a bibliography of the personal computer electronic resource pdf

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... ports on the Atlantic and North Sea coasts. Historians have debated the economicimpact of those large towns, particularly of capitals. Braudel has describedthem as parasites that lived at the expense ... densities—especially the overcrowding of towns, the lack of hygiene, and the proliferation of rats and fleas—made contagion easier.60Historians have no basis to assert that a Malthusian crisis as cata-strophic ... were many andsharp, at each secular peak and at each secular trough population andproduction (and the capital-labor ratio) were higher than last time. The institutional basis of modern capitalism...

... bones release calcium. Lung cancer that has spread to bone can also cause an increased release of calcium from the site of the tumor. Hypercalcemia occurs when the amount of calcium in the blood ... acetaminophen, aspirin, and ibuprofen used to treat pain and inflammation. Metastasis (plural: metastases, verb: metastasize): The spread of cancer cells throughout the body. The cancer cells that have ... have spread to other parts of the body are the same as those in the original tumor. Radiation therapy: Treatment with radiation to kill cancer cells. Steroids: A natural or synthetic compound...

... losses of valuable scarce resources and lead to significantenvironmental damage.Though the recycling of WEEE is already anticipated as anincreasing problem of transnational and partly global dimensions,only ... e-waste management in Africa, Asia and Latin America. Before that he worked in the private sector in the field of environmental and general business consultancy atnational and international levels ... concentrationsmonitored in the urban areas of Hong Kong and Guangzhou (placeswhich already have higher levels of these substances than other urbanand rural areas around the world, Deng et al.,...

... Transportation Library, the National Transportation Atlas Database, international data, and aviation and motor carrierinformation.56061trb024_029 1/13/04 6:06 AM Page 12Measuring Personal Travel ... Movement A Review of the Bureau of Transportation Statistics’ SurveysTRANSPORTATION RESEARCH BOARDSPECIALREPORT 277NATIONAL RESEARCH COUNCIL OF THE NATIONAL ACADEMIESMeasuring Personal Travel and ... dissemination of data• Cooperation with data users• Fair treatment of data providers• Commitment to quality and professional standards of practice• Active research program• Professional advancement...

... and“where.” The Do-specialistreplaces the predicateDO(from the verb“do”)with a morespecificverbchosenfromthoseacquiredfor a domain.Although“do”doesnotappearas the mainverbveryoftenin the databasequerytask, the translatorsdeduceitsimpliedpresenceinsomequeries—forinstanceinsuchcomparativequestionsas“WhatcountriescovermoreareathanPeruLdoes~?”. The comparativespecialistexamines the twoarguments of a comparisontodeterminewhether the comparisontobemadeisbetweentwoattributevalues(e.g.,Jack’sheightandsevenfeet)orbetweenanentityandsomevalue(e.g.,Jackandsevenfeet).In the lattercase,TEAMtriestoidentify the appropriateattribute of the entity(e.g.,Jack’sheight).2.3.4DatabaseSchema The translationfromlogicalformtoSODAqueryrequiresknowing the exactstructure of the targetdatabaseand the mannerinwhich the predicatesappearingin the logicalformareassociatedwith the relationsin the database.Thisinformationisprovidedby the databaseschema,whichincludes the followinginformation8:•Definition of sortsinterms of databaserelations(subject)orfields(andfieldvalueforsortsderivedfromfeaturefields). 8The schematranslatoralsousescertaininformationin the conceptualschema,includingtaxonomicinformationin the sorthierarchyanddelineationinformationassociatedwithnonsortpredicates.