数据仓库概念2

XuXinPresalesConsultantOracle(China)Co.,Ltd.数据仓库的概念Whatis...数据仓库(DataWarehouse)/数据集市(DataMart)决策支持系统(DecisionSupportSystem)联机分析处理(OLAP)/ROLAP/MOLAP元数据(MetaData)分析指标(Measure)/维(Dimension)星型模型(StarSchema)/雪花模型(SnowSchema)数据钻入/数据钻出(DrillDown/DrillUp)表旋转(TableRotation)数据挖掘(DataMining)数据仓库几大功能Query/ReportDrillup/DrillDownCompareExceptionForcast,WhatifDataMining数据仓库实施方法建立数据仓库需要考虑的因素扩展性

灵活性集成性可靠性数据仓库专家的建议需要业务人员的积极参与通过原型设计验证需求确定数据仓库的范围，不要试图Warehouse所有数据为不同需求选择合适工具控制风险利用外部Consultant的经验重点放在不同系统的集成建立数据仓库举例

UseaBuildingEstateOLTPdatabaseasanexampletoillustratetheconceptsandhowtobuildasuccessfulDataWarehousewhichusedtocheckandforecasttherentalrateandsellamountinHongKong.步骤1：确定数据仓库的问题范围列出4月份香港地区每日房屋销售情况找出销售额大于4百万的居民住宅项目比较Whampoo和Kornhill地区上月销售情况找出售屋数量最多的前3个地区截止到当月的累计销售数量用图表反映最佳销售模式时间序列分析确定数据据仓库的的问题范范围确定业务务需求和和用户需需求:用户查询询执行的的频度系统保留留数据的的年限用户主要要希望从从哪些角角度,哪哪些层层次分析析数据数据源是是哪些系系统步骤2:选选择合适适的软硬硬件平台台可靠的供供应商数据建模模和管理理工具易用性开放集中管理理性能并行处理理选择数据据库平台台的依据据：前3位的的考虑因因素:易用性92.4%集中管理理65.2%可靠的供供应商65.2%数据仓库库的考虑虑因素(Source:DataWarehouseInstitute-February96)MOLAP还还是ROLAP？ClientinterfaceMetadataOLAPengineMDDBDataWarehouseOperationalDataDataloadingOLAPserverSQLpassthroughOLAPengineOLAPserverDataWarehouseMetadataClientinterfaceMOLAPROLAPMiddle-tierserverCLIENTDatabaseserverSQLROLAP和和MOLAP的功能能区别Legacy/Operational/ExternalSystemsTransactionSystemsDecisionSupportSystemsKnowledgeWorkersStrategicTacticalWhat-IfAnalysisCustomizedApplicationsDrill&PivotAnalysisForecastingModelingTime-SeriesQuery&ReportingMOLAPROLAPMDBRDBMSDataCachelinkage步骤3：根根据需要创创建新的实实体#Code_noNo_of_transactionConstructor_IDDeveloper_IDBuildingdatePurchasedatePurchasepriceAddressAreaApartment#Code_no#Transaction_noName/CompanyHKIDContactPhone#ContactAddressPurchaseDatePurchasePriceOwner#Code_no#Flat#Transaction_noNameHKIDOccupy_type(P,R)ContactPhone#ContactAddressDatePriceOccupantContractor_IDCompanyNameAddressContactPhone#Constructor#Code_no#FlatNo_of_transTypeFloorArea(Building)Area(Actual)FlatDetailsDeveloper_IDCompanyNameAddressContactPhone#DeveloperDayMonthQuarterYearTimeTerritoryDistrictRegionBuilding/EstateGeographicLocationTypeSizeAreaHousingTypes步骤骤4:确确定定维维表表删删除除不不必必要要的的表表#Code_noNo_of_transactionConstructor_IDDeveloper_IDBuildingdatePurchasedatePurchasepriceAddressAreaApartment#Code_no#Transaction_noName/CompanyHKIDContactPhone#ContactAddressPurchaseDatePurchasePriceOwner#Code_no#Flat#Transaction_noNameHKIDOccupy_type(P,R)ContactPhone#ContactAddressDatePriceOccupantContractor_IDCompanyNameAddressContactPhone#Constructor#Code_no#FlatNo_of_transTypeFloorArea(Building)Area(Actual)FlatDetailsDeveloper_IDCompanyNameAddressContactPhone#DeveloperDayMonthQuarterYearTimeTerritoryDistrictRegionBuilding/EstateGeographicLocationTypeSizeAreaHousingTypes步骤骤5:建建立立层层次次结结构构Date1-Jan-9413-Jun-9512-Jan-9612-Apr-9615-Apr-9620-Oct-9620-Oct-9612-Dec-961-Jan-9731-Mar-9715-Apr-97?.TimeYearQuarterMonthDayTimeHierarchy步骤骤6:确确定定属属性性ClassSSMMMLLLSMLSMAreasq.m10-39.940-49.950-69.970-99.9100-129.9130-159.9160-199.9Over20010-129.9130-299.9Over30010-49.950-129.9SizeABCDETypeDomesticIndustrialShops&CommercialOthers/MiscAreasq.m2563035125400810400300100210300?.TypeSizeAreaClass:AttributesofHousingTypeHousingTypeOccupantHousingTypedimensionlookuptableAttributes步骤骤7:建建立立FactTable，，确确定定合合适适的的粒粒度度TimeLocationTypeAreaOccupantNamePurchasePriceRent?.SalesFactTable步骤8:建建立数据仓仓库模型BuildingEstateOLTPEnvironmentTimeLocationTypeAreaOccupantNamePurchasePriceRent?.SalesFactTableDayMonthQuarterYearTimeTerritoryDistrictRegionBuilding/EstateGeographicLocationTypeSizeAreaHousingTypes#Code_noNo_of_transactionConstructor_IDDeveloper_IDBuildingdatePurchasedatePurchasepriceAddressAreaApartment#Code_no#Transaction_noName/CompanyHKIDContactPhone#ContactAddressPurchaseDatePurchasePriceOwner#Code_no#Flat#Transaction_noNameHKIDOccupy_type(P,R)ContactPhone#ContactAddressDatePriceOccupantContractor_IDCompanyNameAddressContactPhone#Constructor#Code_no#FlatNo_of_transHousingTypeFloorArea(Building)Area(Actual)FlatDetailsDeveloper_IDCompanyNameAddressContactPhone#DeveloperTransformBuildingEstateDataWarehouseOLAPEnvironment步骤9:数数据仓仓库模模型优优化TerritoryDistrictTimeLocationTypeAreaOccupantNamePurchasePriceRent?.SalesFactTableTypeSizeAreaDistrictDiatrictRegionRegionRegionBuilding/EstateYearQuarterQuarterYearQuarterMonthQuarterMonthDayMonthDayHousingTypesTimeLocationTypeAreaOccupantNamePurchasePriceRent?.SalesFactTableDayMonthQuarterYearTimeTerritoryDistrictRegionBuilding/EstateGeographicLocationTypeSizeAreaHousingTypesStarschemaSnowflakeschema数据据仓仓库库设设计计优优化化的的原原则则避免免数数据据实实时时汇汇总总(建建立立汇汇总总表表)减少少表表连连接接操操作作(不不要要超超过过3-5个个)用IDcode作作关关键键字字减少少I/O竞竞争争利用用分分区区技技术术提提高高性性能能和和可可管管理理性性估算算数数据据仓仓库库容容量量的的算算法法X=sizeofonerowinthefacttableTherefore,SalesFactTable=98

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