evaluation). notonlythemanagementoflargedata-sets,butalsonewprocessingstrategiesforspatial

Transcription

1 SpatialDatabases:AccomplishmentsandResearchNeeds EE/CS4-192,200UnionSt.SE.,Minneapolis,MN55455 ComputerScienceDepartment,UniversityofMinnesota S.Shekhar,S.Chawla,S.Ravada A.Fetterer,X.Liu,C.T.Lu thegrowingdatamanagementandanalysisneedsofspatialapplicationssuchasgeographic datatypesandoperators,spatialquerylanguagesandprocessingstrategies,aswellas supportfornetworkandelddata,aswellasqueryprocessing(e.g.costmodels,bulk InformationSystems.Thisresearchhasproducedataxonomyofmodelsforspace,spatial spatialindexesandclusteringtechniques.however,moreresearchisneededtoimprove Spatialdatabaseshavebeenanactiveareaofresearchforovertwodecades,addressing Abstract InformationSystems Keywords:SpatialDatabases,Multi-Dimensional,Object-Relational,Databases,Geographic tonewerapplicationssuchasdatawarehousesandmultimediainformationsystems.the load).anotherimportantneedistoapplythespatialdatamanagementaccomplishments objectiveofthispaperistoidentifyrecentaccomplishmentsandtheresearchneedsinthe nearterm.

2 astronomy);partsoflivingorganisms(anatomyofthehumanbody);engineeringdesign(very Aspatialdatabase[11,15,35]managementsystemaimsattheeectiveandecientmanagementofdatarelatedtoaspacesuchasthephysicalworld(geography,urbanplanning, 1.1SpatialDatabases 1Introduction system,uidow,oranelectro-magneticeld). pharmaceuticaldrug);andconceptualinformationspace(amulti-dimensionaldecisionsupport largescaleintegratedcircuits,thedesignofanautomobileorthemolecularstructureofa InformationSystems(GIS)andComputerAidedDesign(CAD),aswellasfrompotential forimprovingfunctionalitycomesfromtheneedsofexistingapplicationssuchasgeographic inanumberofareas.theeldofspatialdatabasescanbedenedbyitsaccomplishments; currentresearchisaimedatimprovingitsfunctionalityanditsperformance.theimpetus decades.theresultsofthisresearch,e.g.spatialmulti-dimensionalindexes,arebeingused Theeldofspatialdatabaseresearchhasbeenanactiveareaofresearchforovertwo visualizationandterrainanalysis[20]. technologyatalllevelsoftactical,operationalandstrategicplanning,includingbattleed NASA'sEarthObservationSystem(EOS).TheacceptanceofGISasanimportanttoolin applicationssuchasmultimediainformationsystem(mmis),datawarehousing(dwh)and SpatialDataCartridgeandESRI'sSpatialDataEngine(SDE).Researchprototypeexamples blades(i.e.2d,3d,geodetic),oracle'suniversalserverwitheitherspatialdataoptionor governmentdecision-makingisalsodocumented[34]andmilitaryplannershaveembracedgis ofspatialdatabasemanagementsystemsincludespatialdatabladeswithpostgres[30],geo2, andparadise[9].thefunctionalitiesprovidedbythesesystemsincludeasetofspatialdata typessuchasapoint,line-segmentandpolygon,andasetofspatialoperationssuchasinside, CommercialexamplesofspatialdatabasemanagementincludeInformix'sspatialdata- databasemanagementsystem[6,32].theperformanceenhancementprovidedbythesesystems languagesuchassql,whichallowsspatialqueryingwhencombinedwithanobject-relational includesamulti-dimensionalspatialindexandalgorithmsforspatialaccessmethods,spatial intersection,anddistance.thespatialtypesandoperationsmaybemadeapartofaquery intheobject-relationalserverforperformancereasons. rangequeriesandspatialjoins.spatialindexingwithconcurrencycontrolmaybeimplemented evaluation). notonlythemanagementoflargedata-sets,butalsonewprocessingstrategiesforspatial set-operations,eldoperations(e.g.slope),andnetworkanalysis(e.g.shortest-path,route- networkspacesandcontinuouselds.theperformanceneedsofemergingapplicationsrequire Existingandemergingapplicationsrequirenewfunctionalitiesincludingthemodelingof 1