—18—-‘IrisenuIIORLDCBCITYCONTieldP1~nuCITY—COUNTRYBCITY—NRMEBCITY—POPCONT—ARERONT-HEMICONT—NRPIECONT—POPPEAK-COUNTRYERK-HEIGHTPEAK—MAPlEPEAK-VOLWURLOC-RRERIORLDC-CRPITRLWORLOC—COtITIIIEIITUORLDC—TIRMEWORLDC—POPordPlenuRER(n)CAPITAL(n)CITY(n)ONTINENT(n)COUNTRY(o)HEIGHT(n)EPII(n)HEMISPHERE(n)HIGH(edj)ARGE(adj)LOW(edj)N(n)RME(n)MORTIIEN(edj)PERK(n)OP(n)POPULATION(n)POPULOUS(sdj)(n)SHORT(edj)SMALL(adj)uestjonRnswerjn9Area4e~dPERK-HEIGHT1~partoranACTUALrs)ation.Typs of 11.14-SYMOOUC A~ 1)~TICFEATUREeluntyp.DATES~Ait~SCOUNTSAuthaunitsImpfcit?YESNOMarImplicitunit—FOOTI000ursty~ of thisunit-TIMEWEIONTSPEEDVOLUMEI3 ~A~ AMAWORTHTCt,WERATUREOTHERAbbr.vI.donforthisunit?—FTConv.r,lonformulafromMETERStoFEET-(IK0.3048)Conv.rilonfonoulafromFEETtoMETERS-K0.3040)‘ositly.edjactivu—HIGHTAb.Nagetivaodiscdvsa-SHORTLOWFigure4: The AcquisitionMenu•List of convenientidentifyingfieldsforeachsortcorrespondingto a filesubjectorfield.•Definition of predicatesinterms of actualdatabaserelationsandattributes;thisisdoneforpredicatesderivedfrombothactualandvirtualrelations(forrelationsubjectsandattributes).•List of eachrelation’skeyfields. The databaseschemarelatesall the predicatesin the conceptualschematotheirrepresentationin a particulardatabase.Foreachpredicate, the databaseschemagenerates a logicformuladefining the predicateinterms of databaserelations.Forexample, the predicateWORLDC-CAPITAL -OF hasasitsassociateddatabaseschema a formularepresenting the factthatitsfirstargumentistakenfrom the WORLDC-CAPITALfield of a tuple of the WORLDCrelation,andthatitssecondargumentcomesfrom the WORLDC-NAMEfield of the samerelation.If a predicatehasmultipledelineations—i.e.,ifitappliestodifferentsorts of arguments(e.g., a HEMISPHERE -OF predicatecouldapplytobothCOUNTRIESandCONTINENTS) the schemawillinclude a separatedefinitionforeachset of arguments.Insomecases(e.g.,predicatesresultingfrom the acquisition of someverbsandadjectives), the mappingassociatedwith a predicateindicatesthatitisequivalenttoanotherconceptualschema]predicatewithcertainargumentssettofixedvalues.2.4Acquisition The acquisitioncomponent of TEAMiscrucialtoitssuccessas a transportablesystem.RecallthatoneconstraintonTEAMisthat the DBEnotberequiredtohaveanyknowledge of TEAM’sinternalworkings,norabout the intricacies of the grammar,nor of computationallinguisticsingeneral.Yetdetailedinformation,oftennecessarilylinguisticinitsorientation,mustsomehowbeextractedfrom-~desirablethat the acquisitioncomponentbedesignedtoallow a DBEtochangeanswerstoquestionsand ... doesnotneedtoknowhowTEAMworksoranyspeciallanguage-processingterminology. The question-answeringsystemconsists of twomajorcomponents:(1) the DIALOGICsystemGrosS2]formappingnatural-languageexpressionsontoformallogicalrepresentations of theirmeanings;(2) a schematranslatorthattransformstheserepresentationsintostatements of a databasequerylanguage.DIALOGICand the schematranslatorrequirebothdomain-specificanddomain-independentinformation. The requisitedomain-independentinformationispart of the coreTEAMsystem; the domain-specificinformationisobtainedby the acquisitioncomponent.1.3 A SampleDatabaseWewilluse the databaseshownschematicallyinFigureitohelpillustratevariousaspects of TEAM.Thisdatabasecomprisesfourfiles(or,relations) of geographicdata. The firstfile,WORLDC,hasfivefields—NAME,CONTINENT,CAPITAL,AREAandPOP;respectively,theyspecify the continent,capital,area,andpopulationforeachcountryin the world.Variousmountainsin the worldarerepresentedin the secondfile,namedPEAK,alongwiththeircountry,height,andanindicationastowhethertheyarevolcanic. The thirdfile,namedCONT,shows the hemisphere,area,andpopulation of the continents. The fourthfile,BCITY,contains the countryandpopulation of some of the largercities of the