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1、IncrementallyMaintainingClassificationusinganRDBMSM.LeventKocChristopherRe´UniversityofWisconsin-MadisonUniversityofWisconsin-Madisonkoc@cs.wisc.educhrisre@cs.wisc.eduABSTRACT(training)amodel(whichdependsonT)andthenusingthatmodeltolabeleachentityinE.Classificationiswidelyused,Theproli
2、ferationofimprecisedatahasmotivatedbothre-e.g.,inextractingstructurefromWebtext[12,27],indatasearchersandthedatabaseindustrytopushstatisticaltech-integration[13],andinbusinessintelligence[7,14,22].niquesintorelationaldatabasemanagementsystems(RDBM-Manyoftheseapplicationscenariosareh
3、ighlydynamic:Ses).Westudystrategiestomaintainmodel-basedviewsnewdataandupdatestothedataareconstantlyarriving.forapopularstatisticaltechnique,classification,insideanForexample,aWebportalthatpublishesinformationforRDBMSinthepresenceofupdates(tothesetoftrainingtheresearchcommunity,itmus
4、tkeepupwiththenewpa-examples).Wemakethreetechnicalcontributions:(1)Apersthatareconstantlypublished,newconferencesthatstrategythatincrementallymaintainsclassificationinsideareconstantlyannounced,etc.SimilarproblemsarefacedanRDBMS.(2)AnanalysisoftheabovealgorithmthatbyservicessuchasTwi
5、tterorFacebookthathavelargeshowsthatouralgorithmisoptimalamongalldeterminis-amountsofusergeneratedcontent.Unfortunately,currentticalgorithms(andasymptoticallywithinafactorof2ofapproachestointegratingclassifierswithanRDBMStreatanon-deterministicoptimalstrategy).(3)Anovelhybrid-classifi
6、ersasadataminingtool[22].Indataminingscenar-architecturebasedonthetechnicalideasthatunderlietheios,thegoalistobuildaclassificationmodelforananalyst,abovealgorithmwhichallowsustostoreonlyafractionofandsoclassificationisusedinabatch-orientedmanner;intheentitiesinmemory.Weapplyourtechniq
7、uestotextpro-contrast,intheabovescenarios,theclassificationtaskiscessing,andwedemonstratethatouralgorithmsprovideanintegratedintotherun-timeoperationoftheapplication.orderofmagnitudeimprovementovernon-incrementalap-AsmanyoftheseapplicationsareoftenbuiltonRDBM-proachestoclassificationo
8、navarietyofdatasets