3 andhaveprioritizedresearchneeds.abroadsurveyofspatialdatabaserequirementsandan 1.2RelatedWorkandOurContributions Recentreports[11,15,35,1]havedescribedtheaccomplishmentsofspatialdatabaseresearch otheruser-levelissuessuchasgraphicalinputandoutputandquerylanguagesupport.spatial clusteringandindexingtechniques[23]suchasgrid-les,z-order,quad-tree,kd-trees,r-trees [12]andassociatedjoinstrategiesaredescribed.Finally,anarchitectureforspatialdatabases overviewofresearchresultsisprovidedby[35,11,1].basicmodelingrequirementsforspatial objectrelationships(topological,directional,metric,network).requirementsaregivenfor isgivenintermsoftheobject-relationalmodel. objectssuchaspoints,lines,andpolygonsaregivenintermsoftheirgeometry,topologyand controltechniquesforspatialindexingmethods,thedevelopmentofcostmodelsforquery matching. strategies,andthedevelopmentofnewspatialjoinalgorithmsbeyondnested-loopandtree relationaldatabaseswaslistedin[15].theprimaryresearchneedsidentiedwereconcurrency Manyoftheresearchneedsidentiedin[15]havesincebeenaddressed.Forexample, Researchneededtoimprovetheperformanceofspatialdatabasesinthecontextofobject- basedonpublicationsinjournalsandconferenceproceedingsandrecentcommercialtrends. weidentifytherecentaccomplishmentsinspatialdatabasesaswellascurrentresearchneeds, concurrencycontroltechniquesforr-treeshavebeenstudiedinthecontextofr-link[16]trees. Also,newspatialjoinstrategiesusingspacepartitioning[22]havebeenexplored.Inthispaper, databases,whichprovideextensibilitytomanycomponentsoftraditionaldatabasestosupport newapplicationdomains.theseandotherimportantissuesincludingarchitecturaloptions, wefocusthediscussionofspatialdatabasesinthecontextoftheobject-relational[6,32,31] mentsystem(dbms)involved:relational,object-orientedorobject-relational.inthispaper, 1.3ScopeandOutline RasterDBMSandNetworkspacesarecoveredindetailinourforthcomingbook[24].Spatial Theroleofthespatialdatabasecomponentisdependentonthetypeofdatabasemanage- haveinuencedtheirdesignagreatdeal.object-relationaldatabasesallowtheinclusionofspatialdata-types,spatialoperations,andmulti-dimensionalindexingsystems.thisthree-layer databaseshavebeenoneofthemostcommonapplicationsofobject-relationaldatabasesand architecturalframeworkisshowninfigure1,anditconsistsofanobject-relationaldatabase managementsystem,aspatialdatabase,andaspatialapplicationsuchasagisormmis.the interfacebetweentheapplicationandthespatialdatasystemmapsapplication-specicconstructstothespatialdatabase.thespatialdatabaseassociatestheapplicationrequirements queryprocessing,whichinturnsupportsthecoredatabaserequirementsforachievingaccept- tothefunctionalityprovidedbythedbms.theinterfacetothedbmssupportsspecialized 2

4 ableperformance. Interface to Spatial Appllication Line Spatial Application Spatial Database Core Interface to DBMS DBMS Abstract Data Types Point Polygon Space Taxonomy GIS Index Structures Data Model Spatial Data Types and Operations X Spatial Query Interpretation, Discretization, Languages Spatial Join Scale/Resolution Consistency Algorithms for spatial operations with cost MMIS models Object Networks Relational Cost Functions Database Selectivity Evaluation Servers arelikelytoimpactthedatasharingandanalysisneedsofspatialdatabases.scalinguptolarge Spatial index access methods Data Volume (with concurrency le-systems,device-driversfortertiarystorage,computernetworks,andvisualizationsoftware Figure1:3-layerarchitecture control) Bulk Loading Concurrency Control Recovery/Backup thoseissues. andalgorithmsrelatedtographicsandcomputationalgeometry.thispaperdoesnotexplore datasetsrequiresnewresearchinmanyareasbeyondspatialdatabases,includingresearchon Emergingtrendssuchasworld-wide-webinterfaces,multimediadata,andimageprocessing CAD Visualization spatialdatabases.section3statestheresearchneedsforspatialdatabases.section4highlights Theremainderofthepaperisorganizedasfollows:Section2describestherecentadvancesin Views Derived Data ourconclusionsandmotivatesexplorationofapplicationswhoseneedsarenotcurrentlymet byspatialdatabases. 2Accomplishments Researchintospatialdatabaseshasmainlyfocusedondevelopingaspacetaxonomy,spatial datamodels,spatialquerylanguagesandprocessingstrategies,andspatialaccessmethods. Thissectionlistsrecentimportantaccomplishments,notonlyforthecurrentapplicationsof 3

5 Spaceisaframeworktoformalizespecicrelationshipsamongasetofobjects.Depending 2.1SpaceTaxonomy spatialdatabases,butalsofortheemergingdatabaseproblemsthathavespatialdimensions. Relationalandobject-relationaldatabasesusethismodelofspace. relationshipssuchasset-equality,subset,union,cardinality,relation,function,andconvexity. usesthebasicnotionofelements,element-equality,setsandmembershiptoformalizetheset space,euclideanspace,metricspaceandnetworkspacecanbeused[35].set-basedspace ontherelationshipsofinterest,dierentmodelsofspacesuchasset-basedspace,topological tendedobjectrelationshipssuchasboundary,interior,open,closed,within,connected,and malizesrelationshipssuchaseuler'sformula(#faces+#vertices-#edges=1forplanar overlaps,whichareinvariantunderelasticdeformation.combinatorialtopologicalspacefor- conguration).networkspaceisaformoftopologicalspaceinwhichtheconnectivityproperty amongnodesformalizesgraphpropertiessuchasconnectivity,iso-morphism,shortest-path, Topologicalspaceusesthebasicnotionofaneighborhoodandpointstoformalizetheex- andplanarity. propertiesandrelationshipstopropertiesoftuplesofrealnumbers.metricspacesformalize Manymultidimensionalapplicationsuseeuclideancoordinatizedspacewithmetricssuchas thedistancerelationshipsusingpositivesymmetricfunctionsthatobeythetriangleinequality. distance. Euclideancoordinatizedspaceusesthenotionofacoordinatesystemtotransformspatial Theeld-basedmodeltreatsspatialinformationsuchasaltitude,rainfallandtemperatureas 2.2SpatialDataModelandQueryLanguage Aspatialdatamodel[25,35]isatypeofdata-abstractionthathidesthedetailsofdatastorage.Therearetwocommonmodelsofspatialinformation:eld-basedandobject-based. acollectionofspatialfunctionstransformingaspace-partitiontoanattributedomain.the object-basedmodeltreatstheinformationspaceasifitispopulatedbydiscrete,identiable, spatially-referencedentities.theoperationsonspatialobjectsincludedistanceandboundary. eldsmaybecontinuous,dierentiable,discrete,andisotropicoranisotropic,withpositiveor Theoperationsoneldsincludelocal,focal,andzonaloperations,asshowninTable2.The negativeauto-correlation.certaineldoperations(slopeorinterpolation)assumecertaineld properties(dierentiableorpositiveauto-correlation). embeddinginaquerylanguage.consensusisslowlyemergingviastandardizationeorts,and consistsofasetofspatialdatatypesandtheoperationsonthosetypes.muchworkhas beendoneoverthelastdecadeonthedesignofspatialabstractdatatypes(adts)andtheir Animplementationofaspatialdatamodelinthecontextofobject-relationaldatabases 4

