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大数据环境下的分布式数据流实时处理技术研究（英文）.docx

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大数据环境下的分布式数据流实时处理技术研究（英文） Title:ResearchonReal-timeProcessingTechniquesforDistributedDataStreamingintheBigDataEnvironment Abstract: Theadventofbigdatahasgivenrisetonewchallengesinprocessingvastamountsofdatainreal-time.Thisresearchfocusesonexploringthetechniquesforreal-timeprocessingofdistributeddatastreamsinthebigdataenvironment.Thepaperdiscussestheimportanceofreal-timeprocessingandtheimpactofbigdataontraditionalbatchprocessing.Varioustechniquesfordistributingdatastreams,processingframeworks,andalgorithmsareanalyzed.Theobjectiveistoidentifythemostefficientandscalabletechniquesthatcanbeappliedtohandlereal-timescenariosinthebigdataenvironment. 1.Introduction: 1.1Background: Withtheexponentialgrowthofdatasizeinrecentyears,traditionaldataprocessingmethodsarenolongersufficienttohandlethemassiveinfluxofdata.Real-timeprocessingofdatastreamshasbecomeacriticalrequirementformanyapplicationssuchassocialmediaanalytics,financialfrauddetection,IoTdataanalysis,etc.Traditionalbatchprocessingmethodsfallshortinaddressingthesereal-timedemands. 1.2Objective: Theobjectiveofthisresearchistoexplorethevarioustechniquesandframeworksavailableforreal-timeprocessingofdistributeddatastreamsinthebigdataenvironment.Thescalability,efficiency,andfault-toleranceofthesetechniquesareevaluatedtoidentifythemostsuitableapproaches. 2.Real-timeProcessingChallengesinBigDataEnvironment: 2.1VolumeandVelocity: Thebigdataenvironmentischaracterizedbymassivevolumesofdatabeinggeneratedathighvelocities.Real-timeprocessingtechniquesshouldbecapableofhandlingthisdatadelugeefficiently. 2.2Variety: Datastreamingapplicationsdealwithdiversedatatypessuchasstructured,semi-structured,andunstructureddata.Techniquesforhandlingthisvarietyofdataneedtobeconsidered. 2.3ScalabilityandFault-tolerance: Real-timeprocessingshouldscalehorizontallytohandleincreasingdatavolumesandaccommodatehigherprocessingloads.Fault-tolerantmechanismsareessentialtoensureuninterruptedprocessing. 3.DistributedDataStreamingTechniques: 3.1DataPartitioning: