Data Warehouses and OLAP — Slides for Textbook — — Chapter 2 —What is Data Warehouse?Data Warehouse—Subject-OrientedData Warehouse—IntegratedData Warehouse—Time VariantData Warehouse—Non-VolatileData Warehouse vs. Heterogeneous DBMSData Warehouse vs. Operational DBMSOLTP vs. OLAPWhy Separate Data Warehouse?Data Warehouse UsageThree Data Warehouse ModelsOLAP TerminologyConceptual Modeling of Data WarehousesExample of Star SchemaExample of Fact ConstellationA Data Mining Query Language, DMQL: Language PrimitivesDefining a Star Schema in DMQLDefining a Fact Constellation in DMQLMeasures: Three CategoriesA Concept Hierarchy: Dimension (location)View of Warehouses and HierarchiesMultidimensional DataA Sample Data CubeBrowsing a Data CubeTypical OLAP OperationsA Star-Net Query ModelViews and Decision SupportIssues in View MaterializationDiscovery-Driven Exploration of Data CubesExamples: Discovery-Driven Data CubesSummaryReferences (I)References (II)Han: Dataware Houses and OLAP1Data Warehousesand OLAP — Slides for Textbook — — Chapter 2 —©Jiawei Han and Micheline KamberIntelligent Database Systems Research LabSchool of Computing Science Simon Fraser University, Canadahttp://www.cs.sfu.caHan: Dataware Houses and OLAP2What is Data Warehouse?Defined in many different ways, but not rigorously.A decision support database that is maintained separately from the organization’s operational databaseSupport information processing by providing a solid platform of consolidated, historical data for analysis.“A data warehouse is a subject-oriented, integrated, time-variant, and nonvolatile collection of data in support of management’s decision-making process.”—W. H. InmonData warehousing:The process of constructing and using data warehousesHan: Dataware Houses and OLAP3Data Warehouse—Subject-OrientedOrganized around major subjects, such as customer, product, sales.Focusing on the modeling and analysis of data for decision makers, not on daily operations or transaction processing.Provide a simple and concise view around particular subject issues by excluding data that are not useful in the decision support process.Han: Dataware Houses and OLAP4Data Warehouse—IntegratedConstructed by integrating multiple, heterogeneous data sourcesrelational databases, flat files, on-line transaction recordsData cleaning and data integration techniques are applied.Ensure consistency in naming conventions, encoding structures, attribute measures, etc. among different data sourcesE.g., Hotel price: currency, tax, breakfast covered, etc.When data is moved to the warehouse, it is converted.Han: Dataware Houses and OLAP5Data Warehouse—Time VariantThe time horizon for the data warehouse is significantly longer than that of operational systems.Operational database: current value data.Data warehouse data: provide information from a historical perspective (e.g., past 5-10 years)Every key structure in the data warehouseContains an element of time, explicitly or implicitlyBut the key of operational data may or may not contain “time element”.Han: Dataware Houses and OLAP6Data Warehouse—Non-VolatileA physically separate store of data transformed from the operational environment.Operational update of data does not occur in the data warehouse environment.Does not require transaction processing, recovery, and concurrency control mechanismsRequires only two operations in data accessing: initial loading of data and access of data.Han: Dataware Houses and OLAP7Data Warehouse vs. Heterogeneous DBMSTraditional heterogeneous DB integration: Build wrappers/mediators on top of heterogeneous databases Query driven approachWhen a query is posed to a client site, a meta-dictionary is used to translate the query into queries appropriate for individual heterogeneous sites involved, and the results are integrated into a global answer setComplex information filtering, compete for resourcesData warehouse: update-driven, high performanceInformation from heterogeneous sources is integrated in advance and stored in warehouses for direct query and analysisHan: Dataware Houses and OLAP8Data Warehouse vs. Operational DBMSOLTP (on-line transaction processing)Major task of traditional relational DBMSDay-to-day operations: purchasing, inventory, banking, manufacturing, payroll, registration, accounting, etc.OLAP (on-line analytical processing)Major task of data warehouse systemData analysis and decision makingDistinct features (OLTP vs. OLAP):User and system orientation: customer vs. marketData contents: current, detailed vs. historical, consolidatedDatabase design: ER + application vs. star + subjectView: current, local vs. evolutionary, integratedAccess patterns: update vs. read-only but complex queriesHan: Dataware Houses and OLAP9OLTP vs. OLAP OLTP OLAP users clerk, IT professional knowledge worker function day to day operations decision support DB design application-oriented subject-oriented data current, up-to-date detailed, flat relational isolated historical, summarized, multidimensional integrated, consolidated usage repetitive ad-hoc access read/write index/hash on prim. key lots of scans unit of work short, simple transaction complex query # records accessed tens millions #users thousands hundreds DB size 100MB-GB 100GB-TB metric transaction throughput query throughput, response Data WarehouseHan: Dataware Houses and OLAP10Why Separate Data Warehouse?High performance for both systemsDBMS— tuned for OLTP: access methods, indexing, concurrency control, recoveryWarehouse—tuned for OLAP: complex OLAP queries, multidimensional view, consolidation.Different functions and different data:missing data: Decision support requires historical data which operational DBs do not typically maintaindata consolidation: DS requires consolidation (aggregation, summarization) of data from heterogeneous sourcesdata quality: different sources typically use inconsistent data representations, codes and formats which have to be reconciledHan: Dataware Houses and OLAP11Data Warehouse UsageThree kinds of data warehouse applicationsInformation processingsupports querying, basic statistical analysis, and reporting using crosstabs, tables, charts and graphsAnalytical processing and Interactive Analysismultidimensional analysis of