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|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|
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