toqbianditssuitabilityasaqueryprocessingfacilityformobileusersisformally discussedinsection5.section6describesandevaluatesthreealgorithmsforgen-

Transcription

1 SupportingMobileDatabaseAccessthrough QuerybyIcons ckluweracademicpublishers,boston.manufacturedinthenetherlands.,,1{23() ANTONIOMASSARI Dipart.diInformaticaeSistemistica,UniversityofRome"LaSapienza",00198-Roma,Italy SUSANWEISSMAN,PANOSK.CHRYSANTHIS DepartmentofComputerScience,UniversityofPittsburgh,Pittsburgh,PA15260,U.S.A. sincetheycanbemanipulatedwithouttyping.thefacilityrequiresnospecialknowledgeofthe queryprocessingfacilitythatsupportstheexplorationandqueryofdatabasesfromamobile computerbasedonthemanipulationoficons.iconsareparticularlysuitableformobilecomputing Abstract.Inthispaper,wepresentboththetheoreticalframeworkandaprototypeofa locationorthecontentoftheremotedatabasenorunderstandingofthedetailsofthedatabase schema.itsiconicquerylanguageinvolvesnopathspecicationincomposingaquery.the Keywords:MobileQueryProcessing,IconicQueryLanguage,MobileComputing,Semantic communication. queryfacilityprovidesmetaquerytoolsthatassistintheformulationofcompletequeriesinan Distance,SemanticandObjectModel,PathComputations eectivelycopewiththeinherentlimitationsinmemoryandbatterylifeonthemobilecomputer, disconnectionsandrestrictedcommunicationbandwidth,andthehighmonetarycostofwireless theremotedatabase.bynotrequiringconstantaccessandcachingoftheactualdata,itisableto incrementalmanneronthemobilecomputerandwithoutinvolvingaccesstotheactualdatain fectedthewaythatwecomputebut,moresignicantly,theyarechangingtheway 1.Introduction Advancesincomputerandwirelesscommunicationtechnologieshavenotonlyaf- weliveanddobusiness.forexample,mobileusers,sayahospitalparamedicunit capabilityofquicklylocatingandcontactingmedicalpersonnelnearesttotheaccidentsite.thatis,mobileusersbymeansofhand-heldcomputersequippedwitha wirelessinterface,shouldbeable(1)tocomposeadatabasequerywithminimum arrivingatanaccidentsite,needstoeasilyaccessthemedicalhistoryofthevic- ornoknowledgeofhowthedatabaseisstructuredandwhereitislocated,and(2) tocomposethequerywithafewkeyselectionsandminimumtyping[3]. tims,regardlessofthelocationandformoftheinformation.theyalsoneedthe calledquerybyicons(qbi),considerstheinherentlimitationsinmemoryand facilitysuitableformobiledatabaseapplications.thequeryprocessingfacility, batterypoweronthemobilecomputer,disconnectionsofthemobilecomputerfor substantialperiods,restrictedcommunicationbandwidth,andhighmonetarycost ofwirelesscommunication[4,15]. Motivatedbytheserequirements,wepresentinthispaperaqueryprocessing

2 Aniconicvisuallanguageinterface,whichallowsausertocomposeadatabase 2ThesalientfeaturesofQBIarethefollowing: Asemanticdatamodelthatcaptureslocallywithinthemobilecomputermost icallygeneratednaturallanguage. querybymanipulatingiconsusingapointingdevicelikealight-penonahandheldpen-computer.bothstructuralinformationandconstraintsarevisualized focalobject,asclassesofobjectsexpressedasgeneralizedattributesofthefocal ofsimplerepresentationstructures.thatis,auserisnotrequiredtohave oftheaspectsofthedatabasestructureswhilepresentingtheuserwithaset ofthedatabaseschema.auserperceivesthewholedatabasefromanysingle anyspecialknowledgeofthecontentoftheunderlyingdatabasenorthedetails whereastheimplicitambiguityoficonicrepresentationisresolvedbyautomat- Intensionalormetaquerytoolsthatassistintheformulationofacompletequery object.generalizedattributesencapsulateandhidefromtheuserthedetails Whileaniconicinterfaceallowsfastinteractions(fasterthantyping)evenwhen accessingactualdataintheremotedatabasetomaterializeintermediatesteps. Dataareaccessedandtransmittedbacktothemobilecomputeronlywhena completequeryismaterialized. duringdisconnections.aqueryisformulatedinanincrementalmannerwithout ofspecifyingaquery. theuserismoving,queryformulationusingintensionaldataosetstheexpense actualdatabase.frequentcommunicationresultsinslowerresponsetimeduetothe limitedbandwidthofwirelesslinks,aswellasconstantdepletionofthecomputer's andlimitationsoffrequentwirelesscommunicationthatisinherenttoextensional battery.therefore,userscanplaninadvancetobedisconnectedfromthenetwork browsingsystems,e.g.,[27,29].metadatacanbecachedonthediskonthemobile computersinceitsdenitionchangesrarelyanditssizeissmallcomparedtothe inordertosaveenergyandreducecommunicationcostswhileactivelyexploringthe databaseviaintensionalinformationonthemobilecomputer.inaddition,users cancontinuewiththeformulationoftheirqueriesonthemobilecomputereven andstationaryhoststoqueryandexplorealargedistributeddatabasemanaged whenthecomputerisaccidentlydisconnected. byanumberofserversonstationaryhosts.inthenextsection,theconceptional modelofqbithatdenesthemobileuser'sperceptionofadatabaseisdiscussed. Section3describesthecomponentsofaQBIprototypeandillustratesitsfunctionalitythroughitsusetoqueryamedicaldatabase.QBI'stheoreticalframeworkis toqbianditssuitabilityasaqueryprocessingfacilityformobileusersisformally discussedinsection5.section6describesandevaluatesthreealgorithmsforgen- Inamobiledatabaseenvironment,weenvisionQBIbeingusedonbothmobile presentedinsection4whereasthenotionofgeneralizedattributeswhichiscentral eratinggeneralizedattributesonamobilecomputer.thepaperconcludeswitha discussiononrelatedworkinsection7andfutureworkinsection8.

3 2.QBI'sConceptualDataModel InQBI,theconceptsofclassofobjectsandattributeofaclassexclusivelyformthe externalrepresentationofthedatabasestructureduetotheirnaturalsimplicity. Usersarepresentedwithadatabaseabstractioncalledcompleteobjects[25],i.e., 3 relationaldatabases[23].specically,auserperceivestheunderlyingdatabaseas completelyencapsulatedobjects,similartotheuniversalrelationabstractionin throughanexample.assumetheunderlyingdatabaseschemadepictedinfigure1 asetofclasses,eachhavingseveralpropertiescalledgeneralizedattributes(ga). usingthebinarygraphmodel[9,25],whichalsoformsqbi'stheoreticalframework(seesection4).heretherectanglesdenoteobjectclassesandovalsconvey propertyofanentity,agaexpressesagenericpropertyofaclass. jectswithineachfocalobject.thatis,otherobjectclassesareviewedasgasof underlyingdatabasefromitsownviewpoint.letusillustratetheconceptofgas thefocalobject.thus,inqbi,eachfocalobjectprovidesaviewofthewhole InthesamewaythatanattributeintheERmodel[10]representsanelementary theinteractionamongclasses. Further,GAsencapsulatebothimplicitandexplicitrelationshipsamongtheob- OfthethreeGAsoftheclasspersonshowninFigure1,considerattributeGA3 AQBIuserobservesthattheunderlyingdatabasecontainsthesameobjectclasses tirestructureofthedatabasebymeansofthegasofanyoftheseobjectclasses. showninfigure1,namely,person,car,city,andhospital,butviewstheen- Figure1.ExamplesofGeneralizedAttributes sonscorrespondingto,"allthepeoplelivinginthesamecitywherethe Thatis,fromtheviewofahospitalobjectclass,thisGAisasubsetofper- perceivesthatahospitalislocatedinacityandisanattributeofperson.a generalizedattributewithsimilarmeaningexistsfromtheviewpointofhospital. whosevalueisasubsetoftheobjectclasshospital.byobservingga3,theuser 1,1 car owns GA2 0,n GA1 GA3 GA1: Birthdate of the person GA2: Set of cars owned by the person GA3: Set of hospitals located in the same city where the person lives 1,1 0,n born 1,1 lives 0,n date city 0,n located 1,1 hospital

4 4hospitalislocated".Also,thesameinformation,couldbeobtainedbyobservingtheGAsofcity. amedicaldatabasethatincludesradiologicaldatafromamobilecomputer. 3.QBIPrototype Inthissection,wewilldescribeourQBIprototypeandhowitcanbeusedtoquery isonly0.5mbyteswhereasitstoreslessthan100kbytesofintensionalinformation abouttheradiologicalmedicaldatabase. toolkitxvt,anditcurrentlyrunningonncrsystem3125pen-topcomputerswith PenDOSandMS-WindowsforPenComputing.ThesizeoftheQBIprototypeitself TheQBIprototypeiswritteninCfortheMS-Windowsenvironmentusingthe Figure2.ThearchitectureofQBI thedatabaseonastationaryhost,aswellasmanagingthesporadicupdatesto consistsoffourmodules:thepresentationmanagerwhichisresponsibleforall themetadataandstatisticsabouttheunderlyingdatabase.gasarecomputedon demandbecausegivenanobjectclass,potentially,thereareaninnitenumberof interactionswiththeuser;thequerymanagerwhichsupportsthespecication AccessManagerwhichisresponsibleforanyremoteaccesstotheactualdatain GAsassociatedwithanobjectclassandonlyasmallfractionofthemareusefulin ofqueries;gaevaluatorwhichcomputesthegeneralizedattributesanddatabase TheoverallarchitectureoftheQBIprototypeisdiagrammedinFigure2and thegaevaluator,issupportedbytwodatabases,namely,theintensionaldatabase (IDB)andStatisticaldatabase(SDB).TheIDBcontainsallthemetadataand thevisualdatafortheiconicrepresentation.whereassdbcontainsstatistical informationontheinstancesinthedatabaseusedfortheevaluationofthegas. theconstructionofaparticularquery. Theexecutionofthepresentationmanageraswellasofthequerymanagerand QBI User Requests Queries Presentation Manager Query Manager Database Access Manager Network IDB GA Evaluator SDB

5 Thepresentationmanagerstructurestheinteractionswithauseraroundthreewindows,eachdedicatedtoaspecicaspectinthespecicationsofaquery.Thethree windowscomposingtheqbiinterfacearereferredtoastheworkspacewindow, thequerywindowandthebrowserwindow QBI'sIconicVisualLanguageInterface WorkspaceWindow WhentheQBIapplicationstarts,theuserisaskedtoselectthedatabasetobe storedqueries(seefigure3). tiveclassesthatareactuallystoredinthedatabaseandderivedclassesrepresenting Workspacewindowappearsanddisplaysasetoficonscorrespondingtobothprimi- consideredforquerying,inourexamplearadiologicaldatabase.inresponse,the shapeofaniconcanappearat,suchasthepatienticon,orasastackofshapes, theimageisalabelthatallowsforeasyidentication.afullnaturallanguage associatedwithaspecicgeometricoutline,similartoajigsawpuzzlepiece.the sentencedescriptionisalsoprovidedthatcanbereadbypointingattheicon.this descriptionisautomaticallygeneratedbasedonthemethoddescribedin[8]andis essentialfordisambiguatingthemeaningbetweenvariousicons.eventheshapeof objectclassesthatareallowedtobecombinedinaselectionconditionofaqueryare theiconconveysinformation.whenformingaquery,iconsrepresentingcompatible Everyiconhasanimageconveyingametaphoricalmeaningfortheclass.Below representsagroupofhospitals QuerySpaceWindow PointingataniconintheWorkspacecorrespondstoselectingitsobjectclassfora suchasthehospitaliconfoundatthebottomofthebrowserwindowinfigure queryviatheactivationofthequeryspace.iftheclassiconforpersonispicked 3.Thisstackedrepresentationtellstheuserhowmanyinstancesoftheobjectclass fromtheworkspace,thequeryspaceshownatthebottomleftoffigure3will canbereferredtowiththisoneicon.thehospitaliconappearsstackedsinceit ConditionsSpace:Thisspaceontheleftsideofthequerywindowallowstheuser tocomposeaquerybasedontheselect-projectparadigm: becomevisibletotheuser.thereareseveralpartstothiswindowthatallowauser tobuildbothgasconstitutingtheatomsofaselectionconditionandthe

6 6 Figure3.TheQBIInterface Figure4.AQuery

7 ShowSpace:Iconscanalsobearrangedinthesectionontherightcalledthe conditionitself.atomscanbecombinedtogether,accordingtoapositional convention,toformthebooleanexpressionrepresentingtheselectioncondition. ShowSpacewhichisusedforspecifyingtheprojection.Theseiconsrepresent theinformationtheuserchoosestoviewintheoutputresult.aninitialga7 DescriptionSpace:Thisspacecontainsanaturallanguagedescriptionoftheclass beingdened.thedescriptionisautomaticallygeneratedanddynamically andcorrespondstotheattributesthatwouldappearinanequivalententity setisdisplayed(bydefault)intheshowspacewhenaclassiconispicked ofperson. updatedwhenevertheselectionconditionschange. classpersonare:nameoftheperson,sexofthepersonandbirthdate Relationshiprepresentationofthedatabase.Forexample,theinitialGAsofthe TheBrowserWindowistheinterfaceoftheGAgeneratorthatallowsauserto TheBrowserWindowandMetaquerying class,howmeaningfulaparticulargaisfortheobjectclass.theadditionalsetof semanticdistanceorweightthatcharacterizes,fromtheviewpointofanobject perceptionofthemostmeaningfulattributesofperson.thebrowserwindowof notincludedintheinitialgasetdisplayedbypressingthebuttonlabeledmore attributes.withagivenobjectclass,itsgasgenerationiscontrolledbya windowactivatedbyselectingtheiconperson,ausercanseeadditionalgas GAsissortedbytheirsemanticdistancesothatthemostmeaningfulGAsareshown rst.thus,byobservingthetopofthelistofgastheusercanhaveanimmediate exploreadatabasebycontrollingthegenerationofgas.fromthequeryspace andshowspaceswithinthequerywindowwhenformingaquery. metaqueryoperatorspermitthespecicationoflterconditionsonthegaset. Hence,auserinterestedinverydistantpropertiesoftheclasspersoncaneasily environment,asetofmetaquerytoolsareprovidedwithinthebrowser.these suchasthisonecanthenbedraggedfromthebrowserwindowintotheconditions Figure3showstheadditionalGACitywherepersonXlives.AdditionalGAs desiresgaswhichareassociatedwithaspecicobjectclass.iftheuserisinterested inallthegasthattalkaboutcity,theiconforcityfromtheworkspacewindowcan smallergaset.forexample,oneusefulmetaqueryoperatorisusedwhenauser exploretheseproperties,byrestrictingthesearchofthedesiredgaswithina bemovedintothetalkaboutspaceofthebrowser(seefigure3).inparticular Toempowertheuserwiththeabilitytocontrolhis/herviewofthedatabase ofaparticularobjectclassand,(3)talkabout,don'ttalkaboutselectsgasthatare itispossibletoexpressthefollowingmetaqueryconditions:(1)single,printable, orkeyselectsonlysinglevalued,printablegas,orkeygasusedtoidentifyan instanceofaclass,respectively,(2)typeselectsallthegasthatrepresentasubset

