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MapReduce adopts a flexible computation model with a simple interface consisting of\n            <jats:italic>map<\/jats:italic>\n            and\n            <jats:italic>reduce<\/jats:italic>\n            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.\n          <\/jats:p>","DOI":"10.1145\/2503009","type":"journal-article","created":{"date-parts":[[2014,2,7]],"date-time":"2014-02-07T14:22:52Z","timestamp":1391782972000},"page":"1-42","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":83,"title":["Distributed data management using MapReduce"],"prefix":"10.1145","volume":"46","author":[{"given":"Feng","family":"Li","sequence":"first","affiliation":[{"name":"National University of Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Beng Chin","family":"Ooi","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M. 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