简介：Graphsarewidelyusedformodelingcomplicateddatasuchassocialnetworks,chemicalcompounds,proteininteractionsandsemanticweb.Toeffiectivelyunderstandandutilizeanycollectionofgraphs,agraphdatabasethatefficientlysupportselementaryqueryingmechanismsiscruciallyrequired.Forexample,SubgraphandSupergraphqueriesareimportanttypesofgraphquerieswhichhavemanyapplicationsinpractice.Aprimarychallengeincomputingtheanswersofgraphqueriesisthatpair-wisecomparisonsofgraphsareusuallyhardproblems.Relationaldatabasemanagementsystems(RDBMSs)haverepeatedlybeenshowntobeabletoefficientlyhostdifferenttypesofdatasuchascomplexobjectsandXMLdata.RDBMSsderivemuchoftheirperformancefromsophisticatedoptimizercomponentswhichmakeuseofphysicalpropertiesthatarespecifictotherelationalmodelsuchassortedness,properjoinorderingandpowerfulindexingmechanisms.Inthisarticle,westudytheproblemofindexingandqueryinggraphdatabasesusingtherelationalinfrastructure.Wepresentapurelyrelationalframeworkforprocessinggraphqueries.Thisframeworkreliesonbuildingalayerofgraphfeaturesknowledgewhichcapturemetadataandsummaryfeaturesoftheunderlyinggraphdatabase.Wedescribedifferentqueryingmechanismswhichmakeuseofthelayerofgraphfeaturesknowledgetoachievescalableperformanceforprocessinggraphqueries.Finally,weconductanextensivesetofexperimentsonrealandsyntheticdatasetstodemonstratetheefficiencyandthescalabilityofourtechniques.

简介：Thisresearchtakestheviewthatthemodellingoftemporaldataisafundamentalsteptowardsthesolutionofcapturingsemanticsoftime.Theproblemsinherentinthemodellingoftimearenotuniquetodatabaseprocessing.Therepresentationoftemporalknowledgeandtemporalreasoningarisesinawiderangeofotherdisciplines.Inthispaperanaccountisgivenofatechniqueformodellingthesemanticsoftemporaldataanditsassociatednormalizationmethod.ItdiscussesthetechniquesofprocessingtemporaldatabyemployingaTimeSequence(TS)datamodel.Itshowsanumberofdifferentstrategieswhichareusedtoclassifydifferentdatapropertiesoftemporaldata,anditgoesontodevelopthemodeloftemporaldataandaddressesissuesoftemporaldataapplicationdesignbyintroducingtheconceptoftemporaldatanormalisation.

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简介：Thispaperpresentsanewfuzzymultiplecriteria(bothqualitativeandquantitative)decision-making(MCDM)methodbasedonfuzzyrelationaldegreeanalysis.Theconceptsoffuzzysettheoryareusedtoconstructaweightedsuitabilitydecisionmatrixtoevaluatetheweightedsuitabilityofdifferentalternativesversusvariouscriteria.Thepositiveidealsolutionandnegativeidealsolutionarethenobtainedbyusingamethodofrankingfuzzynumbers,andthefuzzyrelationaldegreesofdifferentalternativesversuspositiveidealsolutionandnegativeidealsolutionarecalculatedbyusingtheproposedarithmetic.Finally,therelativerelationaldegreesofvariousalternativesversuspositiveidealsolutionarerankedtodeterminethebestalternative.Anumericalexampleisprovidedtoillustratetheproposedmethodattheendofthispaper.

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简介：WepresentaninitialstudyoftheobjectfeaturesofOracle9i-thefirstofthemarket-leadingobject-relationaldatabasesystemsthatsupportsatrueobjectmodelontheserversideaswellasanODMG-styleC++languagebindingontheclientside.WediscusshowthesefeaturescanbeusedtoprovidepersistentobjectstorageintheHEPenvironment.

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简介：TheestablishmentoftheChinaPilotFreeTradeZone(FTZ)hassignificantlypromotedinternationaltrade,financialdevelopment,andeconomicgrowth.Buildinginternationalfinancialcenters(IFCs)satisfiesthedemandforFTZstofacilitatefinancialdevelopment,aswellaspromotingeconomicgrowth.Thus,successfullypredictingthenextIFCinChinaundertheFTZframeworkisanimportantissue.Inthisstudy,weappliedgreyrelationalanalysiscombinedwithentropymethodtopredictpotentialIFCsamongsevenFTZcities.Accordingtotheresults,ourinterestingfindingsinclude:1)the'totalstockturnover','totalvalueofimportsandexports',and'ForeignDirectInvestment(FDI)'arekeyindicatorsfordeterminingfutureIFCs;2)amongsevencities,ShenzhenandTianjinarehighlylikelytobecomethenextIFCs,whileShanghaiisalreadyanIFC.

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