简介：数据存取延期成为了高端计算系统的突出的性能瓶颈。在系统设计减少数据存取延期的关键是减少数据货摊时间。存储器地区和并发是影响现代存储器系统的性能的二个必要因素。因为全面存储器系统性能上的存储器并发的影响很好没被理解，然而，存在在利用数据存取并发上在很少减少数据货摊时间学习焦点。在这研究，一双新奇数据货摊时间模型，为地区和并发的联合努力的L-C模型和为数据上的纯失误的效果的下午模型阻止时间，被介绍。模型提供数据存取延期的新理解并且为表演优化提供新方向。基于这些新模型，先进缓存优化的一张概括表格被介绍。当时，被数据并发贡献了，把38个条目仅仅，21个条目由数据地区作出贡献，它显示出数据并发的值。在这研究介绍的L-C和下午模型和他们的联系结果和机会为数据中央的建筑学和算法现代计算系统设计的未来重要、必要。

简介：TheInternettechnologyhasalreadychangedtheInformationSocietyinprofoundways,andwillcontinuetodoso.NowadaysmanypeopleforeseethatthereisasimilartrajectoryforthenextgenerationofInternet-GridTechnology.Asanemergingcomputationalandnetworkinginfrastructure,GridComputingisdesignedtoprovidepervasive,uniformandreliableaccesstodata,computationalandhumanresourcesdistributedinadynamic,heterogeneousenvironment.Ontheotherhand,thedevelopmentofGeographicInformationSystem(GIS)hasbeenhighlyinfluencedbytheevolutionofinformationtechnologysuchastheInternet,telecommunications,softwareandvarioustypesofcomputingtechnology.Inparticular,inthedistributedGISdomain,thedevelopmenthasmadesignificantimpactinthepastdecade.However,duetotheclosedandcentralizedlegacyofthearchitectureandthelackofinteroperability,modularity,andflexibility,currentdistributedGISstillcannotfullyaccommodatethedistributed,dynamic,heterogeneousandspeedydevelopmentinnetworkandcomputingenvironments.Hence,thedevelopmentofahighperformancedistributedGISsystemisstillachallengingtask.So,thedevelopmentofGridcomputingtechnologyundoubtedlyprovidesauniqueopportunityfordistributedGIS,andaGridComputingbasedGISparadigmbecomesinevitable.ThispaperproposesanewcomputingplatformbaseddistributedGISframework–theGridGeographicInformationSystem(G2IS).

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简介：Spatialapplicationswillgainhighcomplexityasthevolumeofspatialdataincreasesrapidly.Asuitabledataprocessingandcomputinginfrastructureforspatialapplicationsneedstobeestablished.Overthepastdecade,gridhasbecomeapowerfulcomputingenvironmentfordataintensiveandcomputingintensiveapplications.Integratinggridcomputingwithspatialdataprocessingtechnology,theauthorsdesignedaspatialdataprocessinggrid(calledSDPG)toaddresstherelatedproblems.RequirementsofspatialapplicationsareexaminedandthearchitectureofSDPGisdescribedinthispaper.KeytechnologiesforimplementingSDPGarediscussedwithemphasis.

简介：赛门铁克于近期宣布了一个绝无仅有的融合式解决方案，其可促进企业在跨异质化应用程序、数据库、服务器与存储的平台间于一致的基础架构软件上进行标准化。

简介：Dataclusteringisasignificantinformationretrievaltechniqueintoday’sdataintensivesociety.Overthelastfewdecadesavastvarietyofhugenumberofdataclusteringalgorithmshavebeendesignedandimplementedforallmostalldatatypes.Thequalityofresultsofclusteranalysismainlydependsontheclusteringalgorithmusedintheanalysis.Architectureofaversatile,lessuserdependent,dynamicandscalabledataclusteringmachineispresented.Themachineselectsforanalysis,thebestavailabledataclusteringalgorithmonthebasisofthecredentialsofthedataandpreviouslyuseddomainknowledge.Thedomainknowledgeisupdatedoncompletionofeachsessionofdataanalysis.

简介：Withmassiveamountsofdatastoredindatabases,mininginformationandknowledgeindatabaseshasbecomeanimportantissueinrecentresearch.Researchersinmanydifferentfieldshaveshowngreatinterestindateminingandknowledgediscoveryindatabases.Severalemergingapplicationsininformationprovidingservices,suchasdatawarehousingandon-lineservicesovertheInternet,alsocallforvariousdataminingandknowledgediscoverytchniquestounderstandusedbehaviorbetter,toimprovetheserviceprovided,andtoincreasethebusinessopportunities.Inresponsetosuchademand,thisarticleistoprovideacomprehensivesurveyonthedataminingandknowledgediscorverytechniquesdevelopedrecently,andintroducesomerealapplicationsystemsaswell.Inconclusion,thisarticlealsolistssomeproblemsandchallengesforfurtherresearch.