8 8associatedornotassociatedwithaspeciedclass.Allthemetaqueryconditions 3.2.QueryExamples Letusrevisitthehospitalparamedicunitexamplementionedintheintroduction. arecombinedinaconjunctiveexpressionbydefault. ofthisqueryisasubsetoftheclassdoctorthatcanbesavedasaderivedclass. closeproximityofthehospitalisnotied.forthistypeofinformation,weneedto andcitywheredoctorxworkswiththeconnectiveisequalto. determinethesetofdoctorslivinginthesamecityinwhichtheywork.theresult Asapatientisrushedtothemostappropriatehospital,aspecialistlivingwithin ThisGAisdraggedbytheuserfromtheBrowserwindowintotheconditionspace Inordertobuildthederivedclassitisnecessarytospecifytheselectioncondition Cond:Thecitywherethedoctorworksisequaltothecitywherethedoctorlives. CondcanbespeciedbyconnectingthetwoGAs:Citywheredoctorxlives ofthequerywindow.asfarasthesecondgaisconcerned,theuserneedsto performametaqueryonthegasetofdoctorbydraggingtheiconcityintothe \best"connectionbetweendoctorandcityanditcoincideswiththegatheuser TypespaceoftheBrowserwindow.TherstGAshowninthelistrepresentsthe waslookingfor,thatis:citywhereahospitalislocated.suchahospitalisthe hospitalwheredoctorxworks. TherstGAisimmediatelyfoundbyscrollingthelistintheBrowserwindow. thisoperationtobeperformed.oncethetwogashavebeenattachedtogether,a \attached"together;sincetheyhavethesametype(thatofcity),theirshapesallow connectivetheuserendstheselectionpartofher/hisquery.inthedescriptionspace dialogueboxcontainingasetofvalidconnectivesappears.bychoosingtheequality doctorbecausetheyarepartoftheinitialgasetofdoctorandmustbealready asentenceexplainingtheselectionqueryisautomaticallyadded(figure4).for intheshowspace.inordertoknowwhichcitythehospitalanddoctorarelocated, theprojection,theuserdoesnothavetopickthename,sexandbirthdateofthe ThesecondGAisthendraggedintotheconditionspaceandthetwoGAsare isassignedaniconwhichbecomespartoftheiconsetcontainedintheworkspace windowandcanbeusedasifitwereaprimitiveone.theimageofthisiconwillbe thisquery,alltheuserhastodoisdragthisnewicontoaspecialsystemicon thatofadoctorsincethisgaisasubsetoftheobjectclassdoctor.tomaterialize choosesalabel,sayluckydrs,forthenewlycreatedderivedclass,thenewclass itcanbestoredintheintensionaldatabase.intheaboveexample,aftertheuser theusersimplydragstheappropriate,i.e.,city,iconfromthebrowserintothe querytotheremotedatabase,requestingforitsexecution,andfetchingthequery calledthe\printer".thisoperationcorrespondstoforwardingthecorresponding Theresultofaqueryconstitutesaderivedclassofthepickedclassandassuch resultfordisplay.

9 mentasqueriesrequireverylittletyping.also,onlyasmallscreenisrequired helpfulforuserswithalimitedknowledgeofdatabaselanguages.inaddition,intensionaldataandmetaquerytoolsareprovidedtouserstoallowthemtoformulate sincequeriesdonotrequireanyformofpathspecication.theunderlying,formal schemaishiddenandfeedbackintheformofnaturallanguageandshapesisvery Asseenwiththisexample,QBIisveryusefulinamobilecomputingenviron- 9 4.QBI'sTheoreticalFramework queriesevenwhenthecomputerisdisconnected.inthefollowingsection,wewill Internally,QBIusestheBinaryGraphModel(BGM)[9,25],asemanticdatamodel, theinternalalgorithmsforgagenerationwereimprovedforamobileenvironment. generalizedattributesandsemanticdistance.infurthersections,wewillshowhow formallydenethesemanticdatamodelusedbyqbi,aswellastheconceptsof forcapturingmostoftheaspectsofthestructureoftheremotedatabase.themajor constructsofthismodelare:theclassofobjects,thebinaryrelationshipamong setnofnodesconsistsofclassnodesncrepresentingclassesofobjectsandrole nodesnrrepresentingrelationshipsbetweentwoclasses.classnodescanbeeither schemacanbeexpressedasalabeledgraphcalledtyped-graph. Denition4.1-TypedGraph.Atyped-graphg(N;E)isalabeledmultigraph.The classes,theisarelationshipbetweenaclassanditssuperclass,andcardinality printableornonprintabledependingonwhethertheyrepresentdomainsofvalues orabstractclasses.anedgeinecanonlylinkaclassnodetoarolenodeandis constraintsfortheparticipationofclassinstancesintotherelationships.abgm associatedwithauniquelabell.eachrolenodehasadegreeequaltotwo. representedbygandcwhereasaninstanceofadatabase(extension)arerepresented isasetofconstraints,andmisaninterpretation.theschemaofadatabaseis thetwonodes.eachrolenodewillhaveexactlytwoadjacentclassnodes.when bythenotionofinterpretation. theadjacentclassnodesarecoincidentwesaythattherolenodeisreexive.in thiscase,labelsonedgesareusefulfordisambiguatingthetwoedges. ABGMdatabaseisdenedasatriple<g;c;m>,wheregisatyped-graph,c Aclassnodeissaidtobeadjacenttoarolenodeifthereisanedgeconnecting Denition4.2-Interpretation.Letgbeatyped-graph.Aninterpretationfor gisafunctionmmappingeachclassnodenc2nctoasetm(nc)ofobjects wherelbl1;lbl2arefunctionsreturningthelabelsofthetwoedgesconnectedtonr adjacentclassnodeofnr. andeachrolenodenr2nrtoasetm(nr)ofpairs<lbl1(nr):x1;lbl2(nr):x2>, (lbl1;lbl2:nr!l)and<x1;x2>2m(nc1)m(nc2)wherenc1andnc2arethe

10 10Thatis,aninterpretationspeciesthevalidcombinationsofvaluesfromthe underlyingclasses.thesetofconstraintsonthedatabasereferredtointhispaper aretheminimum(atleast)andmaximum(atmost)cardinalityconstraints, andthesubclass-superclassrelationshipconstraint(isa). Denition4.3-Constraints.Thesetccontains:(1)ATLEAST(k;nc1;nr)speciesthataninstanceofclassnodenc1canparticipateinatleastkinterpretation involvingtheadjacentrolenodenr(2)atmost(k;nc1;nr)speciesthataninstanceofclassnodenc1canparticipateinatmostkinterpretationinvolvingthe adjacentrolenodenr.(3)isa(n^c;nc)speciesthattheclassn^cisasubsetofthe classnc(i.e.,m(n^c)m(nc)).therolenodesconnectedtoncareconsideredas alsobeingconnectedton^c,i.e.,n^cinheritstheedgesofnc. Currently,weassumesingleinheritance,henceeachclassnodehastobelongto oneandonlyoneclasshierarchy.inordertofacilitatetypecheckingofquery expressions,wedenethenotionoftypeofaclassasaclasshierarchy.thatis,the classnodesbelongingtoaclasshierarchyhavethesametype.notethatifnc1and nc2havedierenttypes,theirinterpretationsaredisjoint. person string date hospital car city name name model name born lives located owns 0,n 1,1 1,1 0,n 1,1 0,1 0,1 0,1 0,1 1,1 0,n 1,1 1,1 0,n 1,1 1,1 patient disease disease heart name 0,1 1,1 had 0,n 0,n Figure5.ATypedGraph Figure5showsanexampleofatypedgraphwhichisafragmentofthemedical databaseusedintheprevioussection.therectangularboxesrepresentclassnodes (theprintableonesaregrayed)whiletheovalsrepresentrolenodes.nolabelonan edgeisshownsincetherearenoreexiverolenodesthatneedtobedisambiguated. Theannotations(m,n)onedgesrepresent(ATLEAST,ATMOST)cardinalityconstraints.ISAconstraintsaredenotedbyathickarrowfromasubclassnodetoits superclass.