6 ADTsinSQL.Figure3,whichillustratesthisspatialdata-typehierarchyconsistsofPoint, recentlytheogisconsortium[21]hasproposedaspecicationforincorporating2dgeospatial beintutivelyposedinsql.forexample,thequeryfindalllakeswhichhaveanareagreater ubiquitous9-intersectionmodel[10].usingtheogisspecication,commonspatialqueriescan CurveandPolygonclassesandaparallelclassofGeometryCollection.Thebasicoperations than5sq.km.andarewithin20km.fromthecampgroundscanbeposedasshowninfigure operativeonalldatatypesareshownintable1.thetopologicaloperationsarebasedonthe intable3.theogisspecicationisconnedtotopologicalandmetricoperationsonvector datatypes.otherinterestingclassesofoperationsarenetwork,direction,dynamicandthe eldoperationsoffocal,localandzonal(seetable2).whilestandardsforeldbasedraster datatypesarestillemerging,mapalgebra[33],specicallydesignedforcartographicmodeling OtherexampleGISquerieswhichcanbeimplementedusingOGISoperationsareprovided Figure2:(a)SQLquerywithspatialoperators.(b)Correspondingquerytree. Theecientprocessingofspatialqueriesrequiresbothecientrepresentationandecient algorithms.commonrepresentationsofspatialdatainanobjectmodelincludespaghetti,the andrasql,basedonimagealgebra[3],forgeneralmulti-dimensionaldiscreteobjects(satellite images,x-rays,etc.),areimportantmilestones. 2.3SpatialQueryProcessing node-arc-area(naa)model,thedoubly-connected-edge-list(dcel),andboundaryrepresentation[17],someofwhichareshowninfigure4usingentity-relationshipdiagrams.thenaa 5 2(a). SELECT Area(L.Geometry) FROM WHERE L.name, Fa.name Lake L, Facilities Fa Area(L.Geometry) > 5 AND Fa.type = campground AND Distance(Fa.Geometry, L.Geometry) < 20 (a) Lake L π σ σ (b) L.name, Fa.name > 5 Fa..type = campground Distance(Fa.Geometry, L.Geometry) < 20 Facilities Fa

7 BasicFunctionsSpatialReference()ReturnstheReferenceSystemofthegeometry Topological/Equal Set Operators Disjoint Intersect Envelope() Export() IsEmpty() IsSimple() Boundary() Theminimumboundingrectangleofthegeometry Touch Testsifthegeometriesarespatiallyequal Testsifthegeometriesaredisjoint. Convertthegeometryintoadierentrepresentation. Cross Testsifthegeometriesintersect Testsifthegeometryisaemptysetornot. Within ReturnsTrueifthegeometryissimple(noself-intersection) Testsifthegeometriestoucheachother. Testsifthegeometriescrosseachother. Returnstheboundaryofthegeometry Spatial Analysis Distance Buer Contains Overlap Returnsageometrythatrepresentsall Testsifthegivengeometrycontainsanothergivengeometry Testsifthegeometryoverlapsanothergeometry. Returnstheshortestdistancebetweentwogeometries Testsifthegivengeomtryiswithinanothergivengeometry pointswhosedistancefromthegiven Table1:RepresentativefunctionsspeciedbyOGIS[21] ConvexHull Intersection Union Dierence SymDi Returnstheconvexhullofthegeometry Returnstheintersectionoftwogeometries Returnstheunionoftwogeometries Returnsthedierenceoftwogeometries Returnsthesymmetricdierenceoftwogeometries islessthanorequaltothespecieddistance modeldierentiatesbetweenthetopologicalconcepts(node,arc,areas)andtheembedding spatialobjectisrstusedtolteroutmanyirrelevantobjectsquickly.exactgeometryisthen hexagonalgrid),aswellastriangularirregularnetworks(tin). space(points,lines,areas).thespaghetti-ringanddcelfocusonthetopologicalconcepts. usedfortheremainingspatialobjectstocompletetheprocessing.strategiesforrange-queries Approximategeometrysuchastheminimalorthogonalboundingrectangleofanextended Therepresentationoftheelddatamodelincludesaregulartessellation(triangular,square, includeascanandindex-searchinconjunctionwiththeplane-sweepalgorithm[5].strategies forthespatial-joinincludethenestedloop,treematching[5]whenindicesarepresentonall Thespatialqueries[7]showninTable3areoftenprocessedusinglterandrenetechniques. similarcharacteristics. matchingoriginatedinspatial-databases,andcanpotentiallybeappliedinotherdomainswith participatingrelations,andspacepartitioning[22]intheabsenceofindices.tospeedup objectindicesareusedinextendedltering.strategiessuchasobjectapproximationandtree computationforlargespatialobjects(itiscommonforpolygonstohave1000ormoreedges), 6

8 Geometry SpatialReferenceSystem Figure3:SpatialDataTypeHierarchy[21] Point Curve Surface GeometryCollection LineString Polygon MultiSurface MultiCurve MultiPoint niquesismoredicultcomparedtothedesignoftraditionalclusteringbecausethereisno clusteringmethodsaswellasspatialhashingmethods.thedesignofspatialclusteringtech- Thephysicaldesignofaspatialdatabaseoptimizestheinstructionstostoragedevicesforperformingcommonoperationsonspatialdatales.Filedesignsforsecondarystorageinclude 2.4SpatialFileOrganizationandIndices Line LinearRing MultiPolygon MultiLineString naturalorderinmultidimensionalspacewherespatialdataresides.thisisonlycomplicated 1+ bythefactthatthestoragediskisalogicalone-dimensionaldevice.thus,whatisneeded isamappingfromahigherdimensionalspacetoaone-dimensionalspacewhichisdistancepreserving:sothatelementsthatarecloseinspacearemappedontonearbypointsontheline, andone-one:notwopointsinthespacearemappedontothesamepointontheline[2].several operations.thephysicalorganizationoflescanbesupplementedwithindices,whichare [27]usethemin-cutpartitioningofagraphrepresentationtoecientlysupportgraphtraversal inametricspace.topologicalclusteringmethodslikeconnectivity-clusteredaccessmethods mappings,noneofthemideal,havebeenproposedtoaccomplishthis.themostprominent data-structurestoimprovetheperformanceofsearchoperations. onesincluderow-order,z-orderandthehilbert-curve(figure5). Classicalone-dimensionalindicessuchastheB+treecanbeusedforspatialdatabylinearizingamulti-dimensionalspaceusingaspace-llingcurvesuchastheZ-order(seeFigure 5).Alargenumberofspatialindices[23]havebeenexploredformulti-dimensionaleuclidean space.representativeindicesforpointobjectsincludegridles,multi-dimensionalgridles Metricclusteringtechniquesusethenotionofdistancetogroupnearestneighborstogether 7

9 Is Previous Is Next Figure4:EntityRelationshipDiagramsforCommonRepresentationsofSpatialData Sequence No. Left Bounded Directed Arc Begin Area Sequence Points Area Node Right Spaghetti Data Model Bounded Ends [18],Point-Quad-Trees,andKd-trees.Representativeindicesforextendedobjectsincludethe Double-Connected-Edge List Model R-treefamily,theFieldtree,Celltree,BSPtree,andBalancedandNestedgridles. Sequence No. Sequence No. Polyline Polygon Sequence Sequence Embeds Embeds Embeds Left Directed Begins Area Bounded Arc Node Right Ends Bounded guaranteegoodspaceutilizationandheight-balance,theparentmbrsareallowedtooverlap. allentriescontainedinthechild-pointer.leafnodescontainthembrsofthedataobjects.to Nonleafnodesarecomposedofentriesoftheform(R;child?pointer),whereRistheMBRof structure[12].ther-treeisaheightbalancednaturalextensionoftheb+treeforhigherdimensions.objectsarerepresentedinther-treebytheirminimumboundingrectangles(mbrs). OneoftherstaccessmethodscreatedtohandleextendedobjectswasGuttman'sR-tree Node-Arc-Area Model Figure6(a)illustratesthespatialobjectsorganizedinanR-tree,whileFigure6(b)showsthe Figure5:Space-FillingCurvestoLinearizeaMultidimensionalSpace 8 Row Peano-Hilbert Morton / Z-order

10 DatamodelOperatorGroupOperation VectorObjectTopological Set-Orientedequals,isamemberof,isempty,isasubsetof,isdisjoint Metric Direction Network boundary,interior,closure,meets,overlaps,isinside, distance,bearing/angle,length,area,perimeter. successors,ancestors,connected,shortest-path east,north,left,above,between. covers,connected,components,extremes,iswithin from,intersection,union,dierence,cardinality RastereldFocal Dynamic Local Zonal slope,aspect,weightedaverageofneighborhood translate,rotate,scale,shear,split,merge Point-wisesums,dierences,maximums,means,etc lestructurewherethenodescorrespondtodiskpages.manyvariationsofther-treestructure existwhosemainemphasisisondiscoveringnewstrategiestomaintainthebalanceofthetree, Table2:ASampleofSpatialOperations sumormeanormaximumofeldvaluesineachzone avariantofther-treewithadditionalsiblingpointersthatallowthetrackingofmodications. incaseofasplit,andtominimizetheoverlapofthembrsinordertoimprovethesearchtime. Concurrencyisprovidedduringoperationssuchassearch,insert,anddelete.TheR-linktree isalsorecoverableinawrite-aheadloggingenvironment. Concurrencycontrolforspatialaccessmethods[16]isprovidedbytheR-linktree,whichis Grouping Isolate Classify Scale Rank Evaluate SelectalllandownedbySteveSteiner Recodealllandwithsiltysoiltosilt-loamsoil IftheroadisanInterstate,assignitcode1;iftheroad Ifthepopulationdensityislessthan100people/sq.mi.,landisacceptable Changeallmeasurementtothemetricsystem Iftheroadcodeis1,thenassignitInterstate;iftheroadcodeis2, isastateorushighway,assignitcode2;otherwiseassignitcode3 thenassignitmainartery;iftheroadcodeis3,assignitlocalroad SingleTableQueries Rescale AttributeJoinJointheForestlayerwiththelayercontainingforest-covercodes Zonal RegistrationAligntwolayerstoacommongridreference SpatialJoinOverlaytheland-useandvegetationlayerstoproduceanewlayer Produceanewmapshowingstatepopulationsgivencountypopulation Applyafunctiontothepopulationdensity Table3:TypicalSpatialQueriesfromGIS Multi-TableQueries 9

11 A A B C e d C B i d e f g h i j g f setsencounteredinanyapplicationtodate.thishaspromptednewresearchindatabase-le disk 2.5OtherAccomplishments SpatialapplicationslikeNASA'sEarthObservationSystem(EOS)havesomeofthelargestdata Figure6:(a)Spatialobjects(bold)arrangedinR-treehierarchy,(b)R-treelestructureon designforstorageontertiarystoragedevicessuchasjuke-boxes.representativeresultsinclude thosefromthesequoia2000project[30].high-performancespatialapplicationssuchasight j simulatorswithgeographicaccuracyhavetriggeredthedevelopmentofnewparallelformalizationsfortherangequeryandthespatialjoinquery,includingdeclusteringmethodsand dynamic-loadbalancingtechniquesformulti-dimensionalspatialdata[28,19].otherinterestingdevelopmentsincludehierarchicalalgorithmsforshortestpathcomputation[14]andview materialization[26]. 3ResearchNeeds Spatialdatabasesarebeingusedforanincreasingnumberofnewapplications,suchasIntelligent TransportationSystems,NASA'sEarthObservationSystem,MultimediaInformationSystems (MMIS)andDataWarehouses.Thissectionlistsrepresentativeresearchneeds. h 10

12 similartotheonesintroducedbynite-precisionarithmeticonnumbers.therearepreliminary 3.1SpaceTaxonomy results[11]ontheuseofdiscretebasisandboundingerrorswithpeg-boardsemantics.another Manyspatialapplicationsmanipulatecontinuousspacesofdierentscalesandwithdierent levelsofdiscretization.asequenceofoperationsondiscretizeddatacanleadtogrowingerrors Spatialdatamodelshavebeendevelopedfortopological,metricandcoordinatizedeuclidean relatedproblemconcernsinterpolationtoestimatethecontinuouseldfromadiscretization. space.theogisspecicationalludedtoinsection2.2isconnedtotopologicaloperators[8] frameworktoformalizethediscretizationprocess,itsassociatederrors,andoninterpolation. 3.2SpatialDataModel Negativespatialauto-correlationmakesinterpolationerror-prone.Furtherworkisneededona andmoreworkisneededtoincorporaterelationshipswhichinvolvedirectional[29]andmetric properties(seetable2forexamples).inadditiontherehasbeenverylittleworktowards developingdatamodels,datatypes(e.g.node,edge,path),andakernelsetofoperations(e.g. transportationandutilitymanagement(telephone,gas,electric). get-successors,shortestpath)fornetworkspace,despitetheircriticalroleinapplicationslike land-coverclassication;theeldsinvolvedincludetemperature,texture,andwatercontent, andareobtainedthroughimagingindierentbandssuchasotherinfrared,visiblebands,or querylanguage.operationsoneldswillbeneededtohelpderivenewinformationsuchas microwave. Similarly,thereisaneedfordevelopingtheelddatamodel[33]towardsaeld-based networks.costmodelsareusedtorankandselectthepromisingprocessingstrategies,givena Manyopenresearchareasexistatthelogicallevelofqueryprocessing,includingquery-cost 3.3SpatialQueryProcessing spatialqueryandaspatialdataset.traditionalcostmodelsmaynotbeaccurateinestimating modelingandstrategiesfornearestneighbor,bulkloadingaswellasqueriesrelatedtoeldsand gapbetweenrelationaloperatorsandspatialoperation.costmodelsareneededtoestimate thecostofstrategiesforspatialoperations,duetothedistancemetricaswellasthesemantic theselectivityofspatialsearchandjoinoperationstowardscomparisonofexecution-costsof alternativeprocessingstrategiesforspatialoperationsduringqueryoptimization.preliminary workinthecontextofther-tree,tree-matchingjoin,andfractal-modelsispromising[4,36], butmoreworkisneeded. stepinqueryoptimizationmaynotbealwaysapplicableinthecontextofspatialdatabases. Similarly,commonstrategiesemployedintraditionaldatabasesforthelogicaltransformation 11

13 π L.name, Fa.name π σ L.name, Fa.name Area(L.Geometry) > 5 Distance(Fa.Geometry, L.Geometry) < 20 Forexampleconsiderthequery(seeFigure2(a))rstintroducedinSection2.Letusassume Distance(Fa.Geometry, L.Geometry) < 20 thatthearea()functionisnotpre-computedandthatitsvalueiscomputedafresheverytime Figure7:(a):Area()beforeDistance().(b):Distance()beforeArea(). Area(L.Geometry) > 5 σ σ Fa.type = campground Lake L Facilities Fa Lake L σ Fa.type = campground Facilities Fa therelativecostpertupleofarea()anddistance()isanimportantfactorindecidingthe becomputedbeforethejoinpredicatefunction,distance()(figure7(a)),theunderlying assumptionbeingthatthecomputationalcostofexecutingtheselectandjoinpredicateare equivalentandnegligiblecomparedtothei/ocostoftheoperations.inthespatialsituation itisinvoked.aquerytreegeneratedforthequeryisshowninfigure2(b). Intheclassicalsituation,therule\selectbeforejoin"woulddictatethattheArea()function orderoftheoperations[13].dependingupontheimplementationofthesetwofunctionsthe optimalstrategymaybetoprocessthejoinbeforetheselectoperation(seefigure7(b)). needfurtherstudy. suchastheshortestpathtoasetofdestinations.bulkloadingstrategiesforspatialdataalso otherthanoverlapandqueriesoneldssuchasslopeanalysisaswellasqueriesonnetworks otherfrequentqueriessuchasthoseintable4.theseincludequeriesonobjectsusingpredicates andspatialjoinqueries.however,thereisaneedtodevelopandevaluatestrategiesformany Manyprocessingstrategiesusingtheoverlappredicatehavebeendevelopedforrangequeries Manyleorganizationsandindiceswithdistancemetricshavebeendevelopedforcoordinatized 3.4SpatialFileOrganizationandIndices:PhysicalLevel euclideanspace.however,littleworkhasbeendoneonleclusteringandonindicesfornetwork spacessuchasroad-mapsandtelephonenetworks.furtherworkisneeded,bothtocharacterize 12

