{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T17:28:53Z","timestamp":1790789333516,"version":"4.1.0"},"reference-count":37,"publisher":"Association for Computing Machinery (ACM)","issue":"13","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2016,9]]},"abstract":"<jats:p>Data quality is one of the most important problems in data management, since dirty data often leads to inaccurate data analytics results and wrong business decisions.<\/jats:p>\n          <jats:p>Data cleaning exercise often consist of two phases: error detection and error repairing. Error detection techniques can either be quantitative or qualitative; and error repairing is performed by applying data transformation scripts or by involving human experts, and sometimes both.<\/jats:p>\n          <jats:p>In this tutorial, we discuss the main facets and directions in designing qualitative data cleaning techniques. We present a taxonomy of current qualitative error detection techniques, as well as a taxonomy of current data repairing techniques. We will also discuss proposals for tackling the challenges for cleaning \"big data\" in terms of scale and distribution.<\/jats:p>","DOI":"10.14778\/3007263.3007320","type":"journal-article","created":{"date-parts":[[2016,11,1]],"date-time":"2016-11-01T13:47:47Z","timestamp":1478008067000},"page":"1605-1608","source":"Crossref","is-referenced-by-count":37,"title":["Qualitative data cleaning"],"prefix":"10.14778","volume":"9","author":[{"given":"Xu","family":"Chu","sequence":"first","affiliation":[{"name":"University of Waterloo"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ihab F.","family":"Ilyas","sequence":"additional","affiliation":[{"name":"University of Waterloo"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2016,9]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.5555\/2436823"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.5555\/1287369.1287420"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.14778\/1920841.1920870"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2013.6544854"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.14778\/1687627.1687695"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/1066157.1066175"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2007.367920"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2588555.2610520"},{"key":"e_1_2_1_9_1","volume-title":"PAKDD","author":"Chawla S.","year":"2006","unstructured":"S. Chawla and P. Sun . Outlier detection: Principles, techniques and applications . In PAKDD , 2006 . S. Chawla and P. Sun. Outlier detection: Principles, techniques and applications. In PAKDD, 2006."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2011.5767833"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.14778\/2983200.2983203"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.14778\/2536258.2536262"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2013.6544847"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/2723372.2749431"},{"key":"e_1_2_1_15_1","first-page":"315","volume-title":"PVLDB","author":"Cong G.","year":"2007","unstructured":"G. Cong , W. Fan , F. Geerts , X. Jia , and S. Ma . Improving data quality: Consistency and accuracy . In PVLDB , pages 315 -- 326 . VLDB Endowment , 2007 . G. Cong, W. Fan, F. Geerts, X. Jia, and S. Ma. Improving data quality: Consistency and accuracy. In PVLDB, pages 315--326. VLDB Endowment, 2007."},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/2463676.2465327"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.14778\/1920841.1920867"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/1989323.1989373"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.14778\/2536360.2536363"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/2588555.2588576"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.14778\/1453856.1453900"},{"key":"e_1_2_1_22_1","volume-title":"United Nations Economic Commission for Europe (UNECE)","author":"Hellerstein J. M.","year":"2008","unstructured":"J. M. Hellerstein . Quantitative data cleaning for large databases . United Nations Economic Commission for Europe (UNECE) , 2008 . J. M. Hellerstein. Quantitative data cleaning for large databases. United Nations Economic Commission for Europe (UNECE), 2008."},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1561\/1900000045"},{"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":"10.1145\/1514894.1514901"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.14778\/2367502.2367527"},{"key":"e_1_2_1_27_1","volume-title":"Tutorial at SIGKDD","author":"Kriegel H.-P.","year":"2010","unstructured":"H.-P. Kriegel , P. Kr\u00f6ger , and A. Zimek . Outlier detection techniques . In Tutorial at SIGKDD , 2010 . H.-P. Kriegel, P. Kr\u00f6ger, and A. Zimek. Outlier detection techniques. In Tutorial at SIGKDD, 2010."},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/1989323.1989376"},{"key":"e_1_2_1_29_1","first-page":"381","volume-title":"VLDB","author":"Raman V.","year":"2001","unstructured":"V. Raman and J. M. Hellerstein . 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