world.Becauseseveralfilesmayhavefieldswith the samenames,TEAMprefixesfilenamestofieldnamestoformuniqueidentifiers(e.g.,WORLDC-NAME,PEAK-NAME,CONT-POP,BCITY-POP);wewilldolikewiseinourdiscussion.TEAMdistinguishesamongthreedifferentkinds of fields:feature,arithmetic,andsymbolic.Featurefieldscontaintrue/falsevaluesindicatingwhetherornotsomeattributeis a property of the filesubject.PEAK-VOLandCONT-HEMIarefeaturefields.Arithmeticfieldscontainnumericvaluesonwhichcomputations(e.g.,averaging)canbeperformedWORLDC-AREAandPEAK-HEIGHTareexamples of arithmeticfields.Letusnote,however,that a fieldcontainingsocialsecuritynumbers8TEAMcurrentlyassumes a relationaldatabasewith a numl~er of files.Nodifficultlanguage-processingproblemswouldresultfromconversiontoothermodels.BCITY—12—wouldbetreatedmorenaturallyas a symbolicfieldthanasanarithmeticfield,becauseitisunlikelythatanyarithmeticcomputationswouldbedoneonsuchnumbers.Symbolicfieldstypicallycontainvaluesthatcorrespondtonounsoradjectivesdenoting the subtypes of the domaindenotedby the field.WORLDC-NAMEandPEAK-COUNTRYareexamples.Moreinformationcanbegleanedfrom a databasethansimplywhat the individualfilescontain.Forinstance, the continentonwhich a peakislocatedcanbederivedfrom the countryinwhichitislocatedand the ... and“where.” The Do-specialistreplaces the predicateDO(from the verb“do”)with a morespecificverbchosenfromthoseacquiredfor a domain.Although“do”doesnotappearas the mainverbveryoftenin the databasequerytask, the translatorsdeduceitsimpliedpresenceinsomequeries—forinstanceinsuchcomparativequestionsas“WhatcountriescovermoreareathanPeruLdoes~?”. The comparativespecialistexamines the twoarguments of a comparisontodeterminewhether the comparisontobemadeisbetweentwoattributevalues(e.g.,Jack’sheightandsevenfeet)orbetweenanentityandsomevalue(e.g.,Jackandsevenfeet).In the lattercase,TEAMtriestoidentify the appropriateattribute of the entity(e.g.,Jack’sheight).2.3.4DatabaseSchema The translationfromlogicalformtoSODAqueryrequiresknowing the exactstructure of the targetdatabaseand the mannerinwhich the predicatesappearingin the logicalformareassociatedwith the relationsin the database.Thisinformationisprovidedby the databaseschema,whichincludes the followinginformation8:•Definition of sortsinterms of databaserelations(subject)orfields(andfieldvalueforsortsderivedfromfeaturefields). 8The schematranslatoralsousescertaininformationin the conceptualschema,includingtaxonomicinformationin the sorthierarchyanddelineationinformationassociatedwithnonsortpredicates.—18—-‘IrisenuIIORLDCBCITYCONTieldP1~nuCITY—COUNTRYBCITY—NRMEBCITY—POPCONT—ARERONT-HEMICONT—NRPIECONT—POPPEAK-COUNTRYERK-HEIGHTPEAK—MAPlEPEAK-VOLWURLOC-RRERIORLDC-CRPITRLWORLOC—COtITIIIEIITUORLDC—TIRMEWORLDC—POPordPlenuRER(n)CAPITAL(n)CITY(n)ONTINENT(n)COUNTRY(o)HEIGHT(n)EPII(n)HEMISPHERE(n)HIGH(edj)ARGE(adj)LOW(edj)N(n)RME(n)MORTIIEN(edj)PERK(n)OP(n)POPULATION(n)POPULOUS(sdj)(n)SHORT(edj)SMALL(adj)uestjonRnswerjn9Area4e~dPERK-HEIGHT1~partoranACTUALrs)ation.Typs of 11.14-SYMOOUC A~ 1)~TICFEATUREeluntyp.DATES~Ait~SCOUNTSAuthaunitsImpfcit?YESNOMarImplicitunit—FOOTI000ursty~ of thisunit-TIMEWEIONTSPEEDVOLUMEI3 ~A~ AMAWORTHTCt,WERATUREOTHERAbbr.vI.donforthisunit?—FTConv.r,lonformulafromMETERStoFEET-(IK0.3048)Conv.rilonfonoulafromFEETtoMETERS-K0.3040)‘ositly.edjactivu—HIGHTAb.Nagetivaodiscdvsa-SHORTLOWFigure4: The AcquisitionMenu•List of convenientidentifyingfieldsforeachsortcorrespondingto a filesubjectorfield.•Definition of predicatesinterms of actualdatabaserelationsandattributes;thisisdoneforpredicatesderivedfrombothactualandvirtualrelations(forrelationsubjectsandattributes).