11 Asmentionedabove,theconceptofGAinQBIrepresentsthewayinwhichauser perceivestherelationshipsamongobjects.internally,agaisstrictlyrelatedto 5.GeneralizedAttributes theconceptofpathinatyped-graphcapturingthedatabaseschema,whereapath 11 isasequenceofadjacentclassandrolenodesalwaysstartingandendingwitha withfirst(p)andlast(p)respectively. classnode. Denition5.1-Path.LetGbeatyped-graph.AstepsonGisthetriple <class1(s),role(s),class2(s)>whereclass1(s)=nc1;class2(s)=nc22ncare adjacenttorole(s)=nr2nr.apathpongisthesequences1;s2;skofsteps classnodeofapathp,i.e.class1(s1(p))andclass2(slength(p)(p)),willbedenoted ongsuchthat,fori=1k?1,class2(si)=class1(si+1).therstandthelast endingin^ncdenesagaofncasafunctionmappingeachinstancexofnconto Denition5.2-GeneralizedAttribute.LetGbeatyped-graph,ncaclassnodeofG asetofinstancesof^nc. andpapathongsuchthatfirst(p)=nc;thegaoftheclassnodencassociatedto pisafunctionp:m(first(p))!}(m(last(p)))mappingeveryobjectx02m(nc) toasubsetofobjectsofthelastclassnodeofp,m(last(p)). Giventwoclassnodesnc(pickedclassnode)and^nc,apathpstartinginncand AGAcanbeeithersinglevaluedormultivalueddependingonthecardinalityconstraintsoftherolenodesinvolvedinthepath.SinceaGAisafunctionpreturning typeoflast(p). asetofobjectsbelongingtom(last(p))wewillsaythatphasatypethatisthe onlythosegasthatare\meaningfulenough"forthespecicationofaquery.a representusefulproperties,e.g.,peoplelivinginthesamecitywherethe similarnotiontothatofsemanticdistancefunctioninqbiisthenotionofsemantic withinnitelylong(cyclic)paths,qbidenesasemanticdistancefunctionon personlives.asaconsequencethesetofpossiblegasassociatedtoaclass correspondingga.anitesetofgasofanobjectisconstructedbyconsidering pathswhichreturnsavalueforeachpathrepresentingthemeaningfulnessofthe nodeisinnite.sincenotallpathsareequallymeaningful,andinordertocope Apathinatyped-graphcanbecyclic.Cyclicpathsareallowedsincetheycan lengthinthegenerationofcompletequeriesfromincompletepathexpressions[17]. Denition5.3-SemanticsDistanceFunction.LetpbeaGAofaclassnodenc. ThefunctionSemd:?(nc)!<mapsptoarealvalueSemd(p)thatrepresents howmuchpissemanticallydistantfromnc.

12 12Thesemanticdistanceisexpressedintermsofvariousaspectsofthestructureof nameofacar,"canbediscarded. lessmeaningfulgas,suchas"allthepersonsthathaveanameequaltothemodel thegasuchasthelengthofthepath,thenumberofcycles,inclusion/exclusion conveyssomeinformation.bytakingentropyintoconsideration,alargenumberof TheentropyclearlycapturesthefactthatauserconsidersaGAonlyiftheGA ofspecicpaths,cardinalityconstraintsaswellasstatisticalinformationonthe tropyofagawhichisthemeasureofuncertaintyintheinformationtheory[13]. underlyingdatabase.thestatisticalinformationcanbeusedtocomputetheen- providedthatsemdismonotonicallyincreasing. GAsetofnc,withrespecttoSemd,willbedeterminedby: GivenasemanticdistancefunctionSemdandathresholdvalue2<,thenite GAp.Letthenewstepbeingaddedtopbee=<u,v,w>(i.e.,p0=p[e). semanticdistanceofanewgap0resultingfromaddinganewsteptoanexisting ThefollowingfunctionwasimplementedintheQBIprototypetocomputethe?Semd(nc)=fp2?(nc)jSemd(p)<g Semdp0=(c1length(p0))+(c2numcycles(p0)+ (c3maxcardinality(p)atmostcardinality(e)-1)+npwc4+ lengthisdenedtobethenumberofrolenodesconnectorsusedbythepathand mostcardinality(e)),sodoesthesemanticdistanceassociatedwiththisga.the wherenpw,npuandeareagswith0or1value,andc1;c2;c3;c4;c5andc6 existinanewgapath.asaga'spathbecomeslonger(length(p0)orcyclic (numcycles(p0)),oritscardinalitydramaticallyincreases(maxcardinality(p)at- arepositiverealconstantsusedassemanticpenaltiesforpropertiesthatmay E(c5=avgcardinality(p0)?c5)+NPUc6 doesnotincludeanyclass-superclassconnectors(i.e.,``isa''connectors). lationshipbetweenncandanotherabstractobjectclass.thisobjectclasswill noinformationfortheuserthatisdirectlyobtainablefromthispath,thepathis penalizedbyassigningnpwto1.otherwisenpwis0. containsimpleattributesthathavenotbeendiscovered.however,becausethereis nodesis300andisequivalenttothepenaltyc2associatedwithaclassnodebeing amemberofacycle.theotherpenaltyconstantsarec1=200,c3=0:02,c4=20 Apaththatendswithaclassnodewwhichisnotprintable,representsare- andc5=100.allpenaltyconstantshavebeadjustedforbetterperformanceafter aseriesofexperimentswhilemakingsurethatthesemdremainsmonotonically increasing. IntheQBIprototype,thecurrentweightincreasec6forgoingthroughprintable

13 6.MobileGAGeneration Inamobileenvironment,QBI'smethodofqueryformulationanduseofintensional queryformulationisdoneprimarilythroughthemanipulationofgeneralizedattributes.asstatedabove,agapissimplyapathpinatyped-graphg.every timeastepisaddedtoanexistinggapath,anewgaisformedanditsasso- oftheenvironmentinwhichqbiisexecuting.ontheotherhand,thevaluefor denedasafunctionoftheavailablememory,theenergylevelandtheresponse ciatedreal-valuedsemanticdistancevalue(sd-value)iscomputedbythefunction alizationofcompletequeries.visualizationofthedatabaseaswellascosteective datalimitsthecostoffrequentwirelesscommunicationwithrespecttothemateri- 13 time: accuracytoexpressthemeaningfulnessofaga.however,thiscostisindependent thethresholdthatterminatesthegenerationofgascanbetunedtoconsiderthe capabilitiesofthesystem.inthecaseofthemobilecomputer,thethresholdis isclearlythemostcomputationallyexpensivepartofqbi.thecostofthesemanticdistancefunctiondependsonitscomplexitywhich,inturn,isameasureofits Semdp.ThegenerationofGAsisaninstanceofapathcomputation[2,16],and oryandenergylevel,varyovertime,thethresholddynamicallychangesaswell. whereciareuserdenedparameters.giventhatthersttwoparameters,freemem- =C1(freememory)+C2(energylevel)+C3(cpuspeed)+C4(resprequirements) usedinpathcomputations[2,16],ithasturnedouttobeunsuitableformobile depth-rstsearch(dfs)manner.althoughthisisalsotheapproachtraditionally operations.first,sinceitdoesnotgenerategassortedbasedontheirsd-value, anadditionalsortingphaseisrequiredforthepresentation.moreimportantly,the spendingasignicantamountoftimeingeneratinggaswithhighersd-values.for forthepossibilityofadynamicallysetthresholdtoterminatetheexecutionofthe GAevaluatorbeforethegenerationofGAsassociatedwithalowsd-valueandafter DFSstrategyisnotcompatiblewithadynamicallydenedthreshold.DFSallows TheinitialGAevaluatorinourQBIprototypetraversesthetyped-graphina thesereasons,wehaveexploredtwootheralternatives,thebest-rstsearch(best) andbreadth-rstsearch(bfs)basedgaevaluators. thegasfoundbasedonthesemanticdistancesassociatedwiththegas.ateach iteration,bestalwaysconsidersthegawiththeminimalsemanticvalue.the BEST-basedalgorithmuses:(1)asortedListtomaintainanorderamongtheGAs produced,(2)nc,thestartingclassnodechosenbytheuser,(3)apathpassociated witheverygapandatotalsemanticdistancesemd(p),and(4)thefunction, weight(u,w),whichcomputesthedistancebetweentwoclassnodesuandw,where Gisthegiventyped-graphandforeachstepe2G,e=<u,v,w>.Belowareshown thebasicstepsforthealgorithm. TheBESTalgorithmexploresthegivengraphGbymaintaininganorderamong

