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1、ContinuousMonitoringofNearestNeighborsonLandSurfaceSonghuaXingCyrusShahabiBeiPanComputerScienceDepartmentComputerScienceDepartmentComputerScienceDepartmentUniversityofSouthernCaliforniaUniversityofSouthernCaliforniaUniversityofSouthernCaliforniaLosAngeles,CA90089-0781LosAngeles,CA90089-0781LosAng
2、eles,CA90089-0781sxing@usc.edushahabi@usc.edubeipan@usc.eduABSTRACTterrainmodelsarenolongersparsenorbasedonsyntheticallygenerateddata,disk-basedstructuresareneededtostorethelargeAsgeo-realisticrenderingoflandsurfacesisbecomingcommonplaceingeographicalinformationsystems(GIS),gamesreal-worlddataset
3、s.Unfortunately,mostdatastructuresareandonlineEarthvisualizationplatforms,anewtypeofkNearestdesignedtoexpeditetherenderingofthisgeo-realisticdataratherthanitsqueryingandaccess.ThedatabasecommunityhasNeighbor(kNN)queries,“surface”kNearestNeighbor(skNN)queries,hasemergedandbeeninvestigatedrecently,
4、whichrecentlystartedpayingattentiontothisimportantbutuntappedextendsthetraditionalkNNqueriestoaconstrainedthirdareabystudyinganewtypeofkNearestNeighbor(kNN)queriesdimension(i.e.,landsurface).Allexistingtechniques,however,onsurfaces,calledsurfacekNN(skNN)queries[2,3,4].assumeastaticenvironment,lim
5、itingtheirutilityinemergingGivenaquerypoint,aconventionalkNNquery[14]returnstheapplications(e.g.,Location-basedServices)whereobjectsmove.numberofkobjectswiththeminimumdistancewithreferencetoInthispaper,forthefirsttime,weproposetwoexactmethodsthatthisquerypoint.InthecaseofskNN,thedistanceismeasure
6、dbycancontinuouslyanswerskNNqueriesinahighlydynamicthesurfacedistance.Notethatthisisdifferentfrom3DEuclideanenvironmentwhichallowsforarbitrarymovementsofdataspaceasthe3rddimensionisconstrainedbytheterrainmodel.objects.Thefirstmethod,inspiredbytheexistingtechniquesinTheskNNproblemisanaloguesinsome
7、sensetosupportingkNNmonitoringkNNinroadnetworks[7]maintainsananalogousqueryonroadnetworks,wherethedistanceisthenetworkcounterpartoftheDijkstraExpansionTreeonlandsurface,calleddistance.However,themaindifferenceisthatthe
