Distributed Data Management Using MapReduce

doi:10.1145/2503009

Distributed Data Management Using MapReduce

2014-01-01

109

OA

AI

DOI：10.1145/2503009

Original request for help

Save

Abstract

En 中文

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.

Keywords:

Algorithms

Performance

MapReduce

scalability

Hadoop

Distributed Data Management Using MapReduce

Abstract

Journal

Organization