•List of eachrelation’skeyfields. The databaseschemarelatesall the predicatesin the conceptualschematotheirrepresentationin a particulardatabase.Foreachpredicate, the databaseschemagenerates a logicformuladefining the predicateinterms of databaserelations.Forexample, the predicateWORLDC-CAPITAL -OF hasasitsassociateddatabaseschema a formularepresenting the factthatitsfirstargumentistakenfrom the WORLDC-CAPITALfield of a tuple of the WORLDCrelation,andthatitssecondargumentcomesfrom the WORLDC-NAMEfield of the samerelation.If a predicatehasmultipledelineations—i.e.,ifitappliestodifferentsorts of arguments(e.g., a HEMISPHERE -OF predicatecouldapplytobothCOUNTRIESandCONTINENTS) the schemawillinclude a separatedefinitionforeachset of arguments.Insomecases(e.g.,predicatesresultingfrom the acquisition of someverbsandadjectives), the mappingassociatedwith a predicateindicatesthatitisequivalenttoanotherconceptualschema]predicatewithcertainargumentssettofixedvalues.2.4Acquisition The acquisitioncomponent of TEAMiscrucialtoitssuccessas a transportablesystem.RecallthatoneconstraintonTEAMisthat the DBEnotberequiredtohaveanyknowledge of TEAM’sinternalworkings,norabout the intricacies of the grammar,nor of computationallinguisticsingeneral.Yetdetailedinformation,oftennecessarilylinguisticinitsorientation,mustsomehowbeextractedfrom-~desirablethat the acquisitioncomponentbedesignedtoallow a DBEtochangeanswerstoquestionsand...

... )clusteredonTID.Clusteringisbasedon a hashedortreestructuredorganization. A selectionindexonattribute A of relationRis a baserelationF (A, TID)clusteredon A. LetR1andR2betworelations,notnecessarilydistinct,andletTID1andTID2beidentifiers of tuples of R1and A2 ,respectively. A joinindexonrelationsR1and A2 is a relation of couples(TID1,TID2),whereeachcoupleindicatestwotuplesmatching a joinpredicate.Intuitively, a joinindexisanabstraction of the join of tworelations. A joinindexcanbeimplementedbytwobaserelationsF(TID1,TID2),oneclusteredonTID1and the otheronTID2.Joinindicesareuniquelydesignedtooptimizejoins. The joinpredicateassociatedwith a joinindexmaybequitegeneralandincludeseveralattributes of bothrelations.Furthermore,morethanonejoinindexcanbedefinedbetweenanytworelations. The identification of variousjoinindicesbetweentworelationsisbasedon the associatedjoinpredicate.Thus, the join of relations A1 andR2on the predicate(R1 .A =R2 .A andR1.B=R2.B)canbecapturedaseither a singlejoinindex,on the multi—attributejoinpredicate,ortwojoinindices,oneon(R1 .A =R2 .A) and the otheron(R1.BR2.B). The choicebetween the alternativesis a databasedesigndecisionbasedonjoinfrequencies,updateoverhead,etc.Letusconsider the followingrelationaldatabaseschema(keyattributesarebold):11CUSTOMER(cname,city,age,job)ORDER(cname,pname,qty,date)PART(pname,weight,price,spname) A (partial)physicalschemaforthisdatabase,basedon the storagemodeldescribedabove,is(clusteredattributesarebold)C_PC(CID,cname,city,age,job)City_IND(city,CID)Age_IND(age,CID)0_PC(OlD,cname,pname,qty,date)CnamelND(cname,OlD)CIDJI(CID,OlD)OID_Jl(OlD,CID)C_PCand0_PCareprimarycopies of CUSTOMERandORDERrelations.City_INDandAge_INDareselectionindicesonCUSTOMER.CnamelNDis a selectionindexonORDER.CIDJIandOlDJIarejoinindicesbetweenCUSTOMERandORDERfor the joinpredicate(CUSTOMER.Cname=ORDER.Cname).3.Optimization of Non—RecursiveQueries- The objective of queryoptimizationistoselectanaccessplanforaninputquerythatoptimizes a givencostfunction.Thiscostfunctiontypicallyreferstomachineresourcessuchasdiskaccesses,CPUtime,andpossiblycommunicationtime(for a distributeddatabasesystem). The queryoptimizerisincharge of decisionsregarding the ordering of databaseoperations,and the choice of the accesspathsto the data, the algorithmsforperformingdatabaseoperations,and the intermediaterelationstobematerialized.Thesedecisionsareundertakenbasedon the physicaldatabaseschemaandrelatedstatistics. A set of decisionsthatleadtoanexecutionplancanbecapturedby a processingtreeKrishnamurthy86]. A processingtree(PT)is a treeinwhich a leafis a baserelationand a non—leafnodeisanintermediaterelationmaterializedbyapplyinganinternaldatabaseoperation.Internaldatabaseoperationsimplementefficientlyrelationalalgebraoperationsusingspecificaccesspathsandalgorithms.Examples of internaldatabaseoperationsareexact—matchselect,sort—mergejoin,n—arypipelinedjoin,semi—join,etc. The application of algebraictransformationrulesJarke84]permitsgeneration of manycandidatePT’sfor a singlequery. The optimizationproblemcanbeformulatedasfinding the PT of minimalcostamongallequivalentPT’s.TraditionalqueryoptimizationalgorithmsSelinger79]performanexhaustivesearch of the solutionspace,definedas the set of allequivalentPT’s,for a givenquery. The estimation of the cost of a PTisobtainedbycomputing the sum of the costs of the individualinternaldatabaseoperationsin the PT. The cost of aninternaloperationisitself a monotonicfunction of the operandcardinalities.If the operandrelationsareintermediaterelationsthentheircardinalitiesmustalsobeestimated.Therefore,foreachoperationin the PT,twonumbersmustbepredicted:(1) the individualcost of the operationand(2) the cardinality of itsresultbasedon the selectivity of the conditionsSelinger79,Piatetsky84]. The possiblePT’sforexecutinganSPJqueryareessentiallygeneratedbypermutation of the joinordering.Withnrelations,therearen!possiblepermutations. The complexity of exhaustivesearchisthereforeprohibitivewhennislarge(e.g.,n>10). The use of dynamicprogrammingandheuristics,asinSelinger79],reducesthiscomplexityto2~,whichisstillsignificant.Tohandle the case of complexqueriesinvolving a largenumber of relations, the optimizationalgorithmmustbemoreefficient. The complexity of the optimizationalgorithmcanbefurtherreducedbyimposingrestrictionson the class of 12PT’sIbaraki84),limiting the generality of the costfunctionKrishnamurthy86),orusing a probabilistichill—climbingalgorithmloannidis87].Assumingthat the solutionspaceissearchedbyanefficientalgorithm,wenowillustrate the possiblePT’sthatcanbeproducedbasedon the storagemodelwithjoinindices. The addition of joinindicesin the storagemodelenlarges the solutionspaceforoptimization.Joinindicesshouldbeconsideredby the queryoptimizerasanyotherjoinmethod,andusedonlywhentheyleadto the optimalPT.InValduriez87],wegive a precisespecification of the joinalgorithmusingjoinindex,denotedbyJOINJI,anditscost.ThisalgorithmtakesasinputtwobaserelationsR1(TID1, A1 ,B1, ... of commonsubexpressioneliminationGM82],whichappearsparticularlyusefulwhenflatteningoccurs. A simpletechniqueusing a hill—climbingmethodiseasytosuperimposeon the proposedstrategy,butmoreambitioustechniqueprovide a topicforfutureresearch.Further,anextrapolation of commonsubexpressioninlogicqueriescanbeseenin the followingexample:letbothgoalsP (a, b,X)andP (a, Y,c)occurin a query.ThenitisconceivablethatcomputingP (a, Y,X)onceandrestricting the resultforeach of the casesmaybemoreefficient.Acknowledgments:WearegratefultoShamimNaqviforinspiringdiscussionsduring the development of anearlierversion of thispaper.References:AU79]Aho, A. andJ.Uliman,Universality of DataRetrievalLanguages,Proc.POPLCon!.,SanAntonio,TX,1979.B40]Birkhoff,G.,“LatticeTheory”,AmericanMathematicalSociety,1940.BMSU8S]Bancilhon,F.,D,Maier,Y.SagivandUliman,MagicSetsandotherStrangeWaystoImplementsLogicPrograms,Proc.5—thACMSIGMOD—SIGACTSymposiumonPrinciples of DatabaseSystems,pp.1—16,1986.BR86]Bancilhon,F.,andR.Ramakrishan,AnAmateur’sIntroductiontoRecursiveQueryProcessingStrategies,Proc.1986ACM—SIGMQDIntl.Conf.onMgt. of Data,pp.16—52,1986.D82]Daniels,D.,et.al.,“AnIntroductiontoDistributedQueryCompilationin~Proc. of SecondInternationalConf,onDistriutedDatabases,Berlin,Sept.1982.GM82]Grant,J.andMinkerJ.,OnOptimizing the Evaluation of a Set of Expressions,mt.Journal of Computer