地理信息系统中英文对照外文翻译文献

1、中英文对照外文翻译（文档含英文原文和中文翻译）ASurveyonSpatio-TemporalDataWarehousingAbstractGeographicInformationSystems(GIS)havebeenextensivelyusedinvariousapplicationdomains,rangingfromeconomical,ecologicalanddemographicanalysis,tocityandrouteplanning.Nowadays,organizationsneedsophisticatedGIS-basedDecisionSupportSyste

2、m(DSS)toanalyzetheirdatawithrespecttogeographicinformation,representednotonlyasattributedata,butalsoinmaps.Thus,vendorsareincreasinglyintegratingtheirproducts,leadingtotheconceptofSOLAP(SpatialOLAP).Also,inthelastyears,andmotivatedbytheexplosivegrowthintheuseofPDAdevices,thefieldofmovingobjectdataha

3、sbeenreceivingattentionfromtheGIScommunity.However,notmuchhasbeendoneinprovidingmovingobjectdatabaseswithOLAPfunctionality.InthefirstpartofthispaperwesurveytheSOLAPliterature.WethenmovetoSpatio-TemporalOLAP,inparticularaddressingtheproblemoftrajectoryanalysis.Wefinallyprovideanin-depthcomparativeana

4、lysisbetweentwoproposalsintroducedinthecontextoftheGeoPKDDEUproject:theHermes-MDCsystem,andPiet,aproposalforSOLAPandmovingobjects,developedattheUniversityofBuenosAires,Argentina.Keywords:GIS,OLAP,DataWarehousing,MovingObjects,Trajectories,AggregationINTRODUCTIONGeographicInformationSystems(GIS)haveb

5、eenextensivelyusedinvariousapplicationdomains,rangingfromeconomical,ecologicalanddemographicanalysis,tocityandrouteplanning(Rigaux,Scholl,&Voisard,2001;Worboys,1995).SpatialinformationinaGISistypicallystoredindifferentso-calledthematiclayers(alsocalledthemes).Informationinthemescanbestoredindata

6、structuresaccordingtodifferentdatamodels,themostusualonesbeingtherastermodelandthevectormodel.Inathematiclayer,spatialdataisannotatedwithclassicalrelationalattributeinformation,of(ingeneral)numericorstringtype.Whilespatialdataisstoredindatastructuressuitableforthesekindsofdata,associatedattributesar

7、eusuallystoredinconventionalrelationaldatabases.SpatialdatainthedifferentthematiclayersofaGISsystemcanbemappedunivocallytoeachotherusingacommonframeofreference,likeacoordinatesystem.Theselayerscanbeoverlappedoroverlayedtoobtainanintegratedspatialview.Ontheotherhand,OLAP(OnLineAnalyticalProcessing)(K

8、imball,1996;Kimball&Ross,2002)comprisesasetoftoolsandalgorithmsthatallowefficientlyqueryingmultidimensionaldatabases,containinglargeamountsofdata,usuallycalledDataWarehouses.InOLAP,dataisorganizedasasetofdimensionsandfacttables.Inthemultidimensionalmodel,datacanbeperceivedasadatacube,whereeachce

9、llcontainsameasureorsetof(probablyaggregated)measuresofinterest.Aswediscusslater,OLAPdimensionsarefurtherorganizedinhierarchiesthatfavorthedataaggregationprocess(Cabibbo&Torlone,1997).Severaltechniquesandalgorithmshavebeendevelopedforqueryprocessing,mostoftheminvolvingsomekindofaggregateprecompu

10、tation(Harinarayan,Rajaraman,&Ullman,1996).TheneedforOLAPinGISDifferentdatamodelshavebeenproposedforrepresentingobjectsinaGIS.ESRI()firstintroducedtheCoveragedatamodeltobindgeometricobjectstonon-spatialattributesthatdescribethem.Later,theyextendedthismodelwithobject-orientedsupport,inawaythatbeh

11、aviorcanbedefinedforgeographicfeatures(Zeiler,1999).TheideaoftheCoveragedatamodelisalsosupportedbytheReferenceModelproposedbytheOpenGeospatialConsortium().Thus,inspiteofthemodelofchoice,thereisalwaystheunderlyingideaofbindinggeometricobjectstoobjectsorattributesstoredin(m

12、ostly)object-relationaldatabases(Stonebraker&Moore,1996).Inaddition,querytoolsincommercialGISallowuserstooverlapseveralthematiclayersinordertolocateobjectsofinterestwithinanarea,likeschoolsorfirestations.Forthis,theyuseindexingstructuresbasedonR-trees(Gutman,1984).GISquerysupportsometimesinclude

13、saggregationofgeographicmeasures,forexample,distancesorareas(e.g.,representingdifferentgeologicalzones).However,theseaggregationsarenottheonlyonesthatarerequired,aswediscussbelow.Nowadays,organizationsneedsophisticatedGIS-basedDecisionSupportSystem(DSS)toanalyzetheirdatawithrespecttogeographicinform

14、ation,representednotonlyasattributedata,butalsoinmaps,probablyindifferentthematiclayers.Inthissense,OLAPandGISvendorsareincreasinglyintegratingtheirproducts(see,forinstance,MicrostrategyandMapInfointegrationinForinstance,ifweareinterestedinthesalesofcertainproductsinstoresinagivenregion,wemayconside

15、rthesalesamountsinafacttableoverthethreedimensionsStore,TimeandProduct.Inordertoguaranteesummarizability(Lenz&Shoshani,1997),dimensionsareorganizedintoaggregationhierarchies.Forexample,storescanaggregateovercitieswhichinturncanaggregateintoregionsandcountries.Eachoftheseaggregationlevelscanalsoh

16、olddescriptiveattributeslikecitypopulation,theareaofaregion,etc.TofulfilltherequirementsofintegratedGIS-DSS,warehousedatamustbelinkedtogeographicdata.Forinstance,apolygonrepresentingaregionmustbeassociatedtotheregionidentifierinthewarehouse.Besides,systemintegrationincommercialGISisnotaneasytask.Int

17、hecurrentcommercialapplications,theGISandOLAPworldsareintegratedinanad-hocfashion,probablyinadifferentway(andusingdifferentdatamodels)eachtimeanimplementationisrequired,evenwhenadatawarehouseisavailablefornon-spatialdata.AnIntroductoryExample.Wepresentnowareal-worldexampleforillustratingsomeissuesin

18、thespatialwarehousingproblematic.WeselectedfourlayerswithgeographicandgeologicalfeaturesobtainedfromtheNationalAtlasWebsite().Theselayerscontainthefollowinginformation:states,cities,andriversinNorthAmerica,andvolcanoesinthenorthernhemisphere(publishedbytheGlobalVolcanismPr

19、ogram-GVP).Figure1showsadetailofthelayerscontainingcitiesandriversinNorthAmerica,displayedusingthegraphicinterfaceofthePietimplementationwediscusslaterinthepaper.Notethedensityofthepointsrepresentingcities(particularlyintheeasternregion).Riversarerepresentedaspolylines.Figure2showsaportionoftwooverl

20、ayedlayerscontainingstates(representedaspolygons)andvolcanoesinthenorthernhemisphere.Thereisalsonon-spatialinformationstoredinaconventionaldatawarehouse.Inthisdatawarehouse,dimensiontablescontaincustomer,storesandproductinformation,andafacttablecontainsstoressalesacrosstime.Also,numericalandtextuali

21、nformationonthegeographiccomponentsexist(e.g.,population,area),storedasusualasattributesoftheGISlayers.Inthescenarioabove,conventionalGISandorganizationaldatacanbeintegratedfordecisionsupportanalysis.Salesinformationcouldbeanalyzedinthelightofgeographicalfeatures,convenientlydisplayedinmaps.Thisanal

22、ysiscouldbenefitfromtheintegrationofbothworldsinasingleframework.Eventhoughthisintegrationcouldbepossiblewithexistingtechnologies,ad-hocsolutionsareexpensivebecause,besidesrequiringlotsofcomplexcoding,theyarehardlyportable.Tomakethingsmoredifficult,ad-hocsolutionsrequiredataexchangebetweenGISandOLAP

23、applicationstobeperformed.ThisimpliesthattheoutputofaGISquerymustbeprobablyexportedasmembersindimensionsofadatacube,andmergedforfurtheranalysis.Forexample,supposethatabusinessanalystisinterestedinstudyingthesalesofnauticalgoodsinstoreslocatedincitiescrossedbyrivers.ShecouldfirstquerytheGIS,toobtaint

24、hecitiesofinterest.SheprobablyhasstoredsalesinadatacubecontainingadimensionStoreorGeographywithcityasadimensionlevel.Shewouldneedto“manually”selectthecitiesofinterest(i.e.,theonesreturnedbytheGISquery)inthecube,tobeabletogoonwiththeanalysis(inthebestcase,anad-hoccustomizedmiddlewarecouldhelpher).Ofc

25、ourse,shemustrepeatthisforeachqueryinvolvinga(geographic)dimensioninthedatacube.Figure1TwooverlayedlayerscontainingcitiesandriversinNorthAmerica.Onthecontrary,GIS/Datawarehousingintegrationcanprovideamorenaturalsolution.Thesecondpartofthissurveyisdevotedtospatio-temporaldatawarehousingandOLAP.Moving

26、objectsdatabases(MOD)havebeenreceivingincreasingattentionfromthedatabasecommunityinrecentyears,mainlyduetothewidevarietyofapplicationsthattechnologyallowsnowadays.Trajectoriesofmovingobjectslikecarsorpedestrians,canbereconstructedbymeansofsamplesdescribingthelocationsoftheseobjectsatcertainpointsint

27、ime.AlthoughthereFigure2TwooverlayedlayerscontainingstatesinNorthAmericaandvolcanoesinthenorthernhemisphere.existmanyproposalsformodelingandqueryingmovingobjects,onlyasmallpartofthemaddresstheproblemofaggregationofmovingobjectsdatainaGIS(GeographicInformationSystems)scenario.Manyinterestingapplicati

28、onsarise,involvingmovingobjectsaggregation,mainlyregardingtrafficanalysis,truckfleetbehavioranalysis,commutertrafficinacity,passengertrafficinanairport,orshoppingbehaviorinamall.BuildingtrajectorydatawarehousesthatcanintegratewithaGISisanopenproblemthatisstartingtoattractdatabaseresearchers.Finally,

29、theMODsettingisappropriatefordataminingtasks,andwealsocommentonthisinthepaper.Inthispaper,wefirstprovideabriefbackgroundonGIS,datawarehousingandOLAP,andareviewofthestate-of-the-artinspatialOLAP.Afterthis,wemoveontostudyspatio-temporaldatawarehousing,OLAPandmining.WethenprovideadetailedanalysisoftheP

30、ietframework,aimedatintegratingGIS,OLAPandmovingobjectdata,andconcludewithacomparisonbetweenthisproposal,andtheHermesdatacartrridgeandtrajectorydatawarehousedevelopedinthecontextoftheGeoPKDDproject(InformationabouttheGoePKDDprojectcanbefoundathttp:/www.geopkdd.eu).ASHORTBACKGROUNDGISIngeneral,inform

31、ationinaGISapplicationisdividedoverseveralthematiclayers.Theinformationineachlayerconsistsofpurelyspatialdataontheonehand,thatiscombinedwithclassicalalpha-numericattributedataontheotherhand(usuallystoredinarelationaldatabase).Twomaindatamodelsareusedfortherepresentationofthespatialpartoftheinformati

32、onwithinonelayer,thevectormodelandtherastermodel.ThechoiceofmodeltypicallydependsonthedatasourcefromwhichtheinformationisimportedintotheGIS.TheVectorModel.ThevectormodelisusedthemostincurrentGIS(Kuper&Scholl,2000).Inthevectormodel,infinitesetsofpointsinspacearerepresentedasfinitegeometricstructu

33、res,orgeometries,like,forexample,points,polylinesandpolygons.Moreconcretely,vectordatawithinalayerconsistsinafinitenumberoftuplesoftheform(geometry,attributes)whereageometrycanbeapoint,apolylineorapolygon.Thereareseveralpossibledatastructurestoactuallystorethesegeometries(Worboys,1995).TheRasterMode

34、l.Intherastermodel,thespaceissampledintopixelsorcells,eachonehavinganassociatedattributeorsetofattributes.Usually,thesecellsformauniformgridintheplane.Foreachcellorpixel,thesamplevalueofsomefunctioniscomputedandassociatedtothecellasanattributevalue,e.g.,anumericvalueoracolor.Ingeneral,informationrep

35、resentedintherastermodelisorganizedintozones,wherethecellsofazonehavethesamevalueforsomeattribute(s).Therastermodelhasveryefficientindexingstructuresanditisverywell-suitedtomodelcontinuouschangebutitsdisadvantagesincludeitssizeandthecostofcomputingthezones.Spatialinformationinthedifferentthematiclay

36、ersinaGISisoftenjoinedoroverlayed.Queriesrequiringmapoverlayaremoredifficulttocomputeinthevectormodelthanintherastermodel.Ontheotherhand,thevectormodeloffersaconciserepresentationofthedata,independentontheresolution.Forauniformtreatmentofdifferentlayersgiveninthevectorortherastermodel,inthispaperwet

37、reattherastermodelasaspecialcaseofthevectormodel.Indeed,conceptually,eachcellis,andeachpixelcanberegardedas,asmallpolygon;also,theattributevalueassociatedtothecellorpixelcanberegardedasanattributeinthevectormodel.DataWarehousingandOLAPTheimportanceofdataanalysishasincreasedsignificantlyinrecentyears

38、asorganizationsinallsectorsarerequiredtoimprovetheirdecision-makingprocessesinordertomaintaintheircompetitiveadvantage.WesaidbeforethatOLAP(OnLineAnalyticalProcessing)(Kimball,1996;Kimball&Ross,2002)comprisesasetoftoolsandalgorithmsthatallowefficientlyqueryingdatabasesthatcontainlargeamountsofda

39、ta.Thesedatabases,usuallydesignedforread-onlyaccess(ingeneral,updatingisperformedoff-line),aredenoteddatawarehouses.Datawarehousesareexploitedindifferentways.OLAPisoneofthem.OLAPsystemsarebasedonamultidimensionalmodel,whichallowsabetterunderstandingofdataforanalysispurposesandprovidesbetterperforman

40、ceforcomplexanalyticalqueries.Themultidimensionalmodelallowsviewingdatainann-dimensionalspace,usuallycalledadatacube(Kimball&Ross,2002).Inthiscube,eachcellcontainsameasureorsetof(probablyaggregated)measuresofinterest.Thisfactualdatacanbeanalyzedalongdimensionsofinterest,usuallyorganizedinhierarc

41、hies(Cabibbo&Torlone,1997).ThreetypicalwaysofOLAPtoolsimplementationexist:MOLAP(standingformultidimensionalOLAP),wheredataisstoredinproprietarymultidimensionalstructures,ROLAP(relationalOLAP),wheredataisstoredin(object)relationaldatabases,andHOLAP(standingforhybridOLAP,whichprovidesbothsolutions

42、.InaROLAPenvironment,dataisorganizedasasetofdimensiontablesandfacttables,andweassumethisorganizationintheremainderofthepaper.ThereareanumberofOLAPoperationsthatallowexploitingthedimensionsandtheirhierarchies,thusprovidinganinteractivedataanalysisenvironment.WarehousedatabasesareoptimizedforOLAPopera

43、tionswhich,typically,implydataaggregationorde-aggregationalongadimension,calledroll-upanddrill-down,respectively.Otheroperationsinvolveselectingpartsofacube(sliceanddice)andreorientingthemultidimensionalviewofdata(pivoting).Inadditiontothebasicoperationsdescribedabove,OLAPtoolsprovideagreatvarietyof

44、mathematical,statistical,andfinancialoperatorsforcomputingratios,variances,ranks,etc.Itisanacceptedfactthatdatawarehouse(conceptual)designisstillanopenissueinthefield(Rizzi&Golfarelli,2000).MostofthedatamodelseitherprovideagraphicalrepresentationbasedontheEntity-Relationship(E/R)modelorUMLnotati

45、ons,ortheyjustprovidesomeformaldefinitionswithoutuser-orientedgraphicalsupport.Recently,MalinowskyandZimanyi(2006)proposetheMultiDimmodel.ThismodelisbasedontheE/Rmodelandprovidesanintuitivegraphicalnotation.Alsorecently,Vaisman(Vaisman,2006a,2006b)introducedamethodologyforrequirementelicitationinDec

46、isionSupportSystems,arguingthatmethodologiesusedforOLTPsystemsarenotappropriateforOLAPsystems.TemporalDataWarehousesTherelationaldatamodelasproposedbyCodd(1970),isnotwellsuitedforhandlingspatialand/ortemporaldata.Dataevolutionovertimemustbetreatedinthismodel,inthesamewayasordinarydata.Thisisnotenoug

47、hforapplicationsthatrequirepast,present,and/orfuturedatavaluestobedealtwithbythedatabase.Inreallifesuchapplicationsabound.Therefore,inthelastdecades,muchresearchhasbeendoneinthefieldoftemporaldatabases.Snodgrass(1995)describesthedesignoftheTSQL2TemporalQueryLanguage,anupwardcompatibleextensionofSQL-

48、92.Thebook,writtenasaresultofaDagstuhlseminarorganizedinJune1997byEtzion,Jajodia,andSripada(1998),containscomprehensivebibliography,glossariesforbothtemporaldatabaseandtimegranularityconcepts,andsummariesofworkaround1998.Thesameauthor(Snodgrass,1999),inotherwork,discussespracticalresearchissuesontem

49、poraldatabasedesignandimplementation.RegardingtemporaldatawarehousingandOLAP,MendelzonandVaisman(2000,2003)proposedamodel,denotedTOLAP,anddevelopedaprototypeandadatalog-likequerylanguage,basedona(temporal)starschema.Vaisman,Izquierdo,andKtenas(2006)alsopresentaWeb-basedimplementationofthismodel,alon

50、gwithaquerylanguage,calledTOLAP-QL.Eder,Koncilia,andMorzy(2002)alsoproposeadatamodelfortemporalOLAPsupportingstructuralchanges.Althoughtheseefforts,littleattentionhasbeendevotedtotheproblemofconceptualandlogicalmodelingfortemporaldatawarehouses.SPATIALDATAWAREHOUSINGANDOLAPSpatialdatabasesystemshave

51、beenstudiedforalongtime(Buchmann,Gunther,Smith,&Wang,1990;Paredaens,VanDenBussche,&Gucht,1994).Rigauxetal.(2001)surveyvarioustechniques,suchasspatialdatamodels,algorithms,andindexingmethods,developedtoaddressspecificfeaturesofspatialdatathatarenotadequatelyhandledbymainstreamDBMStechnology.A

52、lthoughsomeauthorshavepointedoutthebenefitsofcombiningGISandOLAP,notmuchworkhasbeendoneinthisfield.VegaL6pez,Snodgrass,andMoon(2005)presentacomprehensivesurveyonspatiotemporalaggregationthatincludesasectiononspatialaggregation.Also,Bedard,Rivest,andProulx(2007)presentareviewoftheeffortsforintegratin

53、gOLAPandGIS.Asweexplainlater,efficientdataaggregationiscrucialforasystemwithGIS-OLAPcapabilities.ConceptualModelingandSOLAPRivest,Bedard,andMarchand(2001)introducedtheconceptofSOLAP(standingforSpatialOLAP),aparadigmaimedatbeingabletoexplorespatialdatabydrillingonmaps,inawayanalogoustowhatisperformed

54、inOLAPwithtablesandcharts.TheydescribethedesirablefeaturesandoperatorsaSOLAPsystemshouldhave.Althoughtheydonotpresentaformalmodelforthis,SOLAPconceptsandoperatorshavebeenimplementedinacommercialtoolcalledJMAP,developedbytheCentreforResearchinGeomaticsandKHEOPS,seehttp:/www.kheops-Stefanovic,Han,andK

55、operski(2000)andBedard,Merret,andHan(2001),classifyspatialdimensionhierarchiesaccordingtotheirspatialreferencesin:(a)non-geometric;(b) geometrictonon-geometric;and(c)fullygeometric.Dimensionsoftype(a)canbetreatedasanydescriptivedimension(Rivestetal.,2001).Indimensionsoftypes(b)and(c) ,ageometryisass

56、ociatedtomembersofthehierarchies.MalinowskiandZimanyi(2004)extendthisclassificationtoconsiderthatevenintheabsenceofseveralrelatedspatiallevels,adimensioncanbeconsideredspatial.Here,adimensionlevelisspatialifitisrepresentedasaspatialdatatype(e.g.,point,region),allowingthemtolinkspatiallevelsthroughtopologicalrelationships(e.g.,contains,overlaps).Thus,aspatialdimensionisadimensionthatcontainsatleastonespatialhierarchy.Acriticalpointinspatialdimensionmodelingistheproblemofmultiple-dependencies,meaningthatanelementinonelevelcanberelatedtomorethanoneelementinalevelaboveitinthehier