IO Cost Evaluation of OLAP Query Processing with MapReduce

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dc.contributor.authorKang, Woo Lamko
dc.contributor.authorLee, Hyeon Gyuko
dc.contributor.authorLee, Yoon Joonko
dc.date.accessioned2015-04-07T02:37:49Z-
dc.date.available2015-04-07T02:37:49Z-
dc.date.created2014-11-24-
dc.date.created2014-11-24-
dc.date.issued2015-
dc.identifier.citationLecture Notes in Electrical Engineering, v.330, pp.997 - 1002-
dc.identifier.issn1876-1100-
dc.identifier.urihttp://hdl.handle.net/10203/195104-
dc.description.abstractGoogle’s MapReduce has emerged as a popular framework for data-intensive computing. It is well-known by its elastic scalability and fine-grained fault tolerance. On the other hand, there are some debates in its efficiency. Especially, local and network I/Os can be a primary factor that degrades the performance of MapReduce, because it follows a data shipping paradigm where many partitioned data blocks move along distributed nodes. In this paper, we conduct a performance study to examine the I/O cost of MapReduce. Our results show that the I/O cost accounts for about 80% of the total processing cost when processing OLAP queries in the MapReduce platform.-
dc.languageEnglish-
dc.publisherSpringer-
dc.titleIO Cost Evaluation of OLAP Query Processing with MapReduce-
dc.typeArticle-
dc.identifier.scopusid2-s2.0-84915820183-
dc.type.rimsART-
dc.citation.volume330-
dc.citation.beginningpage997-
dc.citation.endingpage1002-
dc.citation.publicationnameLecture Notes in Electrical Engineering-
dc.contributor.localauthorLee, Yoon Joon-
dc.contributor.nonIdAuthorLee, Hyeon Gyu-
dc.subject.keywordAuthorData shipping paradigm-
dc.subject.keywordAuthorMapReduce-
dc.subject.keywordAuthorOLAP-
dc.subject.keywordAuthorTPC-H benchmark-
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