{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T09:52:05Z","timestamp":1789984325438,"version":"4.0.1"},"reference-count":25,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2012,8]]},"abstract":"<jats:p>There is a growing trend of applications that should handle big data. However, analyzing big data is a very challenging problem today. For such applications, the MapReduce framework has recently attracted a lot of attention. Google's MapReduce or its open-source equivalent Hadoop is a powerful tool for building such applications. In this tutorial, we will introduce the MapReduce framework based on Hadoop, discuss how to design efficient MapReduce algorithms and present the state-of-the-art in MapReduce algorithms for data mining, machine learning and similarity joins. The intended audience of this tutorial is professionals who plan to design and develop MapReduce algorithms and researchers who should be aware of the state-of-the-art in MapReduce algorithms available today for big data analysis.<\/jats:p>","DOI":"10.14778\/2367502.2367563","type":"journal-article","created":{"date-parts":[[2014,6,24]],"date-time":"2014-06-24T12:17:57Z","timestamp":1403612277000},"page":"2016-2017","source":"Crossref","is-referenced-by-count":66,"title":["MapReduce algorithms for big data analysis"],"prefix":"10.14778","volume":"5","author":[{"given":"Kyuseok","family":"Shim","sequence":"first","affiliation":[{"name":"Seoul National University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2012,8]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"SIGMOD","author":"Agrawal R.","year":"1998","unstructured":"R. Agrawal , J. Gehrke , D. Gunopulos , and P. Raghavan . 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