2014-ICDE论文集总结

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1、2014-ICDE论文集ICDERESEARCHSESSIONSResearchPapersSession1Clustering lIncrementalClusterEvolutionTrackingfromHighlyDynamicNetworkData(shxy)oPeiLee*(UBC)oLaksV.S.Lakshmanan(UBC)oEvangelosMilios(DalhousieUniversity)摘要：Dynamicnetworksarecommonlyfoundinthecurrentwebage.Inscenarioslikes

2、ocialnetworksandsocialmedia,dynamicnetworksarenoisy,areoflarge-scaleandevolvequickly.Inthispaper,wefocusontheclusterevolutiontrackingproblemonhighlydynamicnetworks,withclearapplicationtoeventevolutiontracking.Thereareseveralpreviousworksondatastreamclusteringusinganode-by-nodea

3、pproachformaintainingclusters.However,handlingofbulkupdates,i.e.,asubgraphatatime,iscriticalforachievingacceptableperformanceoververylargehighlydynamicnetworks.Weproposeasubgraph-bysubgraphincrementaltrackingframeworkforclusterevolutioninthispaper.Toeffectivelyillustratethetech

4、niquesinourframework,wetaketheeventevolutiontrackingtaskinsocialstreamsasanapplication,whereasocialstreamandaneventaremodeledasadynamicpostnetworkandadynamicclusterrespectively.Bymonitoringthroughafadingtimewindow,weintroduceaskeletalgraphtosummarizetheinformationinthedynamicne

5、twork,andformalizeclusterevolutionpatternsusingagroupofprimitiveevolutionoperationsandtheiralgebra.Twoincrementalcomputationalgorithmsaredevelopedtomaintainclustersandtrackevolutionpatternsastimerollsonandthenetworkevolves.OurdetailedexperimentalevaluationonlargeTwitterdatasets

6、demonstratesthatourframeworkcaneffectivelytrackthecompletesetofclusterevolutionpatternsinthewholelifecyclefromhighlydynamicnetworksonthefly.大意：动态网络（如社交网络）在网络时代非常常见。本文主要解决的是在动态网络上cluster演化的跟踪问题（事件的演化跟踪）。在本文之前的跟踪方法（节点的更新）涉及到很多的更新操作。本文提出了一个基于子图的增量追踪框架。通过对社交流（skeletalgraph）和事件的单独建模

7、来解决此问题。lFindingCommonGroundamongExperts’OpinionsonDataClustering:withApplicationsinMalwareAnalysisoGuanhuaYan*(LosAlamosNationalLab)摘要：Dataclusteringisabasictechniqueforknowledgediscoveryanddatamining.Asthevolumeofdatagrowssignificantly,dataclusteringbecomescomputationallyprohi

8、bitiveandresourcedemanding,andsometimesitisnecessaryto