The Marketing Data Box - A QUARTERLY COLLECTION OF PRACTICAL MARKETING TOOLS READY FOR PROFESSIONAL USE doc

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... and“where.” The Do-specialistreplaces the predicateDO(from the verb“do”)with a morespecificverbchosenfromthoseacquired for a domain.Although“do”doesnotappearas the mainverbveryoftenin the databasequerytask, the translatorsdeduceitsimpliedpresenceinsomequeries for instanceinsuchcomparativequestionsas“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(andfieldvalue for sortsderivedfromfeaturefields). 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 11.1 4- SYMOOUC A~ 1)~TICFEATUREeluntyp.DATES~Ait~SCOUNTSAuthaunitsImpfcit?YESNOMarImplicitunit—FOOTI000ursty~ of thisunit - TIMEWEIONTSPEEDVOLUMEI3 ~A~ AMAWORTHTCt,WERATUREOTHERAbbr.vI.don for thisunit?—FTConv.r,lonformulafromMETERStoFEET - (IK0.3048)Conv.rilonfonoulafromFEETtoMETERS - K0.3040)‘ositly.edjactivu—HIGHTAb.Nagetivaodiscdvsa - SHORTLOWFigure4: The AcquisitionMenu•List of convenientidentifyingfields for eachsortcorrespondingto a filesubjectorfield.•Definition of predicatesinterms of actualdatabaserelationsandattributes;thisisdone for predicatesderivedfrombothactualandvirtualrelations (for relationsubjectsandattributes).•List of eachrelation’skeyfields. The databaseschemarelatesall the predicatesin the conceptualschematotheirrepresentationin a particulardatabase. For eachpredicate, the databaseschemagenerates a logicformuladefining the predicateinterms of databaserelations. For example, 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 separatedefinition for eachset 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 ... and“where.” The Do-specialistreplaces the predicateDO(from the verb“do”)with a morespecificverbchosenfromthoseacquired for a domain.Although“do”doesnotappearas the mainverbveryoftenin the databasequerytask, the translatorsdeduceitsimpliedpresenceinsomequeries for instanceinsuchcomparativequestionsas“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(andfieldvalue for sortsderivedfromfeaturefields). 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 11.1 4- SYMOOUC A~ 1)~TICFEATUREeluntyp.DATES~Ait~SCOUNTSAuthaunitsImpfcit?YESNOMarImplicitunit—FOOTI000ursty~ of thisunit - TIMEWEIONTSPEEDVOLUMEI3 ~A~ AMAWORTHTCt,WERATUREOTHERAbbr.vI.don for thisunit?—FTConv.r,lonformulafromMETERStoFEET - (IK0.3048)Conv.rilonfonoulafromFEETtoMETERS - K0.3040)‘ositly.edjactivu—HIGHTAb.Nagetivaodiscdvsa - SHORTLOWFigure4: The AcquisitionMenu•List of convenientidentifyingfields for eachsortcorrespondingto a filesubjectorfield.•Definition of predicatesinterms of actualdatabaserelationsandattributes;thisisdone for predicatesderivedfrombothactualandvirtualrelations (for relationsubjectsandattributes).•List of eachrelation’skeyfields. The databaseschemarelatesall the predicatesin the conceptualschematotheirrepresentationin a particulardatabase. For eachpredicate, the databaseschemagenerates a logicformuladefining the predicateinterms of databaserelations. For example, 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 separatedefinition for eachset 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 ... of language-processingtasksfrom the analysis of anEnglishsentenceto the generation of a databasequery. The rectangularboxesrepresent the processes,and the ovalstotheirright, the variousknowledgesources. The acquisition box on the rightpointstothoseknowledgesourcesthatareaugmentedthroughinteractionwith the DBE.AllothermodulesandknowledgesourcesarebuiltintoTEAMandremainunchangedduringacquisition.Inthissectionwewilllookat