14 Buer Voronoize NeighborhoodDetermineslopebasedonelevation Network Allocation TransformationTriangulatealayerbasedonelevation BulkLoad Findtheareas500ft.frompowerlines Classifyhouseholdsastowhichsupermarkettheyareclosestto Findtheshortestpathfromthewarehousetoalldeliverystops Whereisthebestplacetobuildanewrestaurant theaccesspatternsofthegraphalgorithmsthatunderlienetworkoperationsandtodesign Raster$VectorConvertbetweenrasterandvectorrepresentations Table4:DicultSpatialQueriesfromGIS Loadaspatialdataleintothedatabase ThedatavolumeofemergingspatialapplicationssuchasNASA'sEOSisamongthehighest accessmethods. R-tree.Newapproachesforconcurrency-controltechniquesareneededforotherspatialindices. ofanydatabaseapplication.sequoia2000[30]providesanapproachtowardstertiarystorage investigated. lesandindices.otherapproachesformanagingdatabasesontertiarystorageneedtobe TheR-linktree[16]isamongthefewapproachesavailableforconcurrencycontrolonthe 3.5Other Otherresearchneedsincludebenchmarking,work-owmodeling,andthevisualpresentationof results.thesequoia2000[30]benchmarkcharacterizesthedataandqueriesinearthscience toproducenew,derivedlayers.typically,thelayersarecombinedinatree-basedmanner, system[9].similarbenchmarksareneededtocharacterizethespatialdatamanagementneeds applications.theperformanceofloadingdata,rasterqueries,spatialselection,spatialjoins, andrecursionisaddressedin11benchmarkqueries.afewmoreareprovidedintheparadise isproduced.informationaboutdependenceamonglayersisusefulforchangepropagationif startingwithalargenumberofsourcelayersandproducingnewlayersuntilanalresultlayer ofotherapplicationssuchasgis,dwh,andtransportation. thesourcelayersaremodied. Thework-owinsomespatialapplicationssuchasGISisbasedonmanipulatinglayers traditionaldatabases.forexample,transactionsintraditionalsystemstendtobeshort(on ofhoursforeditingandbrowsing.similarly,recoveryandbackupissuesmayalsochange,as thespatialobjectstendbelarge(afewmegabytes)whencomparedtotheircounterpartsin traditionalsystems.thereisaneedtocharacterizetheworkowofspatialapplications. theorderofseconds).however,inspatialdatabases,thesetransactionscanlastuptoacouple Spatialdatabasesmayrequireadierenttypeofconcurrencysupportthanisneededby Manyspatialapplicationspresentresultsvisually,intheformofmapswhichconsistof 13

15 graphicimages,3ddisplays,andanimations.theyalsoallowuserstoquerythevisualrepresentationbypointingtothevisualrepresentationusingdeviceslikeamouseorapen.further ondatabaseperformance. 4SummaryandDiscussion workisneededtoexploretheimpactofqueryingbypointingandvisualpresentationofresults Inthissurveywehavepresentedthemajorresearchaccomplishmentsandtechniqueswhich haveemergedfromtheareaofsdbms.theseincludeobject-baseddatamodeling,spatial datatypes,lterandrenetechniquesforqueryprocessingandspatialindexing.wehave alsoidentiedareaswheremoreresearchisneeded.someoftheseareasarespatialgraphs, eldbasedmodeling,costmodelingandconcurrencycontrol,queryprocessingtechniquesand discretizationandpropogationerror. inthecontextofmulti-mediainformationsystemswithtext,audioandvideodataoverthe dimensionalapplicationssuchasmultimediainformationsystemswillusethesemethodsto numberofapplicationssuchasgis,cad,andeos.webelievethatotheremergingmulti- solveproblemssuchassearchingandindexingspatialcontent.weillustratethepossibilities world-wide-web. Manyofthespatialtechniqueshighlightedinthissurveyarebeingusedinanincreasing insideofcanbeappliedtotexttolocatesentencesthatcontaintheword\multimedia".also, audioisoftenbrokenintochannelswitheachchannelcontaininginputfromadierentsource; bemanipulatedsimilarly.aspatialjoincoulddetermineallofthelocationswheretheinput forinstance,trumpet,guitar,andvoice.thesechannelsareanalogoustolayersingisandcan thathavebecomepopularingeographicinformationsystems.forexample,thespatialoperator Multimediadatahasaspatialcontentwhichcanbequeriedusingthesamespatialoperators frombothpianoandvoiceisoveracertaindecibelthreshold. spatialdatabases.considerthemovietoystory:eachframecontainsspatialcontentwith treeswhenheisying,andframesinthemoviecouldbequeriedbasedonthoserelationships. Forexample,ifyoucannotrememberwheninthemovieanimportanteventoccurred,butyou objectsinteractingintopologicalrelationships.forinstance,buzzlightyearcouldbeabovethe canrememberthatbuzzlightyearwasinfrontofatree,youwouldbeabletoquerythemovie Avideodatabasesuchasamovieservercantakeadvantageoftechniquesdevelopedfor usingthatrelationshiptodeterminewheninthemoviethateventtookplace.suchqueries exploitthetopologicalrelationshipsinherentinalltangibleobjects. 14

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