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简介：ETL(Extract-Transform-Load)通常包括三个阶段：抽取，转变，并且装载。在造数据仓库里，它起数据注射的作用并且是最费时间的活动。因此改进ETL的表演是必要的。在这份报纸，一条新ETL途径，电话(Transform-Extract-Load)被建议。电话途径使用虚拟表格在抽取舞台和装载舞台前认识到转变阶段，在存储从每迥异的来源数据系统提取的未加工的数据的数据阶段区域或阶段数据库外面。电话途径减少数据传播负担，并且从存取层改进询问的性能。试验性的结果基于我们的建议基准证明电话途径可行、实际。

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简介：Inordertohighrealityandefficiency,thetechniqueofmotioncapture(MoCap)hasbeenwidelyusedinthefieldofcomputeranimation.Withthedevelopmentofmotioncapture,alargeamountofmotioncapturedatabasesareavailableandthisissignificantforthereuseofmotiondata.Butduetothehighdegreeoffreedomsandhighcapturefrequency,thedimensionofthemotioncapturedataisusuallyveryhighandthiswillleadtoalowefficiencyindataprocessing.Sohowtoprocessthehighdimensiondataanddesignanefficientandeffectiveretrievalapproachhasbecomeachallengewhichwecan'tignore.Inthispaper,firstwelayoutsomeproblemsaboutthekeytechniquesinmotioncapturedataprocessing.Thentheexistingapproachesareanalyzedandsummarized.Atlast,somefutureworkisproposed.

简介：Thisresearchtakestheviewthatthemodellingoftemporaldataisafundamentalsteptowardsthesolutionofcapturingsemanticsoftime.Theproblemsinherentinthemodellingoftimearenotuniquetodatabaseprocessing.Therepresentationoftemporalknowledgeandtemporalreasoningarisesinawiderangeofotherdisciplines.Inthispaperanaccountisgivenofatechniqueformodellingthesemanticsoftemporaldataanditsassociatednormalizationmethod.ItdiscussesthetechniquesofprocessingtemporaldatabyemployingaTimeSequence(TS)datamodel.Itshowsanumberofdifferentstrategieswhichareusedtoclassifydifferentdatapropertiesoftemporaldata,anditgoesontodevelopthemodeloftemporaldataandaddressesissuesoftemporaldataapplicationdesignbyintroducingtheconceptoftemporaldatanormalisation.

简介：Page-basedsoftwareDSMsystemssufferfromfalsesharingcausedbythelargesharinggranularity,andonlysupportone-dimensionBlockorCyclicblockdatadistributionschemes,Thusapplicationsrunningonthemwillsufferfrompoordatalocalityandwillbeabletoexploitparallelismonlywhenusingalargenumberofprocessors,Inthispaper.awaytowardssupportingflexibledatadistribution(FDD)onsoftwareDSMsystemispresented.Smallgranularity-tunableblocks,thesizeofwhichcanbesetbycompilerorprogrammer,areusedtooverlaptheworkingdatasetsdistributedamongprocessors.TheFDDwasimplmentedonasoftwareDSMsystemcalledJIAJIA.ComparedwithBlock/Cyclic-blockdistributionschemesusedbymostDSMsystemsnow,experimentsshowthattheproposedwayofflexibledatadistributionismoreeffective.Theperformanceoftheapplicationsusedintheexperimentsissignificantlyimproved.

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简介：Thispaperpresentsanewefficientalgorithmforclusteringcategoricaldata,Squeezer,whichcanproducehighqualityclusteringresultsandatthesametimedeservegoodscalability.TheSqueezeralgorithmreadseachtupletinsequence,eitherassigningttoanexistingcluster(initiallynone),orcreatingtasanewcluster,whichisdeterminedbythesimilaritiesbetweentandclusters.Duetoitscharacteristics,theproposedalgorithmisextremelysuitableforclusteringdatastreams,wheregivenasequenceofpoints,theobjectiveistomaintainconsistentlygoodclusteringofthesequencesofar,usingasmallamountofmemoryandtime.OutlierscanalsobehandledefficientlyanddirectlyinSqueezer.Experimentalresultsonreal-lifeandsyntheticdatasetsverifythesuperiorityofSqueezer.

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