andInformationScience,11,3(1982),179—189.1W87]loannidis,Y.E,Wong,E,QueryOptimizationbySimulatedAnnealing,SIGMOD87,SanFrancisco.KBZ86]Krishnamurthy,R.,Boral,H.,Zaniolo,C.Optimization of NonrecursiveQueries,Proc. of 12thVLDB,Kyoto,Japan,1986.KRS87]Krishnamurthy,R,Ramakrishnan,R,Shmueli,0.,“TestingforSafetyandEffectiveComputability”,ManuscriptinPreparation.KT811Kellog,C.,andTravis,L.Reasoningwithdatain a deductivelyaugmenteddatabasesystem,inAdvancesinDatabaseTheory:Vol1,H.Gallaire,J.Minker,andJ.Nicholaseds.,PlenumPress,NewYork,1981,pp261—298.Lb84]Lloyd,J.W.,Foundations of LogicProgramming,SpringerVerlag,1984.M84]Maier,D., The Theory of RelationalDatabases,(pp.542—553),Comp.SciencePress,1984.Na86]Naish,L.,NegationandControlinPrologJournal of LogicProgramming,toappear.Sel79]Sellinger,P.G.et.al.AccessPathSelectionin a RelationalDatabaseManagementSystem.,Proc.1979ACM—SIGMODIntl.Conf.onMgt. of Data,pp.23—34,1979.5Z86]Sacca’,D.andC.Zaniolo, The GeneralizedCountingMethodforRecursiveLogicQueries,Proc.ICDT‘86——mt.Conf.onDatabaseTheory,Rome,Italy,1986.TZ86]Tsur,S.andC.Zaniobo,LDL: A Logic—BasedDataLanguage,Proc. of 12thVLDB,Kyoto,Japan,1986.U85]Ullman,J.D.,Implementation of logicalquerylanguagesfordatabases,TODS,10,3,(1985),289—321.UV85]Ullman,J.D.and A. VanGelder,TestingApplicability of Top—DownCaptureRules,StanfordUniv.ReportSTAN—CS—85—146,1985.V86]Viflarreal,M.,“Evaluation of anO(N**2)MethodforQueryOptimization”,MSThesis,Dept. of Computer Science,Univ. of TexasatAustin,Austin,TX.Z85]Zaniolo,C. The representationanddeductiveretrieval of complexobjects,Proc. of 11thVLDB,pp.458—469,1985.Z86]Zaniolo,C.,SafetyandCompilation of Non—RecursiveHornClauses,Proc.Firstmt.Con!.onExpertDatabaseSystems,Charleston,S.C.,1986.3OPTIMIZATION OF COMPLEXDATABASEQUERIESUSINGJOININDICESPatrickValduriezMicroelectronicsand Computer TechnologyCorporation3500WestBalconesCenterDriveAustin,Texas78759ABSTRACTNewapplicationareas of databasesystemsrequireefficientsupport of complexqueries.Suchqueriestypicallyinvolve a largenumber of relationsandmayberecursive.Therefore,theytendtouse the joinoperatormoreextensively. A joinindexis a simpledatastructurethatcanimprovesignificantly the performance of joinswhenincorporatedin the databasesystemstoragemodel.Thus,asanyotheraccessmethod,itshouldbeconsideredasanalternativejoinmethodby the queryoptimizer.Inthispaper,weelaborateon the use of joinindicesfor the optimization of bothnon—recursiveandrecursivequeries.Inparticular,weshowthat the incorporation of joinindicesin the storagemodelenlarges the solutionspacesearchedby the queryoptimizerandthusoffersadditionalopportunitiesforincreasingperformance.1.IntroductionRelationaldatabasetechnologycanwellbeextendedtosupportnewapplicationareas,suchasdeductivedatabasesystemsGallaire84].Comparedto the traditionalapplications of relationaldatabasesystems,theseapplicationsrequire the support of morecomplexqueries.Thosequeriesgenerallyinvolve a largenumber of relationsandmayberecursive.Therefore, the quality of the queryoptimizationmodule(queryoptimizer)becomes a keyissueto the success of databasesystems. The idealgoal of a queryoptimizeristoselect the optimalaccessplanto the relevantdataforaninputquery.Most of the workontraditionalqueryoptimizationJarke84]hasconcentratedonselect—project—join(SPJ)queries,fortheyare the mostfrequentonesintraditionaldataprocessing(business)applications.Furthermore,emphasishasbeengivento the optimization of joinsIbaraki84]becausejoinremains the mostcostlyoperator.Whencomplexqueriesareconsidered, the joinoperatorisusedevenmoreextensivelyforbothnon—recursivequeriesKrishnamurthy86]andrecursivequeriesValduriez8 6a] .InValduriez87],weproposed a simpledatastructure,called a joinindex,thatimprovessignificantly the performance of joins.Inthispaper,weelaborateon the use of joinindicesin the