14 14 BEST(!=nc,p,,?(nc)) DoForeachstepe=<u,v,w>whereu=! Markeveryclassnodeinpofpasnewlyvisitedinordertodetectcycles. calculatesemd(p0)=semd(p)+weight(u,w) IfSemd(p0)Then WhiletheListisnotempty EndFor IftheListisnotempty p=firstoflist(list)!=last(p)(thepassociatedwithournewp)?(nc)=?(nc)[p p0=p[e SortedInsert(List,p0,p0,Semd(p0)) incrementalsortwithmanycomparisonswouldbeavoided.however,ithasturned moreoften,averysmallnumberofgaswouldbesortedeachtime,andbest's theclassnodesatalevelxwereexplored.although,sortingwouldbeperformed pathsthataretobeexpandedisnowdonebyaqueueinsteadofalist. levelareexploreddoesthealgorithmmoveontothenextlevel.alloftheitems usedbybestarealsousedbybfs,exceptmaintaininganorderamongthega typed-graphginalevel-by-levelfashion.onlywhenalltheclassnodesatagiven InaccordancetothebasicBFSalgorithm,theQueuewassortedeverytimeall TheBFSalgorithm,thesecondalternativeGAevaluator,exploresthegiven NPW,NPU,andEwhosevaluesaredependedonthetypeoftheclassnode,all ofgasendingatdierentlevelsalongdierentpaths.becauseofthebinaryags sortingphaseasdfs. 6.1.AdvantagesandDisadvantagesofDFS,BFS,andBEST outthatthisisnotenough,sincetherearenoorderguaranteesamongtheweights WithBESTandBFS,themainadvantageoverDFSiseciencyinndingmeaningfulGAs.BESTgeneratesGAsintheorderoftheirmeaningfulnesstotheuser basedontheirsemanticdistancefromthefocalobjectclassnc.withbfs,gas pathsproducedbypthatendatlevelxarenotguaranteedtohaveasmallersdvaluesthanallpathsfoundatlevelx+1.hence,bfsrequiresforanexplicit couldbesetbyafunctionthatdescribesthelimitationsofthemobileunit.with BEST,onlythemostmeaningfulGAswithrespecttotheselimitationswouldbe generated,anditdoesnotrequireaseparateexplicitsortingphase. withtheshortestpathsaregeneratedrst,anditishighlylikelythatthesepaths areverysemanticallymeaningfulfromtheviewpointofncduetothemonotonicallyincreasingpropertyofsemdalongapath.theorderingperformedbybest andbfsisusefulinamobileenvironmentsincethesemanticdistancethreshold

15 Ifthegraphisverydense,witheachnode!havingalargedegree,thenumberof combatthecostofinsertion,however,thisapproachwouldrequiremorespace.one comparisonswillbeveryhigh.anadditionalindexingstructurecouldbeusedto theformofasortedlistorqueue.intheworstcase,eachelementaddedtothe Listmustbecomparedtoalltheotherelementsbeforendingitscorrectlocation. However,unlikeDFS,bothBESTandBFSmaintaincomplexdatastructuresin 15 incrementalsortingoneachinsertionofanewgathewaybestdoes. advantagebfshasinthisregardisthatitdoesnotneedtoperformanexpensive fromthefocalpointobjectncarebeingexpandedconcurrentlyandbothreachthe requiresamarkerassociatedwiththenodew.iftheclassnodewisalreadya endingclassnodewtobecomeamemberofacycle.detectionofthisindfsonly partofthepathpbeforetheadditionofe,thenthenodewillbemarked.hence, marked.thispreventsthedetectionofafalsecyclewhenevertwoseparateroutes anotherfactorthatincreasestheexecutiontimeofbestandbfswhencompared todfs,isthemethodneededforcycledetection.inordertodetectifthenodewis partofacycle,alltheclassnodesofthepathpcurrentlybeingexpandedmustbe Inaddition,addingastepe=<u,v,w>toanexistingpathpmaycausethe samenode.however,thecostforthisisonetraversalofthepathpeverytime disadvantages,furtherinvestigationisnecessaryinordertodeterminewhichoneis Totheuser,thetwomostimportantcriteriaareresponse-timeandqualityofGAs i.e.,thesemanticdistancesofthegasproduced.thesearetwoimportantcriteria themostusefulwithrespecttoamobilecomputingenvironment. 6.2.EvaluatingtheGAGenerationMethods astepisadded.sinceeachofthesealgorithmshavecomparableadvantagesand indetermininghowwelleachofalgorithmsproposedforthegaevaluatorperform. However,withinamobileenvironment,theGAevaluatorshouldoperatewithout threeimplementationsunderadynamicallychangingthreshold.theseexperiments evaluated. regardstothequalityofattributesfoundbyeach.thesecondtestevaluatesthe discussedintheprevioussections.thersttestcomparesthethreealgorithmswith depletingalargeamountofthemobileunit'sresources.themethodproposed notionofadynamicallychangingthreshold.howeectiveachangingthreshold performswithrespecttondingmeaningfulattributesandresponse-timemustbe aboveforcontrollingtheamountofresourcesusedbythegaevaluatoristhe Test1:MeaningfulAttributeTest(MAT) weredoneusinganintel486dx,66mhzpcwith16mbytesram. TwotestsweredoneinordertoevaluatethethreeGAGenerationMethods forthedoctorobjectclassintheradiologicaldatabaseoftheqbiprototype. Task:Eachalgorithmwasgiventhetaskofndingagivennumber(X)ofGAs

