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1、OnMassiveSpatialDataRetrievalBasedonSpark(&)XiaolanXie,ZhuangXiong,XinHu,GuoqingZhou,andJinshengNiInstituteofInformationScienceandEngineering,GuilinUniversityofTechnology,Guilin,Chinaxie_xiao_lan@foxmail.comAbstract.Inordertosearchmoreefficientlyforrapidlygr
2、owingspatialdata,cloudquadtreeandR-treeisadoptedinspatialindexforthenon-relationaldatabasesofcloudHBase,bywhichdatacanberetrievedsuccessfully.BycomparisonofretrievalefficiencybetweencloudquadtreeandR-tree,thathowdifferentparametersactedistestedondataindexand
3、retrievalefficiencyandputforwardanrelativelymorereasonablesolution.Subsequently,weverifythevalidityofindexcalculationwhenthereisagiantone.Keywords:SpatialdataSparkQuadtreeRtreeIndexCloudcomputing1IntroductionWiththegrowingdevelopmentofcomputertechnology
4、,GeographicInformationSystem(GIS)havebeendevelopingrapidly,withtheexpansionandaugmentofitsapplicationanddemand.Forfasterprocessingandbetterspatialindex,cloudcom-putingisappliedtoretrievespatialdataprovetobealeadingsubjectofresolvingmassivespatialdataandgeog
5、raphicdistribution[1].R-tree(RectangleTree)isadatastructurewhichiswidelyusedinspatialretrieval.Thedepthofthetreeisrelativelysmallerthanthequad-tree,thereforethequeryefficiencyismuchhigher.R-treeasanindexcanachieveagreatfasterspeedinlesschangingspatialdata.Be
6、sides,thecontrollablesectionfortheusertocontrolismuchlessandthereforeitismoreconvenienttouse.However,theaddordeleteoperationonR-tree,whichhasalreadybeengenerated,willdecreasethequalityofR-treeindex,whileitwillconsumeahugeamountofcomputingiftheindexisrepeate
7、dlyrebuilt(especiallyatpresentalotofmutationofRtree,suchasR+tree,R*tree,areattheexpenseofinsertandmodificationtoimprovetheperformanceofquery).Asforthedemandforthistype,thequad-treeasrepresentedinsegmentationruleisabetterchoice.Inthispaper,weachieveaquad-tree
8、andR-treealgorithmforparallelquerybasedonSpark,tolaythefoundationforthemassivespatialdataqueryandprocessing[2].©SpringerInternationalPublishingSwitzerland2014Y.Chenetal.(Eds.):WAIM2014,LNCS8597,pp.200–
