DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kang, Woo-Lam | ko |
dc.contributor.author | Lee, Hyeon Gyu | ko |
dc.contributor.author | Lee, Yoon-Joon | ko |
dc.date.accessioned | 2023-05-25T07:02:38Z | - |
dc.date.available | 2023-05-25T07:02:38Z | - |
dc.date.created | 2023-05-25 | - |
dc.date.created | 2023-05-25 | - |
dc.date.issued | 2014-12 | - |
dc.identifier.citation | 6th FTRA International Conference on Computer Science and its Applications, CSA 2014, pp.997 - 1002 | - |
dc.identifier.issn | 1876-1100 | - |
dc.identifier.uri | http://hdl.handle.net/10203/306931 | - |
dc.description.abstract | Google’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.language | English | - |
dc.publisher | Springer | - |
dc.title | IO Cost Evaluation of OLAP Query Processing with MapReduce | - |
dc.type | Conference | - |
dc.identifier.scopusid | 2-s2.0-84915820183 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 997 | - |
dc.citation.endingpage | 1002 | - |
dc.citation.publicationname | 6th FTRA International Conference on Computer Science and its Applications, CSA 2014 | - |
dc.identifier.conferencecountry | US | - |
dc.identifier.conferencelocation | Guam | - |
dc.contributor.localauthor | Lee, Yoon-Joon | - |
dc.contributor.nonIdAuthor | Lee, Hyeon Gyu | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.