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1、中國科學(xué)院計算技術(shù)研究所博士學(xué)位論文科學(xué)數(shù)據(jù)網(wǎng)格中數(shù)據(jù)挖掘技術(shù)研究姓名：佟強(qiáng)申請學(xué)位級別：博士專業(yè)：計算機(jī)系統(tǒng)結(jié)構(gòu)指導(dǎo)教師：閻保平20060601ResearchonDataMiningintheScientificDataGridQiangTong(ComputerArchitecture)DirectedByBaopingYanWiththeemergenceanddevelopmentof面dcomputing，itbecomespossibletosharedataandcollaborateinalargescalemodelofcross-organiz
2、ationandcross-legion．Intheareaofscientificresearch，theproblemofmodernscientificresearchbecomesmoleandmorecomplex，whichresultsinabrand·newscientificcollaborationmodelandthelargescienceproject，i．e．，theinfomationizationofscientificresearch(e-Science)．Inordertoshareresourcesandproducts，and
3、alsocollaboratetoaccomplishlargescalemodemscientificresearches，itisnecessarytoestablishallalliedvirtualresearchgroupviatheIntemetbasedon卯dcomputing．Byusingdataminingtechnologies，thispaperaimstoimprovetheserviceleveloftheScientificDataGridandtheScientificDatabase，basedontheirexistinglar
4、ge—scaledatastorageandpowerfulcomputingcapabilities．ThemainresearchcontentsandcontributionsarelistedasfoIlows．’(1)BasedondetailedanalysesofthedataminingpropertiesoftheScientificDataGrid,ascientificdataminingsystemisproposed．Thesystemconsistsofthleemaincomponents：theScientificDataMining
5、Architecture(SDMA)，theScientificDataMimngToolkit(SDMK),andtheScientificDataMiningService(SDMS)．SDMAdescribesthemulti-dimensionmodelarchitectureofdatamimngapplications；SDMKprovidesalargeamountofdatapreprocessinganddataminingalgorithms；SDMSpresentsadataminingschemetoaddresstheproblemsund
6、er畫denvironmentthroughaformof鰣dservice．Comparedwithtraditionaldataminingsystems，theproposedsystemhasmanyexcellentproperties，andismolesuitabletotheenvironmentoftheScientificDataGridandtheScientificDatabase．Nowadays，ithasbeenappliedinsomerealdatabaseapplications．Besidesthesimplequeryands
7、earchfunctions，theproposedsystemCanalsoperformmoreadvancedfunctionssuchasdatastatistic，dataanalysis，andknowledgediscovery．Asaresult,theservicelevelofthedatabaseisimproved．(2)Clusteringindataminingisadiscoveryprocesswhichgroupsasetofdatasuchthattheintra-clustersimilarityismaximizedand
