{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T19:12:23Z","timestamp":1770837143579,"version":"3.50.1"},"reference-count":11,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2018,8]]},"abstract":"<jats:p>On line analytical processing (OLAP) is an essential element of decision-support systems. However, OLAP queries can be biased and lead to perplexing and incorrect insights. In this demo, we present HypDB, the first system to detect, explain and resolve bias in OLAP queries. Our demonstration, shows several examples of OLAP queries from real world datasets that are biased and could lead to statistical anomalies such as Simpson's paradox. Then, we demonstrate step-by-step how HypDB: (1) detects whether an OLAP query is biased, (2) explains the root causes of the bias and reveals illuminating insights about the domain and the data collection process and (3) eliminates the bias via query rewriting and generates decision-support insights.<\/jats:p>","DOI":"10.14778\/3229863.3236260","type":"journal-article","created":{"date-parts":[[2018,9,10]],"date-time":"2018-09-10T12:12:28Z","timestamp":1536581548000},"page":"2062-2065","source":"Crossref","is-referenced-by-count":13,"title":["HypDB"],"prefix":"10.14778","volume":"11","author":[{"given":"Babak","family":"Salimi","sequence":"first","affiliation":[{"name":"University of Washington"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Corey","family":"Cole","sequence":"additional","affiliation":[{"name":"University of Washington"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter","family":"Li","sequence":"additional","affiliation":[{"name":"University of Washington"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Johannes","family":"Gehrke","sequence":"additional","affiliation":[{"name":"Microsoft"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dan","family":"Suciu","sequence":"additional","affiliation":[{"name":"University of Washington"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2018,8]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.187.4175.398"},{"key":"e_1_2_1_2_1","volume-title":"CIDR","author":"Binnig C.","year":"2017","unstructured":"C. Binnig , L. D. Stefani , T. Kraska , E. Upfal , E. Zgraggen , and Z. Zhao . Toward sustainable insights, or why polygamy is bad for you . In CIDR , 2017 . C. Binnig, L. D. Stefani, T. Kraska, E. Upfal, E. Zgraggen, and Z. Zhao. Toward sustainable insights, or why polygamy is bad for you. In CIDR, 2017."},{"key":"e_1_2_1_3_1","volume-title":"Are we really discovering interesting knowledge from data. Expert Update (the BCS-SGAI magazine), 9(l):41--47","author":"Freitas A. A.","year":"2006","unstructured":"A. A. Freitas . Are we really discovering interesting knowledge from data. Expert Update (the BCS-SGAI magazine), 9(l):41--47 , 2006 . A. A. Freitas. Are we really discovering interesting knowledge from data. Expert Update (the BCS-SGAI magazine), 9(l):41--47, 2006."},{"key":"e_1_2_1_4_1","volume-title":"Lung cancer simple model, 10","author":"Guyon I.","year":"2009","unstructured":"I. Guyon . Lung cancer simple model, 10 2009 . I. Guyon. 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Performance. http:\/\/www.transtats.bts.gov\/."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214504000001880"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.14778\/3137765.3137818"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3183713.3196914"},{"key":"e_1_2_1_11_1","first-page":"401","volume-title":"2017 IEEE European Symposium on","author":"Trainer F.","year":"2017","unstructured":"F. Trainer and et al. Fairtest: Discovering unwarranted associations in data-driven applications. In Security and Privacy (EuroS&P) , 2017 IEEE European Symposium on , pages 401 -- 416 . IEEE, 2017 . F. Trainer and et al. Fairtest: Discovering unwarranted associations in data-driven applications. In Security and Privacy (EuroS&P), 2017 IEEE European Symposium on, pages 401--416. IEEE, 2017."}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3229863.3236260","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T10:08:30Z","timestamp":1672222110000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3229863.3236260"}},"subtitle":["a demonstration of detecting, explaining and resolving bias in OLAP queries"],"short-title":[],"issued":{"date-parts":[[2018,8]]},"references-count":11,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2018,8]]}},"alternative-id":["10.14778\/3229863.3236260"],"URL":"https:\/\/doi.org\/10.14778\/3229863.3236260","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2018,8]]}}}