{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T21:57:46Z","timestamp":1790373466632,"version":"4.1.0"},"reference-count":37,"publisher":"Association for Computing Machinery (ACM)","issue":"11","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2018,7]]},"abstract":"<jats:p>\n            Many popular data cleaning approaches are rule-based: Constraints are formulated in a logical framework, and data is considered dirty if constraints are violated. These constraints are often discovered from data, but to ascertain their validity, user verification is necessary. Since the full set of discovered constraints is typically too large for manual inspection, recent research integrates user feedback into the discovery process. We propose a different approach that employs user interaction\n            <jats:italic>only at the start<\/jats:italic>\n            of the algorithm: a user manually cleans a small set of dirty tuples, and we infer the constraint underlying those repairs, called an\n            <jats:italic>explanation<\/jats:italic>\n            . We make use of conditional functional dependencies (CFDs) as the constraint formalism. We introduce XP\n            <jats:sc>lode<\/jats:sc>\n            , an on-demand algorithm which discovers the best explanation for a given repair. Guided by this explanation, data can then be cleaned using state-of-the-art CFD-based cleaning algorithms. Experiments on synthetic and real-world datasets show that the best explanation can typically be inferred using a limited number of modifications. Moreover, XP\n            <jats:sc>lode<\/jats:sc>\n            is substantially faster than discovering all CFDs that hold on a dataset, and is robust to noise in the modifications.\n          <\/jats:p>","DOI":"10.14778\/3236187.3236193","type":"journal-article","created":{"date-parts":[[2018,9,10]],"date-time":"2018-09-10T12:12:28Z","timestamp":1536581548000},"page":"1387-1399","source":"Crossref","is-referenced-by-count":26,"title":["Explaining repaired data with CFDs"],"prefix":"10.14778","volume":"11","author":[{"given":"Joeri","family":"Rammelaere","sequence":"first","affiliation":[{"name":"University of Antwerp, Antwerp, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Floris","family":"Geerts","sequence":"additional","affiliation":[{"name":"University of Antwerp, Antwerp, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2018,7]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Full version. https:\/\/bit.ly\/2tU2zcF.  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K\u00e4rkk\u00e4inen , P. Porkka , and H. Toivonen . TANE: An efficient algorithm for discovering functional and approximate dependencies. The computer journal, 42(2):100--111 , 1999 . Y. Huhtala, J. K\u00e4rkk\u00e4inen, P. Porkka, and H. Toivonen. TANE: An efficient algorithm for discovering functional and approximate dependencies. The computer journal, 42(2):100--111, 1999."},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.5555\/2901670"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/2723372.2747646"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/0304-3975(95)00028-U"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/1514894.1514901"},{"key":"e_1_2_1_27_1","volume-title":"UCI machine learning repository","author":"Lichman M.","year":"2013","unstructured":"M. Lichman . UCI machine learning repository , 2013 . M. Lichman. 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