the TEAMsystemfromseveralangles.Tobegin,wewillsketch the overallflow of processingduringquestion-answering,describing the variousprocessesinvolvedintransforminganEnglishqueryinto a formaldatabasequery.Because the particularlogicalform(LF)TEAMusestoencode the meaning of a queryplays a crucialroleinmediatingbetween the wayqueriesareposedand the wayinformationisobtainedfrom the database,itaffects the design of severalcomponents of the system.Wethenlookinsomewhatmoredetailat the data structuresthatencodedomain-specificinformation.Finally,wediscuss the overallstrategyused for acquiringinformationaboutspecificdomainsanddatabases.2.1Flow of Control The flow of controlduringTEAM’stranslation of a natural-languagequeryinto a formalqueryto the databaseisillustratedas the pathon the leftside of Figure2,fromtoptobottom. The transformationtakesplaceintwomajorsteps:first, a representation of the literalmeaning of the query,orlogicalform,isconstructed;second,thislogicalformistransformedinto a databasequery. The translationintologicalformisperformedby the DIALOGICsystem,whichcomprises the following-components,shownsurrounded-by the~ dotted~ box inFigure2: the DIAMONDparser, the DIAGRAMgrammar, the lexicon,semantic-interpretationfunctions,basicpragmaticfunctions,andprocedures for determining the scope of quantifiers.Since a description of DIALOGICisprovidedelsewhereGrosS2],letusdiscusshereonlythoseaspects of the systemthatwereinfluencedby the development of TEAM.Twocentral data structuresinDIALOGICthatareaffectedbyTEAM’sacquisitionprocessaredescribed: the lexiconand—13—Figure2:TEAMSystemDiagram the conceptualschema.Tounderstand the semanticandpragmaticcomponents of TEAM,itisalsonecessarytoappreciateDIALOGIC’sseparation of semanticinterpretationoperationsintotwomainclasses:translators,whichdefinehow the interpretations of the constituents of a phrasearecombinedinto the phrase’sinterpretation;basicsemanticfunctions,whicharecalledby the translatorstoassemble the actuallogical-formfragmentsthatform the interpretations of phrases.hibrief,when the enduserasks a query,DIALOGICparses the sentence,producingoneormoretreesrepresentingpossiblesyntacticstructures. The “best”parsetree,basedon a priorisyntacticcriteria,isselectedandannotatedwithsemanticinformation(Robi82,Mart83J.Next,pragmaticanalysisisappliedtoassignspecificmeaningsthatarerelevantto the currentdomaintonoun-nouncombinationsandto“vague”predicateslikeHAVEand OF. 4Finally, the quantifier-scopedeterminationprocess,afterconsideringallpossiblealternatives,determines the bestrelativescope for the quantifiersin the query. The logicalformthusconstructed,using a set of predicatesthataremeaningfulwithrespectto the givendomainanddatabase,constitutesanunambiguousrepresentation of the Englishquery. The logicalformproducedbyDIALOGICistranslatedinto a queryin the SODAMoor79J~databasequerylanguageby the schematranslator.Inadditionto the conceptualschema, the schematranslatoruses a databaseschemathatfurnishesinformationabout the particulardatabasestructures.Thisschema,describedbrieflybelow,isalsoaffectedby the acquisitionprocess.4Weconsiderthesepredicatesvaguebecausetheycanbeappliedtomanykinds of entities;theyarereplacedby~predicatesduringpragmaticprocessing.5SODAisactually a querycompilerthattakesqueriesin a standardrelationalfonnalismandcompilesthemintooptimizedqueriesin the languages of otherdatabasemanagementsystems;bothrelationalandcodicilDBMSshavebeenaccommodated. For ourexperiments,aninterpreterthatfollowsSODAcommandstoaccess a smalldatabaseinprimarymemorywasusedin...

