{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T00:28:11Z","timestamp":1777854491901,"version":"3.51.4"},"reference-count":30,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2019,9,11]],"date-time":"2019-09-11T00:00:00Z","timestamp":1568160000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of P.R. 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Instead of focusing on attribute significance, the notion of a core attribute was applied to construct a new heuristic reduction algorithm, and only |\n                    <jats:italic toggle=\"yes\">C<\/jats:italic>\n                    | jobs were considered to obtain a reduct. The algorithm only included two basic operations: compare and sort. The latter was optimised using the shuffle mechanism in MapReduce, which provided an efficient sorting ability for big data. In particular, we connected jobs in an iterative form to transfer the processing result of the former job to the latter job. Finally, experimental results demonstrated that the proposed attribute reduction algorithm was efficient and significantly improved upon the classical algorithms in runtime and number of jobs.\n                  <\/jats:p>","DOI":"10.1177\/0165551519874617","type":"journal-article","created":{"date-parts":[[2019,9,11]],"date-time":"2019-09-11T12:18:30Z","timestamp":1568204310000},"page":"101-117","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":6,"title":["An efficient attribute reduction algorithm using MapReduce"],"prefix":"10.1177","volume":"47","author":[{"given":"Linzi","family":"Yin","sequence":"first","affiliation":[{"name":"Central South University, P.R. 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