Please use this identifier to cite or link to this item:
|Title:||Distributed data management using mapreduce|
|Citation:||Li, F., Ooi, B.C., Özsu, M.T., Wu, S. (2014-01). Distributed data management using mapreduce. ACM Computing Surveys 46 (3) : -. ScholarBank@NUS Repository. https://doi.org/10.1145/2503009|
|Abstract:||MapReduce is a framework for processing and managing large-scale datasets in a distributed cluster, which has been used for applications such as generating search indexes, document clustering, access log analysis, and various other forms of data analytics. MapReduce adopts a flexible computation model with a simple interface consisting of map and reduce functions whose implementations can be customized by application developers. Since its introduction, a substantial amount of research effort has been directed toward making it more usable and efficient for supporting database-centric operations. In this article, we aim to provide a comprehensive review of a wide range of proposals and systems that focusing fundamentally on the support of distributed data management and processing using the MapReduce framework. © 2014 ACM.|
|Source Title:||ACM Computing Surveys|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Sep 21, 2018
WEB OF SCIENCETM
checked on Sep 11, 2018
checked on May 11, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.