... of commonsubexpressioneliminationGM82],whichappearsparticularlyusefulwhenflatteningoccurs. A simpletechniqueusing a hill—climbingmethodiseasytosuperimposeon the proposedstrategy,butmoreambitioustechniqueprovide a topic for futureresearch.Further,anextrapolation of commonsubexpressioninlogicqueriescanbeseenin the followingexample:letbothgoalsP (a, b,X)andP (a, Y,c)occurin a query.ThenitisconceivablethatcomputingP (a, Y,X)onceandrestricting the result for each of the casesmaybemoreefficient.Acknowledgments:WearegratefultoShamimNaqvi for inspiringdiscussionsduring the development of anearlierversion of thispaper.References:AU79]Aho, A. andJ.Uliman,Universality of Data RetrievalLanguages,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 ComputerandInformationScience,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.,“Testing for SafetyandEffectiveComputability”,ManuscriptinPreparation.KT811Kellog,C.,andTravis,L.Reasoningwith data in 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 GeneralizedCountingMethod for RecursiveLogicQueries,Proc.ICDT‘86——mt.Conf.onDatabaseTheory,Rome,Italy,1986.TZ86]Tsur,S.andC.Zaniobo,LDL: A Logic—Based Data Language,Proc. of 12thVLDB,Kyoto,Japan,1986.U85]Ullman,J.D.,Implementation of logicalquerylanguages for databases,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)Method for QueryOptimization”,MSThesis,Dept. of ComputerScience,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 COMPLEXDATABASEQUERIESUSINGJOININDICESPatrickValduriezMicroelectronicsandComputerTechnologyCorporation3500WestBalconesCenterDriveAustin,Texas78759ABSTRACTNewapplicationareas of databasesystemsrequireefficientsupport of complexqueries.Suchqueriestypicallyinvolve a largenumber of relationsandmayberecursive.Therefore,theytendto use the joinoperatormoreextensively. A joinindexis a simple data structurethatcanimprovesignificantly the performance of joinswhenincorporatedin the databasesystemstoragemodel.Thus,asanyotheraccessmethod,itshouldbeconsideredasanalternativejoinmethodby the queryoptimizer.Inthispaper,weelaborateon the use of joinindices for the optimization of bothnon—recursiveandrecursivequeries.Inparticular,weshowthat the incorporation of joinindicesin the storagemodelenlarges the solutionspacesearchedby the queryoptimizerandthusoffersadditionalopportunities for increasingperformance.1.IntroductionRelationaldatabasetechnologycanwellbeextendedtosupportnewapplicationareas,suchasdeductivedatabasesystemsGallaire84].Comparedto the traditionalapplications of relational data basesystems,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 relevant data for aninputquery.Most of the workontraditionalqueryoptimizationJarke84]hasconcentratedonselect—project—join(SPJ)queries, for theyare the mostfrequentonesintraditional data processing(business)applications.Furthermore,emphasishasbeengivento the optimization of joinsIbaraki84]becausejoinremains the mostcostlyoperator.Whencomplexqueriesareconsidered, the joinoperatorisusedevenmoreextensively for bothnon—recursivequeriesKrishnamurthy86]andrecursivequeriesValduriez8 6a] .InValduriez87],weproposed a simple data structure,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 data structuresavailablein the databasesystem. The incorporation of joinindicesin the storagemodelenlarges the solutionspacesearchedby the queryoptimizer,andthusoffersadditionalopportunities for increasingperformance.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 surrogate for tupleidentity,called a TID(tupleidentifier). A TIDis a valueunique for alltuples 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 rationale for a partitioningfunctionis the optimization of projection,bystoringtogetherattributeswithhighaffinity,i.e.,frequentlyaccessedtogether. The replicatingfunctionreplicatesoneormoreattributesassociatedwith the TID of the relationintooneormorebaserelations. The primary use of replicatedattributesis for optimizingselectionsbasedonthoseattributes.Another use is for increasedreliabilityprovidedbythoseadditional data copies.inthispaper,weassume a simplestoragemodel ... )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)physicalschema for thisdatabase,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.CIDJIandOlDJIarejoinindicesbetweenCUSTOMERandORDER for the joinpredicate(CUSTOMER.Cname=ORDER.Cname).3.Optimization of Non—RecursiveQueries - The objective of queryoptimizationistoselectanaccessplan for aninputquerythatoptimizes 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 algorithms for performingdatabaseoperations,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.Internal data baseoperationsimplementefficientlyrelationalalgebraoperationsusingspecificaccesspathsandalgorithms.Examples of internaldatabaseoperationsareexact—matchselect,sort—mergejoin,n—arypipelinedjoin,semi—join,etc. The application of algebraictransformationrulesJarke84]permitsgeneration of manycandidatePT’s for 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, for eachoperationin the PT,twonumbersmustbepredicted:(1) the individualcost of the operationand(2) the cardinality of itsresultbasedon the selectivity of the conditionsSelinger79,Piatetsky84]. The possiblePT’s for executinganSPJqueryareessentiallygeneratedbypermutation 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 solutionspace for optimization.Joinindicesshouldbeconsideredby the queryoptimizerasanyotherjoinmethod,andusedonlywhentheyleadto the optimalPT.InValduriez87],wegive a precisespecification of the joinalgorithmusingjoinindex,denotedbyJOINJI,anditscost.ThisalgorithmtakesasinputtwobaserelationsR1(TID1, A1 ,B1, ... usedtodeflect the readingbeamveryfast.As a result,itismuchfastertoretrieveinformationfromtracksthatarelocatednear the currentlocation of the readinghead.Wecallthis a spanaccesscapability. The spanaccesscapability of opticaldiskshasimplications for schedulingalgorithmsand data structuresthatareappropriate for opticaldisks,aswellassignificantimpactonretrievalperformanceChristodoulakis8 7a] .InChristodoulakis87]wealsoderiveexactanalyticcostestimatesaswellasapproximationsthatarecheapertoevaluate, for the retrieval of recordsandlongerobjectssuchastext,images,voice,anddocuments(possiblycrossingblockboundaries)fromCAVopticaldisks.Theseestimatesmaybeusedbyqueryoptimizers of traditionalormultimedia data bases.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 data structuresthataredesirable for CLVopticaldisksand the parameters of theirimplementation, for the selection of accesspathstobesupported for data basesstoredonCLVdisks,aswellas for the retrievalperformanceand the optimalqueryprocessingstrategytobechosen.(TheseimplicationsarestudiedindetailinChristodoulakis87b],inwhichisshownthatthesedecisionsdependon the location of data placementon the disk.)Analyticcostestimates for 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 informationretrievalbibliography.)Althoughsuchstatisticsmaybefoundbytraversinganindexontext(possiblyseveraltimes for complicatedqueries)indexesmaynotbe the desirabletextaccessmethodsinseveralenvironmentsHaskin81].Given a set of stopwords(wordsthatappeartoofrequentlyinEnglishtobe of a practical valueincontentaddressibility),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) for queryoptimizationor for givinganestimate 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 whole data base. A potentialproblemwiththisapproachisthatinordertobeconfidentabout the statistics a largeportion of the filemayhavetobescanned.Instead of blocks of the actualtextfile,blocks of the textsignaturescouldbeusedwhensignaturesareusedastextaccessmethods.Sincemoreinformationexistsinblocks of signaturesthaninblocks of the...

