{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T07:14:11Z","timestamp":1779174851980,"version":"3.51.4"},"reference-count":40,"publisher":"Association for Computing Machinery (ACM)","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:p>\n            The detection of constraint-based errors is a critical task in many data cleaning solutions. Previous works perform the task either using traditional data management systems or using specialized systems that speed up error detection. Unfortunately, both approaches may fail to execute in a reasonable time or even exhaust the available memory in the attempt. To address the main drawbacks of previous approaches, we present the\n            <jats:italic>FAst Constraint-based Error DeTector<\/jats:italic>\n            (FACET) to detect violations of denial constraints (DCs). FACET uses column sketch information to organize a pipeline of special operators for DC predicates and it implements these operators using a set of efficient algorithms and data structures that adapt to different data characteristics and predicate structures. We evaluate our system on a diverse array of datasets and constraints, showing its robustness and performance gains compared to different types of DBMSs and to a specialized system.\n          <\/jats:p>","DOI":"10.14778\/3503585.3503595","type":"journal-article","created":{"date-parts":[[2022,4,14]],"date-time":"2022-04-14T22:18:07Z","timestamp":1649974687000},"page":"859-871","source":"Crossref","is-referenced-by-count":15,"title":["Fast detection of denial constraint violations"],"prefix":"10.14778","volume":"15","author":[{"given":"Eduardo H. M.","family":"Pena","sequence":"first","affiliation":[{"name":"Federal University of Technology, Campo Mour\u00e3o, Paran\u00e1, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eduardo C.","family":"de Almeida","sequence":"additional","affiliation":[{"name":"Federal University of Paran\u00e1, Curitiba, Paran\u00e1, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Felix","family":"Naumann","sequence":"additional","affiliation":[{"name":"University of Potsdam, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,4,14]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/303976.303983"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3452831"},{"key":"e_1_2_1_3_1","volume-title":"Database Repairing and Consistent Query Answering","author":"Bertossi Leopoldo","unstructured":"Leopoldo Bertossi . 2011. Database Repairing and Consistent Query Answering . Morgan & Claypool Publishers . Leopoldo Bertossi. 2011. Database Repairing and Consistent Query Answering. Morgan & Claypool Publishers."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3294052.3322190"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/1989323.1989328"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.14778\/3157794.3157800"},{"key":"e_1_2_1_7_1","volume-title":"Dong Ping Zhang","author":"Breslow Alex D.","year":"2016","unstructured":"Alex D. Breslow , Dong Ping Zhang , Joseph L. Greathouse , Nuwan Jayasena , and Dean M. Tullsen. 2016 . Horton Tables : Fast Hash Tables for in-Memory Data-Intensive Computing. In Proceedings of the USENIX Annual Technical Conference. 281--294. Alex D. Breslow, Dong Ping Zhang, Joseph L. Greathouse, Nuwan Jayasena, and Dean M. Tullsen. 2016. Horton Tables: Fast Hash Tables for in-Memory Data-Intensive Computing. 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Discrete Mathematics & Theoretical Computer Science","author":"Flajolet Philippe","year":"2007","unstructured":"Philippe Flajolet , \u00c9ric Fusy , Olivier Gandouet , and Fr\u00e9d\u00e9ric Meunier . 2007. Hyper-LogLog: the analysis of a near-optimal cardinality estimation algorithm. Discrete Mathematics & Theoretical Computer Science ( 2007 ), 137--156. Philippe Flajolet, \u00c9ric Fusy, Olivier Gandouet, and Fr\u00e9d\u00e9ric Meunier. 2007. Hyper-LogLog: the analysis of a near-optimal cardinality estimation algorithm. Discrete Mathematics & Theoretical Computer Science (2007), 137--156."},{"key":"e_1_2_1_18_1","volume-title":"Freitag and Thomas Neumann","author":"Michael","year":"2019","unstructured":"Michael J. Freitag and Thomas Neumann . 2019 . Every Row Counts: Combining Sketches and Sampling for Accurate Group-By Result Estimates . In Proceedings of the Conference on Innovative Data Systems Research (CIDR). Michael J. Freitag and Thomas Neumann. 2019. 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