context of non—recursiveandrecursivequeries.Weview a joinindexasanalternativejoinmethodthatshouldbeconsideredby the queryoptimizerasanyotheraccessmethod.Ingeneral, a queryoptimizermaps a queryexpressedonconceptualrelationsintoanaccessplan,i.e., a low—levelprogramexpressedon the physicalschema. The physicalschemaitselfisbasedon the storagemodel, the set of datastructuresavailablein the databasesystem. The incorporation of joinindicesin the storagemodelenlarges the solutionspacesearchedby the queryoptimizer,andthusoffersadditionalopportunitiesforincreasingperformance.10Joinindicescouldbeusedinmanydifferentstoragemodels.However,inordertosimplifyourdiscussionregardingqueryoptimization,wepresent the integration of joinindicesin a simplestoragemodelwithsingleattributeclusteringandselectionindices.Thenweillustrate the impact of the storagemodelwithjoinindiceson the optimization of non—recursivequeries,assumedtobeSPJqueries.Inparticular,efficientaccessplans,where the mostcomplex(andcostly)part of the querycanbeperformedthroughindices,canbegeneratedby the queryoptimizer.Finally,weillustrate the use of joinindicesin the optimization of recursivequeries,where a recursivequeryismappedinto a program of relationalalgebraenrichedwith a transitiveclosureoperator.2.StorageModelwithJoinIndices The storagemodelprescribes the storagestructuresandrelatedalgorithmsthataresupportedby the databasesystemtomap the conceptualschemainto the physicalschema.In a relationalsystemimplementedon a disk—basedarchitecture,conceptualrelationscanbemappedintobaserelationson the basis of twofunctions,partitioningandreplicating.All the tuples of a baserelationareclusteredbasedon the value of oneattribute.Weassumethateachconceptualtupleisassigned a surrogatefortupleidentity,called a TID(tupleidentifier). A TIDis a valueuniqueforalltuples of a relation.Itiscreatedby the systemwhen a tupleisinstantiated.TID’spermitefficientupdatesandreorganizations of baserelations,sincereferencesdonotinvolvephysicalpointers. The partitioningfunctionmaps a relationintooneormorebaserelations,where a baserelationcorrespondsto a TIDtogetherwithanattribute,severalattributes,orall the conceptualrelation’sattributes. The rationalefor a partitioningfunctionis the optimization of projection,bystoringtogetherattributeswithhighaffinity,i.e.,frequentlyaccessedtogether. The replicatingfunctionreplicatesoneormoreattributesassociatedwith the TID of the relationintooneormorebaserelations. The primaryuse of replicatedattributesisforoptimizingselectionsbasedonthoseattributes.Anotheruseisforincreasedreliabilityprovidedbythoseadditionaldatacopies.inthispaper,weassume a simplestoragemodel ... usedtodeflect the readingbeamveryfast.As a result,itismuchfastertoretrieveinformationfromtracksthatarelocatednear the currentlocation of the readinghead.Wecallthis a spanaccesscapability. The spanaccesscapability of opticaldiskshasimplicationsforschedulingalgorithmsanddatastructuresthatareappropriateforopticaldisks,aswellassignificantimpactonretrievalperformanceChristodoulakis8 7a] .InChristodoulakis87]wealsoderiveexactanalyticcostestimatesaswellasapproximationsthatarecheapertoevaluate,for the retrieval of recordsandlongerobjectssuchastext,images,voice,anddocuments(possiblycrossingblockboundaries)fromCAVopticaldisks.Theseestimatesmaybeusedbyqueryoptimizers of traditionalormultimediadatabases.RetrievalPerformance of CLVOpticalDisksConstantLinearVelocity(CLV)opticaldiskshavedifferentcharacteristicsthan the CAVopticaldisks.CLVopticaldisksvary the rotationalspeedsothat the unitlength of the trackwhichisreadpassesunder the readingmechanisminconstanttime,whichisindependent of the location of the track.Thishasimplicationson the rotationaldelaycostwhich,inCLVdisks,dependson the tracklocation.Thisalsoimpliesthat,inCLVdisks, the number of sectorspertrackvaries(outsidetrackshavemoresectors). The latter(variablecapacity of a track)hasmanyfundamentalimplicationsonselection of datastructuresthataredesirableforCLVopticaldisksand the parameters of theirimplementation,for the selection of accesspathstobesupportedfordatabasesstoredonCLVdisks,aswellasfor the retrievalperformanceand the optimalqueryprocessingstrategytobechosen.