16 16Parameters:Thesemanticweightthresholdremainedaconstant1800.At Eachalgorithmwastimedfromthemomentitwasinvokeduntilitwasableto produceasortedlistoftherequirednumber(x)ofattributes.inaddition,to measurethequalityoftheattributesproducedbythealgorithm,acomparisonwas madetoseehowmanyoftheattributesproducedbythealgorithmmatchedthe rstxattributesfoundinsal. Figure6isagraphofthesetestruns. thisvalue,thesystemproducedasortedattributelist(sal)of947gas.for eachsuccessivetestrun,thenumberofgasrequired(x)wasincreasedby100. Figure6.MAT:Qualityofattributesfound Test2:DynamicThresholdTest(DTT) Eachalgorithmwastimedfromthemomentitwasinvokeduntilitwasableto cessivetestrun.itwasincrementedbyavalueof100foreachrun. distancethresholdforthedoctorobjectclassintheradiologicaldatabaseof Parameters:Thesemanticdistancethresholdwaschangedforeachsuc- theqbiprototype. Task:EachalgorithmwasrequiredtondalltheGAs,belowagivensemantic numberofattributeswithingthatarebelowthecorrespondingthreshold. produceasortedlistofattributesbelowthegivensemanticdistancethreshold. Figure7isagraphthatshowshowmanysecondsittookeachalgorithmtond attributesinthetyped-graphgwillhavedistancesbelow.inordertocompare allthegasbelowagiventhreshold.foreachthreshold,acertainnumberof Inthisgraph,theamountoftimerequiredbyeachalgorithmismappedtothe theresultsofmatwiththistest,anothergraphshowninfigure8wascreated. Number matching first X of SAL dfs bfs best Number of attributes required (X)

17 # seconds to find attributes <= threshold 17 mationtheywantedifthebrowserwindowalwaysdisplayedtherst450most agiventhresholdvalue ImplicationsofMATandDTT Supposethatinamobileapplicationuserswouldbeabletoobtainalltheinfor- Figure7.DTT:TimetondattributesbelowFigure8.DTT:Numberofattributesfound belowagiventhreshold 0.72secondsusingBFScorrespondstoathresholdof=1600.Ofcourse,these meaningfulattributesforthedoctorobjectclass.figure6showsthatbfsnds thetop450mostmeaningfulattributesbygeneratingatotalof600attributesfrom thetyped-graphg.fromfigure7,itisapparentthatbfsndsthese600attributeswithin0.72seconds.also,fromfigure8,nding600attributeswithin 0 atablethatcorrespondstotheanswersfordfs,bfs,andbestforthebrowser Windowtoshow400,600,and800ofthemostmeaningfulattributestotheuser. gurescouldhavebeencalculatedusinganyofthethreeimplementations.belowis numberattributesdfssec.bfssec.bestsec. semanticdistancewhiledfsperformstheworstatgeneratingmeaningfulgas ThisresultwasexpectedsinceBESTdoesnotrequireanadditionalexplicitsorting phase.bfs'sperformancewasbetweendfsandbest.thealgorithmdoesrequire MATandespeciallyFigure6,showthatBESTndsGAsinorderofincreasing aexplicitsortingphase,butbecauseitgeneratesthegasbylevels,itsperformance intermsofgeneratingmeaningfulgaswasbetterthandfs dfs bfs best Maximum semantic weight threshold # seconds to find attributes <= threshold dfs bfs best Number of attributes found

18 matelythesameamountoftimeuntilthesemanticweightthresholdreaches1400. Soonafterthispoint,DFSbeginstotaketheleastamountoftimeandBESTthe increases,moreofthetyped-graphisexplored.allthreealgorithmstakeapproxi- most.thisphenomenoncanbeexplainedbynoticingthatastheexplorationofthe timeeachalgorithmrequirestodoitswork.thishappensbecauseasthethreshold 18InFigure7,weobservethatasthethresholdchanges,sodoestheamountof generatedthathavethesamepropertiesand,therefore,approximatelythesame graphmovesfurtherawayfromthefocalpointobjectclassnc,moreattributesare tohaveapproximatelythesamesemanticmeaningfromtheviewpointofnc.since BESTmustmaintainasortedlist,everytimealargegroupofcloselyweightedGAs semanticdistance.theselarge,similargroupsofgasarefarenoughawayfromnc thresholdbyafunctiondependingonhowmuchmemory,processing,batterypower, alargenumberoftraversalsandaccountsforthedecreaseinbest'sperformance areproduced,theymustbeplacedintheirproperpositioninthelist.thisrequires Withinamobileenvironment,themobilecomputercouldsetthesemanticdistance AnIntegratedMobileGAEvaluator asthethresholdincreases. littlevaluetotheuser.bestcangeneratethemostmeaningfulgaswithrespectto theselimitations.althoughthebestalgorithmismorecompatiblewithamobile environment,itsresponse-timesuersasthethresholdisdynamicallyincreased. tryingtoreserveitsresourcesandnotwastetimeorenergyndinggasthatareof inqbiwitheitherthebestorbfsalgorithms,ourexperimentationclearly tor.therefore,weproposetocombinethesetechniquesintooneintegratedmobile anddelaythehostandusercanaord.therefore,themobilecomputerwouldbe onthemobilecomputer.bestwillbethedefaultalgorithmofthemobilega GAevaluator.Sinceeachalgorithmonlyrequiresafewkilobytes,thisproposedintegratedevaluatorwillnotrequireadramaticincreaseintheamountofcodekept longasalowsemanticthresholdiscomputed,thebestalgorithmwillbeused. evaluator,sinceitdoesproduceasortedlistofgas.inamobileenvironment,as AlthoughouroriginalintentionwastoreplacethecurrentlyusedDFSalgorithm showedthatonealgorithmdoesnotmeetalltheneedsofamobilegaevalua- waythattheerthresholdwassetforthequerywindow(seesubsection3.1.2).as supportstation),ortheuserwantstoexaminealargenumberofgas,aswitch toa\focus"dfsalgorithmwouldfacilitatetheexplorationofthedatabaseusing moreresources.therefore,gaevaluatorthresholdscanbesetinmuchthesame host(e.g.,whenthemobilehostisstationaryandattachedtodockingmobility- abetterresponse-time.finally,whenevertherearefewrestrictionsonthemobile changesdynamicallyandcapturesthestatusofthemobilehost,thesearchtechniqueusedbythegaevaluatorwillalsochangetoaccommodatetheselimitations. However,asthelimitationsofthemobilehostarerelaxed,theGAevaluatorwill switchtoabfsthatcanfacilitateabroader,sweepingsearchofthegraphwith WearecurrentlyinvestigatingthisintegratedmobileGAevaluator.

19 graphicaluserinterfaces,anddatamodeling. 7.RelatedWork Mostoftheworkrelatedtoourapproachhasbeendoneintheareasofdatabase 19 highexpressivepowerofthequerylanguage[7,20].mostoftheproposedsystems Usersdonotneedtorememberattributenamesorvariablenames.Queriesare ducedwiththepurposeoffacilitatinguserinteractionwhilestillmaintainingthe quested.g+makesuseofadiagrammaticparadigmbyusingagraphwhoseedges [35]andG+[11].InQBE,thequeryismadebyllingintemplatesofrelations. adoptform,tabular,ordiagrambasedvisualparadigms.earlyexamplesofthese typesofvisualquerylanguagesthatusearelationalexternaldatamodelareqbe speciedbytypingexampletuplesexpressingtheinformationthatisbeingre- Intheareaofgraphicaluserinterfacesalargeamountofresearchhasbeenpro- correspondtothetuplesinarelation. vidingtheuserwithamoreabstractlogicalviewofthedata.ofthese,theer graphicalvisualquerysystemsthatprovidetheuserwithanerdiagramofthe model[10]isoftenusedastheexternaldatamodelinexistingvisualquerysystems. GORDAS[12],QBD[5],GRAQULA[28,33],andGQL/ER[34]areexamplesof schema.queriesinthesesystemsareformulatedbydrawingnodesandedgestobe conditionsandprojectionsarespeciedasannotationsofthenodesandedges.for ERschemadiagramwithcertainnodesandedgesreplicatedasnecessary.Selection matchedintheschemadiagram.thatis,queriesarespeciedassubgraphsofthe Semanticdatamodelsgoevenfurtherthantherelationalmodelintermsofpro- ofinterest,asimpliedhierarchicaldiagramoftheschemaisprovidedinorderto example,ingordasandqbd*,onceauserselectstheentitiesandrelationships theermodelwithoutthesupportofaggregationorquantication.picasso[19], ontheotherhand,providesanexternaluniversalrelationdatamodelthatinterfacesauniversalrelationaldatabasesystem.inpicasso,maximalobjectsare aidintheformulationofqueries.ingeneral,thedierencebetweenthesesystems cursion.further,gql/ercombinesfeaturesoftheuniversalrelationalmodeland istheirvaryingsupportinthespecicationofaggregation,quantication,andre- operators,userscandynamicallyconstructportionsofthedatabaseschemaand perusetheschemaforrelatedinformation,havingcompleteaccesstoanunderlying querysystemwhichasqbiisbasedonarichersemanticmodelthantheermodel ableand,therefore,nocharacter-typetuplevariablesarenecessary.anothervisual representedashyperedgesinahypergraphwhichcontainstextualattributelabels. semanticdatalanguage.similartoqbi,thisperusalisnotperformednavigationallybutsemantically.asopposedtoqbi,skiisdiagrambasedandsupports theselectionofaggregateandsetoperatorsaswellascomparisonoperatorsused inpredicateformulation.addingahyperedgewiththemousecreatesatuplevari- anduniversalrelationalmodelisski[21].inski,bymeansofasetofsemantics Queriesareformulatedviamouseclickswhichrevealpop-upmenusthatallowfor navigationthroughthepathsoftheunderlyingdatabaseschema.

