative or small set of initiatives.Implementation factorsOrganizational issuesProject issuesTechnical issuesScalability.A data warehouse needs to support scalabilityFlexibility20© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and LiangData MartsDependentCreated from warehouseReplicated Functional subset of warehouseIndependentScaled down, less expensive version of data warehouseDesigned for a department or strategic business unit (SBU)Organization may have multiple data martsDifficult to integrate21© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang5. OLAPActivities performed by end users in online systemsSpecific, open-ended query generationSQLAd hoc reportsStatistical analysisBuilding DSS applicationsModeling and visualization capabilitiesSpecial class of toolsDSS/BI/BA front endsData access front endsDatabase front endsVisual information access systems22© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang6.Data MiningOrganizes and employs information and knowledge from databasesStatistical, mathematical, artificial intelligence, and machine-learning techniques Automatic and fastTools look for patterns Simple models Intermediate modelsComplex Models23© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and LiangData MiningData mining application classes of problemsClassificationClusteringAssociationSequencingRegressionForecastingOthersHypothesis or discovery drivenIterativeScalable24© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and LiangData Mining Tools and TechniquesData miningStatistical methodsDecision treesCase based reasoningNeural computingIntelligent agentsGenetic algorithmsText MiningHidden contentGroup by themesDetermine relationships25© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and LiangKnowledge Discovery in DatabasesData mining used to find patterns in data.KDD process consists ofSelection: Identification of dataPreprocessingTransformation to common formatData mining through algorithmsEvaluation26© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang7.Data VisualizationData visualization: technologies that supports visualization and interpretation of data and information.Digital imaging, GIS, GUI, tables, multidimensions, graphs, Virtual Reality (VR), 3D, animationIdentify relationships and trendsData manipulation allows real time look at performance data27© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and LiangMultidimensionalityData organized according to business standards, not analystsConceptualThree factors in multidimensionality:DimensionsMeasuresTimeMultidimentionality has some limitations:Significant overhead and storageExpensiveComplex28© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang8.GISA GIS is a computer-based system for managing and manipulating data with digitized maps.By integrating spatially oriented databases with other databases, users can generate information for planning, problem solving and decision making.Geographic spreadsheet to model business activities and perform what-if analysis.Software allows web access to mapsGIS can be used for modeling and simulations29© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang30© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang