Full metadata record
DC FieldValueLanguage
dc.contributor.authorGau, Rung-Hungen_US
dc.contributor.authorHsieh, Tzu-Chiangen_US
dc.contributor.authorTsai, Sheng-Wenen_US
dc.contributor.authorCheng, Ching-Peien_US
dc.date.accessioned2014-12-08T15:23:05Z-
dc.date.available2014-12-08T15:23:05Z-
dc.date.issued2011en_US
dc.identifier.isbn978-1-4503-0903-5en_US
dc.identifier.urihttp://hdl.handle.net/11536/16239-
dc.description.abstractIn this paper, we introduce our own implementation of Map Reduce graph-theoretic algorithms for Email social network analysis on the Hadoop platform. Graph theory is a powerful tool for social network analysis and Map Reduce is a well-known paradigm for distributed parallel computing. However, based on our own experience, unlike writing conventional Java/C++ programs, writing Java programs to implement Map Reduce graph-theoretic algorithms is not straight-forward, even for some fundamental graph-theoretic algorithms. In this paper, for the problem of Email social network analysis, we compare the performance of cloud computing programs with that of conventional computer programs. We show that as long as the size of the input data exceeds a threshold, the cloud computing programs outperform their conventional counterparts.en_US
dc.language.isoen_USen_US
dc.subjectCloud computingen_US
dc.subjectMapReduceen_US
dc.subjectgraph theoryen_US
dc.subjectsocial network analysisen_US
dc.titleAn Implementation Framework of MapReduce Email Social Network Analysisen_US
dc.typeProceedings Paperen_US
dc.identifier.journalWMUNEP 11: PROCEEDINGS OF THE SEVENTH ACM WORKSHOP ON WIRELESS MULTIMEDIA NETWORKING AND COMPUTINGen_US
dc.citation.epage67en_US
dc.contributor.department電機工程學系zh_TW
dc.contributor.departmentDepartment of Electrical and Computer Engineeringen_US
dc.identifier.wosnumberWOS:000304071400011-
Appears in Collections:Conferences Paper