20 userswithlittle,ifany,trainingindatabases,andskilledones.thetwoperformancemeasuresusedwerethetimeinsecondstocompleteaqueryandaccuracyof thequery.eachgroupofusersafterashorttrainingsessionofequaltimesinusing ationofqbiandqbd[24,6].inthisstudyuserswereclassiedintounskilled withrespecttounsophisticateduserswasestablishedthroughanempiricalevalu- 20Comparedtodiagrammaticvisuallanguages,theeaseandeectivenessofQBI language.usersweregiventhesequeriesindierentordersinordertominimizethe QBIandQBDweregivensixqueriesofdierentlevelsofcomplexityinnatural learningeect.ingeneral,unskilledusersdidbetterwithqbiwhereasskilledones feltmorecomfortableusingqbd,particularlyinexpressingqueriescharacterized byahighsemanticdistancevalueinvolvingpathsoflength4,ormore,andwithno cycles.thereasonwasthatskilledusersperceivedthewholepathnotasasingle acleardistinctionamongdierentoccurrencesofthesameconcept. ofthesameconcept(entityorrelationship).onthecontrary,inqbiapathcorrespondstoagaandeverygaisvisuallyrepresentedasadierenticononthe andcontrolled.ontheotherhand,therewasasignicantdierenceinaccuracy complexfunction,i.e.,ga,butasasequenceofstepsthatcanbemanuallybuilt andperformanceforquerieswithlowsemanticdistancevalueorqueriesinvolving screen.therefore,whenaqueryexpressioncontainscycles,theuserstillperceives cycles.inthepresenceofcycles,qbdusersgetmuchmoreconfusedbecausethey seemultiplecopiesofthesameform,eachcorrespondingtoadierentoccurrence palmtopcomputerandthelimitedpossibilityofusingakeyboard,maketheiconic particular,othersystemsdonotusuallyassignuniformsemanticstoicons.also, approachparticularlysuitablefortheusersofamobilesystem.ingeneral,themain asopposedtoqbi,thesesystemsadopttheextensionalbrowsingapproach(that evidentthatagreateramountofworkhasbeenperformedusingformanddiagrammaticparadigms.however,thesmallscreenspaceofthetypicalnotebookor Whencomparedtotheworkdoneinvolvingiconbasedvisualparadigms,itis dierencebetweenqbiandtheothericonicinterfacesproposedintheliterature [14,30,29]isinthewayiconsaredenedandusedforexpressingconcepts.In links. is,browsingofinstancesintheremotedatabase)astheprincipalqueryingstrategy [27,29]hencemakingthemunsuitableformobileenvironmentsthatarecharacterizedbylowcommunicationbandwidthoverexpensivewirelesscommunication needforanalternativevisualqueryparadigmformobile,pen-basedcomputersthat takesintoconsiderationtherequirementsofmobileusers,suchasexplorationof screenandnokeyboard,wasrstidentiedin[3].asopposedtoqbi,theproposed alargedatabaseschema,andthelimitationsofmobilecomputers,suchassmall datamodelwhichusestheuniversalrelationapproachatdierentlevelstocoalesce alternativeisform-basedwhereastheexternaldatamodelisamulti-levelsemantic relatedinformationandeliminatelow-levelinformationnotrelevanttotheuser.as Allvisualqueryinterfacesdiscussedabovehavebeenproposedinthecontext ofworkstationswithlargescreens,graphicscapabilitiesandpointingdevices.the statedearlier,theconceptofgasinqbiservesasimilarpurpose,coalescingrelated

21 informationofanobjectfromtheperspectiveoftheuserandrepresentingitonthe screenwithanicon. strictive,anumberofauthorshaveproposedvarioussolutions.in[18],pathex- pressionsareexaminedandformthebasisofthexsqlsystem.xsqlallowsthe specicationofpathvariablesbymeansofwhichincompletepathexpressionscan Recognizingthatquerylanguageswhichrequirefullyspeciedpathsaretoore- 21 bespecied.in[17],pathexpressionsareconsideredtobeabbreviatedqueries withinauserinterfacetoadatabasesystem.givenanambiguouspathexpression whichcouldresultinmultiplepossiblepaths,thetaskistondthosecompletions mostlikelyintendedbytheuser. PICASSO[19]providesagraphicalqueryinterfacebasedontheUniversalRelation basicrelationthattheuserhasinmind.thisrelationiscomputedthroughthe adecisionproblemconcerningwhichisthemostmeaningfulattributeistackled. WindowFunctiononthesetofattributenamesX.WithinaWindowFunction Model.Thechoiceofassigningameaningtoanattributenameisbasedonthe embeddedinattributenamesandforeverysetofattributesxthereisaunique analysisoftheschemaoftheunderlyingdatabaseandvariouskindsofdependencies. Withthisapproach,thesameattributenamecanhavedierentmeaningsifused TheideaoftheUniversalRelationModel[23,22,31]isthataccesspathsare uatingthesemanticsoftheattributes,iscommontobothqbiandtheuniversal Relationapproach.However,thequeryingstrategy,isslightlydierent.Insteadof internalschemaofthedatabase,she/hemustbeawareofthedomainofinterest. indierentcontexts;asaconsequence,eveniftheuserisnotrequiredtoknowthe thesemanticdistancefunctiontopresenttotheuserallthemeaningfulattributes beforethequeryiscomposed.moreover,theuseofasemanticmodelandstatisticalinformationonthedatabaseextensionallowsthedenitionofarichernotion ofmeaningfulnessofanattribute. Theideaofpresentingtotheuserasimpliedstructureofthedatabasebyeval- assigningameaningtoanattributeafterthequeryhasbeenspecied,qbiuses 8.Conclusions Inthispaper,wehavedescribedanicon-basedqueryprocessingfacilitycalledQBI, suitableformobileusers.thatis,qbisatisesallthreeofthecriteriaidentied intheintroductionforaneectivemobilequeryprocessingfacility: languagedoesnotinvolvepathspecicationincomposingaquery.thus,itis sizelimitationsofamobilecomputerwhilenewrequirementsarenotimposed. (1)QBIallowstheconstructionofadatabasequerywithnospecialknowledge ofhowthedatabaseisstructuredandwhereitislocated.itsiconicvisualquery equallyusefultobothunsophisticatedandexpertmobileusers. (2)Usersprimarilyinteractwiththesystemwithapointingdevice,suchasa penoramouse,andcomposeaquerybyarrangingicons.thus,itovercomesany

22 22(3)QBI'salgorithms,particularlythemetaquerytoolsandGAevaluator,are inextendingaspectsofthisworkinordertominimizetheamountofretrievedand transmitteddataoverwirelesslinks. resourceconstraintssuitableformobilequeryprocessing.further,weareinterested Asmentionedabove,wearelookingintointegratedpathcomputationsunder batterypower,andrestrictedwirelesscommunicationbandwidth. designedtoeectivelyoperateunderlimitedmemoryanddiskcapacity,limited ThisworkwassupportedinpartbyNationalScienceFoundationundergrants Acknowledgments participationinthedevelopmentoftheprototype. IRI andIRI (USA),IntegraSistemiInterattivi,andComputer andmicroimages.p.a.(italy).wealsothanks.pavaniandl.saladinifortheir References 3.AlonsoR.,E.Haber,andH.Korth.ADatabaseInterfaceforMobileComputers.Proceedings 5.AngelaccioM.,T.Catarci,andG.Santucci.QBD*:AGraphicalQueryLanguagewith 4.AlonsoR.,andH.Korth.DatabaseIssuesinNomadicComputing.ProceedingsofACM 2.AgrawalR.,S.Dar,andH.Jagadish.DirectTransitiveClosureAlgorithms:Designand 1.AbiteboulS.,andA.Bonner.ObjectsandViews.ProceedingsoftheInt'lConferenceACM- Recursion.IEEETransactionsonSoftwareEngineering,16(10): ,1990. PerformanceEvaluation.ACMTransactiononDatabaseSystems,15(3): ,1990. ofthe1992globecomworkshoponnetworkingofpersonalcommunicationapplications, SIGMODInt'lConferenceonManagementofData,pp ,May1993. Dec SIGMOD,Denver,Colorado,pp ,June BatiniC.,T.Catarci,M.F.CostabileandS.Levialdi.VisualQuerySystems.TechnicalReportNo DipartimentodiInformaticaeSistemistica,Universita'diRoma\Lgrammaticvs.anIconicQueryLanguage.(submittedforpublication),Feb Sapienza",Mar Germany,pp ,Oct icallanguage.proceedingsofthe11thint'lconferenceonentity-relationshipapproach, 8.BonoG.,andP.Ficorilli.NaturalLanguageRestatementofQueriesExpressedinaGraph- 6.BadreA.N.,T.Catarci,A.Massari,andG.Santucci.ComparativeEectivenessofaDia- 9.CatarciT.,andG.Santucci.FundamentalGraphicalPrimitivesforVisualQueryLanguages. 14.GroetteI.P.,andE.G.Nillson.SICON:anIconPresentationModuleforanE-RDatabase. 13.GallagerR.G..InformationTheoryandReliableCommunication.Wiley,NewYork, ChenP.P.TheEntityRelationshipModeltowardaUniedViewofData.ACMTransactions 12.ElmasriR.,andG.Wiederhold.GORDAS:AFormalHigh-levelQueryLanguageforthe 11.CruzI.F.,A.O.Mendelzon,andP.T.Wood.G+:RecursiveQuerieswithoutRecursion. Approach,Washington,D.C.,pp.49-72,1981. Proceedingsofthe2ndInt'lConferenceonExpertDatabaseSystems,pp ,1988. InformationSystems,3(18),pp.75-98,1993. Proceedingsofthe7thInt'lConferenceonEntityRelationshipApproach,Roma,Italy,pp ,1988. ondatabasesystems,1(1),1976. Entity-RelationshipModel.Proceedingsofthe2ndInt'lConferenceonEntity-Relationship

23 23 15.ImielinskiTandB.R.Badrinath.MobileWirelessComputing:ChallengesinDataManagement.CommunicationofACM,37(10):18-28,Oct IoannidisY.E.,R.Ramakrishnan,andL.Winger.TransitiveClosureAlgorithmsBasedon GraphTraversal.ACMTransactionsonDatabaseSystems,18(3): , IoannidisY.E.,andY.Lashkari.IncompletePathExpressionsandtheirDisambiguation. ProceedingsoftheACMSIGMODInt'lConferenceonManagementofData,Minneapolis, MI,pp ,May KiferM.,KimW.,andSagivY.QueryingObjectOrientedDatabases.Proceedingsofthe ACMSIGMODInt'lConferenceonManagementofData,pp ,May KimH.,H.Korth,andA.Silberschatz.PICASSO:AGraphicalQueryLanguage.Software PracticeandExperience,18(3): ,Mar KimW.IntroductiontoObject-OrientedDatabases.MITpress,Cambridge,MA, KingR.,andS.Melville.Ski:ASemantics-KnowledgeableInterface.Proceedingsofthe10th Int'lConferenceonVeryLargeDataBases,Singapore,pp.30-33,Aug MaierD.,D.Rozenshtein,andD.S.Warren.WindowFunctions.AdvancesInComputing Research,3: , MaierD.,andJ.D.Ullman.MaximalObjectsandtheSemanticsofUniversalRelation Databases.ACMTransactionsonDatabaseSystems,1(8):1-14, MassariA.AnIconBasedQuerySystemforRadiologicalData.Ph.D.Thesis,Dipartimento diinformaticaesistemisticauniversita'diroma"lasapienza",nov MassariA.,andP.K.Chrysanthis.VisualQueryofCompletelyEncapsulatedObjects. Proceedingsofthe5thInt'lWorkshoponResearchIssuesinDataEngineering-Distributed ObjectManagement,Taipei,Taiwan,18-25,Mar MassariA.,S.Pavani,andL.Saladini.QBI:AnIconicQuerySystemforInexpertUsers. ProceedingsoftheWorkshoponAdvancedVisualInterfaces,Bari,Italy,pp ,June MotroA.,A.D'Atri,andL.Tarantino.KIVIEW:TheDesignofanObjectOrientedBrowser. Proceedingsofthe2ndConferenceonExpertDatabaseSystems,Virginia,pp , SockutG.H.,L.M.Burns,A.Malhotra,andK.Y.Whang.GRAQULA:AGraphicalQuery LanguageforEntity-RelationshiporRelationalDatabases.ResearchReportRC16877,IBM T.J.WatsonResearchCenter,YorktownHeights,NY,Mar TonomuraY.,andS.Abe.ContentOrientedVisualInterfacesUsingVideoIconsforVisual DatabaseSystems.ProceedingsoftheIEEEWorkshoponVisualLanguages,Roma,Italy, pp.68-73, TsudaK.,M.Hirakawa,M.Tanaka,andT.Ichikawa.IconicBrowser:AnIconicRetrieval SystemforObject-Orienteddatabases.JournalofVisuallanguagesandComputing,1(1):59-76, UllmanJ.D..TheU.R.StrikesBack.ProceedingsoftheACMPrinciplesofDatabase Systems,LosAngeles,California,pp.10-22, WeissmanS..ChangingQuerybyIconstoImproveQueryingProcessingforMobileUsers. M.S.Project,UniversityofPittsburgh,May WhangK.Y.,A.Malhotra,G.H.Sockut,L.M.Burns,andK.S.Choi.Two-Dimensional SpecicationofUniversalQuanticationinaGraphicalDatabaseQueryLanguage.TransactionsonSoftwareEngineering,18(3): ,Mar ZhangZ.,andA.O.Mendelzon.AGraphicalQueryLanguageforEntityRelationship Databases.AnEntity-RelationshipApproachtoSoftwareEngineering.DavisC.,S.Jajodia, P.Ann-BengNG,andR.T.YehEds.,NorthHolland,pp , ZloofM.M.QuerybyExample.ProceedingsoftheNationalComput.Conference,pp ,1975.