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Author Li, Feng ♦ Ooi, Beng Chin ♦ Zsu, M Tamer ♦ Wu, Sai
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2014
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Hadoop ♦ MapReduce ♦ Scalability
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 $\textit{map}$ and $\textit{reduce}$ 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.
ISSN 03600300
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2014-01-01
Publisher Place New York
e-ISSN 15577341
Journal ACM Computing Surveys (CSUR)
Volume Number 46
Issue Number 3
Page Count 42
Starting Page 1
Ending Page 42

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Source: ACM Digital Library