{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T18:05:12Z","timestamp":1792173912434,"version":"4.4.0"},"reference-count":30,"publisher":"Association for Computing Machinery (ACM)","issue":"7","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2014,3]]},"abstract":"<jats:p>The Big Data problem is characterized by the so called 3V features: Volume - a huge amount of data, Velocity - a high data ingestion rate, and Variety - a mix of structured data, semi-structured data, and unstructured data. The state-of-the-art solutions to the Big Data problem are largely based on the MapReduce framework (aka its open source implementation Hadoop). Although Hadoop handles the data volume challenge successfully, it does not deal with the data variety well since the programming interfaces and its associated data processing model is inconvenient and inefficient for handling structured data and graph data.<\/jats:p>\n          <jats:p>\n            This paper presents\n            <jats:italic>epiC<\/jats:italic>\n            , an extensible system to tackle the Big Data's data variety challenge.\n            <jats:italic>epiC<\/jats:italic>\n            introduces a general Actor-like concurrent programming model, independent of the data processing models, for specifying parallel computations. Users process multi-structured datasets with appropriate\n            <jats:italic>epiC<\/jats:italic>\n            extensions, the implementation of a data processing model best suited for the data type and auxiliary code for mapping that data processing model into\n            <jats:italic>epiC<\/jats:italic>\n            's concurrent programming model. Like Hadoop, programs written in this way can be automatically parallelized and the runtime system takes care of fault tolerance and inter-machine communications. We present the design and implementation of\n            <jats:italic>epiC<\/jats:italic>\n            's concurrent programming model. We also present two customized data processing model, an optimized MapReduce extension and a relational model, on top of\n            <jats:italic>epiC.<\/jats:italic>\n            Experiments demonstrate the effectiveness and efficiency of our proposed\n            <jats:italic>epiC.<\/jats:italic>\n          <\/jats:p>","DOI":"10.14778\/2732286.2732291","type":"journal-article","created":{"date-parts":[[2015,5,12]],"date-time":"2015-05-12T15:37:52Z","timestamp":1431445072000},"page":"541-552","source":"Crossref","is-referenced-by-count":45,"title":["epiC"],"prefix":"10.14778","volume":"7","author":[{"given":"Dawei","family":"Jiang","sequence":"first","affiliation":[{"name":"National University of Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gang","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Beng Chin","family":"Ooi","sequence":"additional","affiliation":[{"name":"National University of Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kian-Lee","family":"Tan","sequence":"additional","affiliation":[{"name":"National University of Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sai","family":"Wu","sequence":"additional","affiliation":[{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2014,3]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"The hadoop offical website. http:\/\/hadoop.apache.org\/.  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Technical report, Stanford InfoLab, November 1999."},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/1559845.1559865"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/1005813.1041517"},{"key":"e_1_2_1_26_1","volume-title":"VLDB","author":"Stonebraker M.","year":"2005","unstructured":"M. Stonebraker , D. J. Abadi , A. Batkin , X. Chen , M. Cherniack , M. Ferreira , E. Lau , A. Lin , S. Madden , E. O'Neil , P. O'Neil , A. Rasin , N. Tran , and S. Zdonik . C-store: a column-oriented dbms . In VLDB , 2005 . M. Stonebraker, D. J. Abadi, A. Batkin, X. Chen, M. Cherniack, M. Ferreira, E. Lau, A. Lin, S. Madden, E. O'Neil, P. O'Neil, A. Rasin, N. Tran, and S. Zdonik. C-store: a column-oriented dbms. 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