Efficient Relational Techniques for Processing Graph Queries-中国期刊网

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

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Efficient Relational Techniques for Processing Graph Queries

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Efficient Relational Techniques for Processing Graph Queries

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