(TheseimplicationsarestudiedindetailinChristodoulakis87b],inwhichisshownthatthesedecisionsdependon the location of dataplacementon the disk.)Analyticcostestimatesfor the performance of retrieval of recordsandobjectsfromCLVdisksarealsoderivedinChristodoulakis87b]).Theseestimatesmaybeusedbytraditionalormultimediaqueryoptimizers.Itisshownthat the optimalqueryprocessingstrategydependson the location of fileson the CLVdisk.Thisimpliesthatqueryoptimizersmayhavetomaintaininformationabout the location of fileson the disk.Estimation of SelectivitiesinTextInmultimediainformationsystemsmuch of the contentspecificationwillbedonebyspecifying a pattern of textwords.Queriesbasedon the content of imagesaredifficulttospecify,andimageaccessmethodsareveryexpensive.Voicecontentistransformedtotextcontentif a goodvoicerecognition18deviceisavailable.Thusaccurateestimation of textselectivitiesisimportantinqueryoptimizationinmultimediaobjects.Thereisanotherimportantreasonwhyaccurateestimation of textselectivitiesisimportant.Frequently the userwantstohave a fastfeedback of howmanyobjectsqualifyinhisquery.Iftoomanyobjectsquality, the usermaywanttorestrict the set of qualifyingobjectsbyaddingmoreconjunctiveterms.Iftoofewobjectsqualify, the usermaywanttoincrease the number of objectsthathereceivesbyaddingmoredisjunctiveterms.(Tradeoffs of precisionversusrecallareextensivelydescribedin the informationretrieval bibliography. )Althoughsuchstatisticsmaybefoundbytraversinganindexontext(possiblyseveraltimesforcomplicatedqueries)indexesmaynotbe the desirabletextaccessmethodsinseveralenvironmentsHaskin81].Given a set of stopwords(wordsthatappeartoofrequentlyinEnglishtobe of a practicalvalueincontentaddressibility),itiseasytogiveananalyticformulathatcalculates the averagenumber of wordsthatqualifyin a textqueryChristodoulakisandNg87].Thisanalyticformulauses the factthat the distribution of wordsin a longpiece of textisZipfwithknownparameters.However, the averagenumber of documentsmaynotbe a goodenoughestimate(insomecases)forqueryoptimizationorforgivinganestimate of the size of the responseto the userChristodoulakis84].Moredetailedestimateswillhavetoconsiderselectivities of individualwordsandqueries.Thiscanbedoneusingsampling. A samplingstrategylooksatsomeblocks of text,counts the number of occurrences of a particularwordortextpattern,andbasedonthisextrapolates the probabilitydistribution of the number of patternoccurrencesto the wholedatabase. A potentialproblemwiththisapproachisthatinordertobeconfidentabout the statistics a largeportion of the filemayhavetobescanned.Instead of blocks of the actualtextfile,blocks of the textsignaturescouldbeusedwhensignaturesareusedastextaccessmethods.Sincemoreinformationexistsinblocks of signaturesthaninblocks of the...

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... as a castawy on a desert island for more than 28 years. To the end of the novel, Defoe repainted a true merchant Robinson. The hero started a new trading voyage. He had now a vast capital of ... He taught Friday English but did learn any of Friday’s language. Crusoe didn’t point to a goat and say: “this is a goat” and then signal to Friday to say what it was called in his language. ... the mastery of the others was a very unattractive trait that Crusoe displayed. The novel shows that mastery of one’s own life is a praiseworthy achievement, but that mastery of another’s is a...