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1、DimensionalModelingforDataWarehouseUmashankerSharma,AnjanaGosainGGS,IndraprasthaUniversity,DelhiAbstractstoresinformationinordertosatisfydecision-making2Manysurveysindicatethatasignificantpercentageofrequests.ThiskindofDBhasthefollowingparticularDWsfailtomeetbusinessobjectivesorareoutrig
2、htfeatures.Itcontainsdatathatistheresultoffailures.Inthispaper,wearedescribingabouttransformations,qualityimprovement,andintegrationofmultidimensionaldatamodelfortheDWs,whichwillhelpdatathatcomesfromoperationalbases,alsoincludinginsurroundfactswithasmuchrelevantcontextindicatorsthatgivei
3、tadditionalvalue.TheDWshavetodimensions.Multidimensionalityisjustadesignsupportcomplexquerieshoweveritsmaintenancedoestechniquethatseparatestheinformationintofactsandnotsupposetransactionalload.Thesefeaturescausethedimensions.Itgivesrisetoschemawithstarshape,designtechniquesandtheusedstr
4、ategiestobedifferentConsistsofafacttablewithasingletableforeachfromthetraditionalones.dimension.SnowflakeSchemaisavariationofstar•EnterpriseDW-AnenterpriseDWprovidesacentralschema,inwhichthedimensionaltablesfromastardatabasefordecisionsupportthroughouttheenterprise.schemaareorganizedinto
5、ahierarchybynormalizing•OperationalDataStore-Thishasabroadenterprisethem.Multidimensionalmodelpresentinformationtothewidescope,butunlikethereal.EnterprisesDW,dataisend-userinawaythatcorrespondstohisnormalrefreshedinnearrealtimeandusedforroutinebusinessunderstandingofhisbusiness,keyfigure
6、sorfactsfromactivity.thedifferentperspectivesthatinfluencethem.Weare•DataMart-Datamartisasubsetofdatawarehouseandsurveyedonpresentdifferentmultidimensionaldataitsupportsaparticularregion,businessunitorbusinesscubes,whicharethebasiclogicalmodelforOLAPfunction.applications.1.1-Characterist
7、icsofDW-ThemaincharacteristicsKeyWords:MultidimensionalModeling,dataofdatawarehouseare:warehouse,OLAP,FactSchema,Separate,TheDWisseparatefromtheoperationalsystemsinthecompany.Itgetsitsdataoutofthese1.Introductionlegacysystems.Available.ThetaskofaDWistomakedataaccessiblefo
