abstract discovering and ranking web services with basil a personalized approach with biase

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1、DiscoveringandRankingWebServiceswithBASIL:APersonalizedApproachwithBiasedFocusJamesCaverlee,LingLiu,andDanielRoccoGeorgiaInstituteofTechnologyCollegeofComputingAtlanta,GA30332,U.S.A.fcaverlee,lingliu,rockdjg@cc.gatech.eduABSTRACTData-intensivewebservicesprovideaccesstohugeandgrow-ingdatas

2、toresandsupporttoolsforsearching,manipulating,Inthispaperwepresentapersonalizedwebservicediscov-andanalyzingthosedatastores.Forexample,bothAmazon[1]eryandrankingtechniquefordiscoveringandrankingrelevantandGoogle[7]nowprovideXML-andSOAP-basedwebser-data-intensivewebservices.Ourrstprototype

3、{calledBASILviceinterfacestotheirunderlyingdatarepositorieswithsup-{supportsapersonalizedviewofdata-intensivewebservicesportforadvancedsearchoperatorsover,collectively,billionsofthroughsource-biasedfocus.BASILprovidesservicediscoveryitems.Inthelifesciencesdomain,manybioinformaticsservicesa

4、ndrankingthroughsource-biasedprobingandsource-biasedaretransitioningfromhuman-in-the-loopwebinterfacestotherelevancemetrics.Concretely,theBASILapproachhasthreewebservicesmodel[9],providingdirectaccesstounprecedenteduniquefeatures:(1)Itisabletodetermineinveryfewinter-amountsofrawdataandspec

5、ializedresearchtoolstoprovideactionswhetheratargetserviceisrelevanttothegivensourcehigh-levelanalysisandsearchoverthesedataservices.servicebyprobingthetargetwithverypreciseprobes;(2)ItWiththeincreasingvisibilityofwebservicesandtheService-canevaluateandranktherelevantservicesdiscoveredbased

6、onOrientedComputingparadigm[18],thereisagrowingneedasetofsource-biasedrelevancemetrics;and(3)Itcaniden-forecientmechanismsfordiscoveringandrankingservices.tifyinterestingtypesofrelationshipsforeachsourceserviceEectivemechanismsforwebservicediscoveryandrankingarewithrespecttootherdiscover

7、edservices,whichcanbeusedcriticalfororganizationstotakeadvantageofthetremendousasvalue-addedmetadataforeachservice.Wealsointroduceaopportunitiesoeredbywebservices,toengageinbusinessperformanceoptimizationtechniquecalledsource-biasedprob-collaborationsandservi