文献信息：文献标题：AStudyofDataMiningwithBigData（大数据挖掘研究）国外作者：VHShastri，VSreeprada文献出处：《InternationalJournalofEmergingTrendsandTechnologyinComputerScience》,2016,38(2):99-103字数统计：英文2291单词，12196字符；中文3868汉字外文文献：AStudyofDataMiningwithBigDataAbstractDatahasbecomeanimportantpartofeveryeconomy,industry,organization,business,functionandindividual.BigDataisatermusedtoidentifylargedatasetstypicallywhosesizeislargerthanthetypicaldatabase.Bigdataintroducesuniquecomputationalandstatisticalchallenges.BigDataareatpresentexpandinginmostofthedomainsofengineeringandscience.Datamininghelpstoextractusefuldatafromthehugedatasetsduetoitsvolume,variabilityandvelocity.ThisarticlepresentsaHACEtheoremthatcharacterizesthefeaturesoftheBigDatarevolution,andproposesaBigDataprocessingmodel,fromthedataminingperspective.Keywords:BigData,DataMining,HACEtheorem,structuredandunstructured.I.IntroductionBigDatareferstoenormousamountofstructureddataandunstructureddatathatoverflowtheorganization.Ifthisdataisproperlyused,itcanleadtomeaningfulinformation.Bigdataincludesalargenumberofdatawhichrequiresalotofprocessinginrealtime.Itprovidesaroomtodiscovernewvalues,tounderstandin-depthknowledgefromhiddenvaluesandprovideaspacetomanagethedataeffectively.Adatabaseisanorganizedcollectionoflogicallyrelateddatawhichcanbeeasilymanaged,updatedandaccessed.Dataminingisaprocessdiscoveringinterestingknowledgesuchasassociations,patterns,changes,anomaliesandsignificantstructuresfromlargeamountofdatastoredinthedatabasesorotherrepositories.BigDataincludes3V’sasitscharacteristics.Theyarevolume,velocityandvariety.Volumemeanstheamountofdatageneratedeverysecond.Thedataisinstateofrest.Itisalsoknownforitsscalecharacteristics.Velocityisthespeedwithwhichthedataisgenerated.Itshouldhavehighspeeddata.Thedatageneratedfromsocialmediaisanexample.Varietymeansdifferenttypesofdatacanbetakensuchasaudio,videoordocuments.Itcanbenumerals,images,timeseries,arraysetc.DataMininganalysesthedatafromdifferentperspectivesandsummarizingitintousefulinformationthatcanbeusedforbusinesssolutionsandpredictingthefuturetrends.Datamining(DM),alsocalledKnowledgeDiscoveryinDatabases(KDD)orKnowledgeDiscoveryandDataMining,istheprocessofsearchinglargevolumesofdataautomaticallyforpatternssuchasassociationrules.Itappliesmanycomputationaltechniquesfromstatistics,informationretrieval,machinelearningandpatternrecognition.Dataminingextractonlyrequiredpatternsfromthedatabaseinashorttimespan.Basedonthetypeofpatternstobemined,dataminingtaskscanbeclassifiedintosummarization,classification,clustering,associationandtrendsanalysis.BigDataisexpandinginalldomainsincludingscienceandengineeringfieldsincludingphysical,biologicalandbiomedicalsciences.II.BIGDATAwithDATAMININGGenerallybigdatareferstoacollectionoflargevolumesofdataandthesedataaregeneratedfromvarioussourceslikeinternet,social-media,businessorganization,sensorsetc.WecanextractsomeusefulinformationwiththehelpofDataMining.Itisatechniquefordiscoveringpatternsaswellasdescriptive,understandable,modelsfromalargescaleofdata.Volumeisthesizeofthedatawhichislargerthanpetabytesandterabytes.Thescaleandriseofsizemakesitdifficulttostoreandanalyseusingtraditionaltools.BigDatashouldbeusedtominelargeamountsofdatawithinthepredefinedperiodoftime.Traditionaldatabasesystemsweredesignedtoaddresssmallamount...
