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1、IncrementallyMaintainingClassi?cationusinganRDBMSM.LeventKocChristopherRe′UniversityofWisconsin-MadisonUniversityofWisconsin-Madisonkoc@cs.wisc.educhrisre@cs.wisc.eduABSTRACT(training)amodel(whichdependsonT)andthenusingthatmodeltolabeleachentityinE.Classi?cationiswidelyused,Theproli
2、ferationofimprecisedatahasmotivatedbothre-e.g.,inextractingstructurefromWebtext[12,27],indatasearchersandthedatabaseindustrytopushstatisticaltech-integration[13],andinbusinessintelligence[7,14,22].niquesintorelationaldatabasemanagementsystems(RDBM-Manyoftheseapplicationscenariosareh
3、ighlydynamic:Ses).Westudystrategiestomaintainmodel-basedviewsnewdataandupdatestothedataareconstantlyarriving.forapopularstatisticaltechnique,classi?cation,insideanForexample,aWebportalthatpublishesinformationforRDBMSinthepresenceofupdates(tothesetoftrainingtheresearchcommunity,itmus
4、tkeepupwiththenewpa-examples).Wemakethreetechnicalcontributions:(1)Apersthatareconstantlypublished,newconferencesthatstrategythatincrementallymaintainsclassi?cationinsideareconstantlyannounced,etc.SimilarproblemsarefacedanRDBMS.(2)AnanalysisoftheabovealgorithmthatbyservicessuchasTwi
5、tterorFacebookthathavelargeshowsthatouralgorithmisoptimalamongalldeterminis-amountsofusergeneratedcontent.Unfortunately,currentticalgorithms(andasymptoticallywithinafactorof2ofapproachestointegratingclassi?erswithanRDBMStreatanon-deterministicoptimalstrategy).(3)Anovelhybrid-classi?
6、ersasadataminingtool[22].Indataminingscenar-architecturebasedonthetechnicalideasthatunderlietheios,thegoalistobuildaclassi?cationmodelforananalyst,abovealgorithmwhichallowsustostoreonlyafractionofandsoclassi?cationisusedinabatch-orientedmanner;intheentitiesinmemory.Weapplyourtechniq
7、uestotextpro-contrast,intheabovescenarios,theclassi?cationtaskiscessing,andwedemonstratethatouralgorithmsprovideanintegratedintotherun-timeoperationoftheapplication.orderofmagnitudeimprovementovernon-incrementalap-AsmanyoftheseapplicationsareoftenbuiltonRDBM-proachestoclassi?cationo
8、navarietyofdatasets