... al., 2003)and again to try to predict these. There are specialty databasessuch as the Nucleic Acid Database (Berman et al., 1992) and the HIV Protease Structural Database (Ravichandran et al.,2002) ... inclusion intheir own in-house databases. These structural data are usedto aid the discovery of new pharmaceuticals. Indeed, the ready availability of the structure of HIV protease (Navia et al.,1989) ... While the PDB is considered an archival data resource that stores and distributes primary data, there arehundreds of derivative databases that catalog the data indifferent ways. For example, CATH...

... TV for ready- to-eat cereals than any other category of packaged food or beverage. ❑ In 2011, the average 6- to 11-year-old saw more than 700 TV ads for cereals (1.9 per day), and the average ... Blanchard K (2011). The Children’s Food & Beverage Advertising Initiative in action. Available at www.bbb.org/us/childrens-food-and-beverage-advertising-initiative/.3. Children’s Food & ... children), family cereals (marketed to parents to serve their children), and adult cereals (marketed to adults for their own consumption). We also used syndicated market research data and independent...

... process of search-ing and consequently also the character of re-sulting concept-formative clusters. We have op-timized their values by a sort of machine learn-ing, using a small manually annotated ... that we have a function, which gives a degree of document similarity for each pair of documents.Then we represent the collection as a graph.Definition: A labeled graph is called graph of collection ... ambiguity in the data, and its main advantage over hard clustering isthat it yields much more detailed informationon the structure of the data (cf. (Kaufman andRousseeuw, 1990), chapter 4).Then we...

... (CCAAACCACGTTTACTCACCTCAGCAGCCAATACCG) for KR mutation; RKDmsAF (GCTGCTGAGGTGAGTCGCAAAGGTTTGGTAAAAACG) and RKD-msAR (CGTTTTTACCAAACCTTTGCGACTCACCTCAGCAGC) for RK mutation; and KRtoKKDmsAF(GCTGAGGTGAGTAAAAAGGGTTTGGTAAAAACGACAGCG) ... KRtoKKDmsAF(GCTGAGGTGAGTAAAAAGGGTTTGGTAAAAACGACAGCG) and KRtoKKDmsAR (CGCTGTCGTTTTTACCAAACCCTTTTTACTCACCTCAGC) for KKmutation.SDS/PAGE and western blottingProteins were separated using SDS ⁄ PAGE and ... PCR_AmiA_EcoRI _for (GGCCGAATTCACCATTATGAGCACTTTTA) and PCR_Ami- A_ EcoRI_rev (GGCCGAATTCGCTGTGTCCGTTGCTGGTT) for AmiA, and PCR_MdoD_EcoRI _for (GGCCGAATTCACCATTATGGATCGTAGAC) and PCR_Mdo-D_EcoRI...

... sales area from 30 feet away, unable to hear any verbal communications, but still be aware of what is taking place at each and every table. You do not have to hear what they are saying because ... if they are at this stage of the buying process, you really have little to worry about. Don’t be Afraid to Ask for Add-On Sales Are you aware of the fact that McDonald’s increases their ... talk loses the battle! I’ve seen it time and time again. A salesperson asks for the sale, then because of a brief uncomfortable silence begins talking again. I don’t care if there is a...