{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:16:16Z","timestamp":1750306576978,"version":"3.41.0"},"reference-count":32,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2014,9,25]],"date-time":"2014-09-25T00:00:00Z","timestamp":1411603200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGKDD Explor. Newsl."],"published-print":{"date-parts":[[2014,9,25]]},"abstract":"<jats:p>\n            Most data analytics applications are industry\/domain specific, e.g., predicting patients at high risk of being admitted to intensive care unit in the healthcare sector or predicting malicious SMSs in the telecommunication sector. Existing solutions are based on \"best practices\", i.e., the systems' decisions are\n            <jats:italic>knowledge-driven and\/or data-driven<\/jats:italic>\n            . However, there are rules and exceptional cases that can only be precisely formulated and identified by subject-matter experts (SMEs) who have accumulated many years of experience. This paper envisions a more intelligent database management system (DBMS) that captures such knowledge to effectively address the industry\/domain specific applications. At the core, the system is a hybrid human-machine database engine where the machine interacts with the SMEs as part of a feedback loop to gather, infer, ascertain and enhance the database knowledge and processing. We discuss the challenges towards building such a system through examples in healthcare predictive analysis -- a popular area for big data analytics.\n          <\/jats:p>","DOI":"10.1145\/2674026.2674032","type":"journal-article","created":{"date-parts":[[2014,10,1]],"date-time":"2014-10-01T13:34:59Z","timestamp":1412170499000},"page":"39-46","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["Contextual crowd intelligence"],"prefix":"10.1145","volume":"16","author":[{"given":"Beng Chin","family":"Ooi","sequence":"first","affiliation":[{"name":"National University of Singapore"}]},{"given":"Kian Lee","family":"Tan","sequence":"additional","affiliation":[{"name":"National University of Singapore"}]},{"given":"Quoc Trung","family":"Tran","sequence":"additional","affiliation":[{"name":"National University of Singapore"}]},{"given":"James W.L.","family":"Yip","sequence":"additional","affiliation":[{"name":"National University Health System"}]},{"given":"Gang","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhejiang University"}]},{"given":"Zheng Jye","family":"Ling","sequence":"additional","affiliation":[{"name":"National University Health System"}]},{"given":"Thi","family":"Nguyen","sequence":"additional","affiliation":[{"name":"National University of Singapore"}]},{"given":"Anthony K.H.","family":"Tung","sequence":"additional","affiliation":[{"name":"National University of Singapore"}]},{"given":"Meihui","family":"Zhang","sequence":"additional","affiliation":[{"name":"National University of Singapore"}]}],"member":"320","published-online":{"date-parts":[[2014,9,25]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"http:\/\/www.comp.nus.edu.sg\/_epic.  http:\/\/www.comp.nus.edu.sg\/_epic."},{"key":"e_1_2_1_2_1","unstructured":"The comprehensive it infrastructure for dataintensive applications and analysis project. http:\/\/www.comp.nus.edu.sg\/_ciidaa\/.  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A general natural-language text processor for clinical radiology . JAMIA , 1 ( 2 ): 161 -- 174 , 1994 . C. Friedman, P. O. Alderson, J. H. Austin, J. J. Cimino, and S. B. Johnson. A general natural-language text processor for clinical radiology. JAMIA, 1(2):161--174, 1994.","journal-title":"JAMIA"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/1656274.1656278"},{"key":"e_1_2_1_16_1","volume-title":"Data Mining: Concepts and Techniques","author":"Han J.","year":"2005","unstructured":"J. Han . Data Mining: Concepts and Techniques . Morgan Kaufmann Publishers Inc ., San Francisco, CA, USA, 2005 . J. Han. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 2005."},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/1143844.1143897"},{"key":"e_1_2_1_18_1","volume-title":"IAAI","author":"Hosseinzadeh A.","year":"2013","unstructured":"A. Hosseinzadeh , M. T. Izadi , A. Verma , D. Precup , and D. L. Buckeridge . Assessing the predictability of hospital readmission using machine learning . In IAAI , 2013 . A. Hosseinzadeh, M. T. Izadi, A. Verma, D. Precup, and D. L. Buckeridge. Assessing the predictability of hospital readmission using machine learning. In IAAI, 2013."},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.14778\/2732286.2732291"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCOUTCOMES.108.802686"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/2505515.2508213"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.14778\/2336664.2336676"},{"key":"e_1_2_1_23_1","first-page":"211","volume-title":"CIDR","author":"Marcus A.","year":"2011","unstructured":"A. Marcus , E. Wu , S. Madden , and R. C. Miller . Crowdsourced databases: Query processing with people . In CIDR , pages 211 -- 214 , 2011 . A. Marcus, E. Wu, S. Madden, and R. C. Miller. Crowdsourced databases: Query processing with people. 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Active learning applied to patientadaptive heartbeat classification . In NIPS , pages 2442 -- 2450 , 2010 . J. Wiens and J. Guttag. Active learning applied to patientadaptive heartbeat classification. 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