简介：Inthispaper,aneweffectivemethodisproposedtofindclassassociationrules(CAR),togetusefulclassassociationrules(UCAR)byremovingthespuriousclassassociationrules(SCAR),andtogenerateexceptionclassassociationrules(ECAR)foreachUCAR.CARmining,whichintegratesthetechniquesofclassificationandassociation,isofgreatinterestrecently.However,ithastwodrawbacks:oneisthatalargepartofCARsarespuriousandmaybemisleadingtousers;theotheristhatsomeimportantECARsaredifficulttofindusingtraditionaldataminingtechniques.Themethodintroducedinthispaperaimstogetovertheseflaws.Accordingtoourapproach,ausercanretrievecorrectinformationfromUCARsandknowtheinfluencefromdifferentconditionsbycheckingcorrespondingECARs.Experimentalresultsdemonstratetheeffectivenessofourproposedapproach.

简介：Forworkflow-basedservicecompositionapproach,therelationsbetweentheWebserviceQoSandenvironmentsareusuallynotconsidered,sothattheinformationaboutQoSforcompositeserviceselectionisinaccurate.Itmakestheselectedcompositeserviceinefficient,orevenunexecutable.Toaddressthisproblem,anovelservicecompositionapproachbasedonproductionQoSrulesisproposedinthispaper.Generally,itisverydifficulttodirectlyanalyzehowdifferentkindsofenvironmentfactorsinfluencetheWebserviceQoS.Weadopt'black-box'analysismethodofoptimizingcompositeservices,discoveringtheknowledgesuchas'theQoSofoneWebservicewillbehigherinspecificenvironments'.Inourapproach,theexecutioninformationofthecompositeserviceisrecordedintoalogfirst,whichwillbetakenasthebasisofthesubsequentstatisticalanalysisanddatamining.Then,thetimelyQoSvaluesoftheWebservicesareestimatedandtheproductionQoSrulesbeingusedtoqualitativelyexpressthedifferentperformancesoftheWebserviceQoSindifferentenvironmentsaremined.Atlast,weemploytheminedQoSknowledgeoftheWebservicestooptimizethecompositeserviceselection.Extensiveexperimentalresultsshowthatourapproachcanimprovetheperformanceofselectedcompositeservicesonthepremiseofassuringtheselectingcomputationcost.

简介：Inthispaper,ARMiner,adataminingtoolbasedonassociationrules,isintroduced.Beginningwiththesystemarchitecture,thecharacteristicsandfunctionsaredis-cussedindetails,includingdatatransfer,concepthierarchygeneralization,miningruleswithnegativeitemsandthere-developmentofthesystem.Anexampleofthetool'sapplicationisalsoshown.Finally,someissuesforfutureresearcharepresented.