Please use this identifier to cite or link to this item: https://doi.org/10.1145/2503009
Title: Distributed data management using mapreduce
Authors: Li, F.
Ooi, B.C. 
Özsu, M.T.
Wu, S.
Keywords: Hadoop
MapReduce
Scalability
Issue Date: Jan-2014
Source: 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
URI: http://scholarbank.nus.edu.sg/handle/10635/77843
ISSN: 03600300
DOI: 10.1145/2503009
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