{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,27]],"date-time":"2025-09-27T13:53:18Z","timestamp":1758981198494,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031553257"},{"type":"electronic","value":"9783031553264"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-55326-4_22","type":"book-chapter","created":{"date-parts":[[2024,3,14]],"date-time":"2024-03-14T08:46:10Z","timestamp":1710405970000},"page":"448-468","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["RDC-Repair: Towards a\u00a0Relevance-Driven Approach for\u00a0Data and\u00a0Constraints Repair"],"prefix":"10.1007","author":[{"given":"Nibel","family":"Nadjeh","sequence":"first","affiliation":[]},{"given":"Sabrina","family":"Abdellaoui","sequence":"additional","affiliation":[]},{"given":"Fahima","family":"Nader","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,3,15]]},"reference":[{"key":"22_CR1","doi-asserted-by":"publisher","unstructured":"Abdellaoui, S., Nader, F., Chalal, R.: QDflows: a system driven by knowledge bases for designing quality-aware data flows. J. Data Inf. Q. 8(3\u20134), 1\u201339 (2017). https:\/\/doi.org\/10.1145\/3064173. https:\/\/dl.acm.org\/doi\/10.1145\/3064173","DOI":"10.1145\/3064173"},{"key":"22_CR2","doi-asserted-by":"crossref","unstructured":"Berti-Equille, L.: Reinforcement Learning for Data Preparation with Active Reward Learning (2019)","DOI":"10.1007\/978-3-030-34770-3_10"},{"key":"22_CR3","doi-asserted-by":"crossref","unstructured":"Beskales, G., Ilyas, I.F., Golab, L., Galiullin, A.: On the Relative Trust between Inconsistent Data and Inaccurate Constraints (2013)","DOI":"10.1109\/ICDE.2013.6544854"},{"key":"22_CR4","doi-asserted-by":"crossref","unstructured":"Bohannon, P., Fan, W., Rastogi, R., Flaster, M.: A Cost-Based Model and Effective Heuristic for Repairing Constraints by Value Modification (2005)","DOI":"10.1145\/1066157.1066175"},{"key":"22_CR5","doi-asserted-by":"crossref","unstructured":"Buranosky, M., Stellnberger, E., Pfaff, E., Diaz-Sanchez, D., Ward-Caviness, C.: FDTool: a python application to mine for functional dependencies and candidate keys in tabular data. F1000Research 7 (2018)","DOI":"10.12688\/f1000research.16483.1"},{"key":"22_CR6","doi-asserted-by":"crossref","unstructured":"Chiang, F., Miller, R.J.: A Unified Model for Data and Constraint Repair (2011)","DOI":"10.1109\/ICDE.2011.5767833"},{"issue":"13","key":"22_CR7","doi-asserted-by":"publisher","first-page":"1498","DOI":"10.14778\/2536258.2536262","volume":"6","author":"X Chu","year":"2013","unstructured":"Chu, X., Ilyas, I.F., Papotti, P.: Discovering denial constraints. Proc. VLDB Endow. 6(13), 1498\u20131509 (2013)","journal-title":"Proc. VLDB Endow."},{"key":"22_CR8","doi-asserted-by":"publisher","unstructured":"Chu, X., et al.: KATARA: a data cleaning system powered by knowledge bases and crowdsourcing. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, Melbourne, Victoria, Australia, pp. 1247\u20131261. ACM (2015). https:\/\/doi.org\/10.1145\/2723372.2749431. https:\/\/dl.acm.org\/doi\/10.1145\/2723372.2749431","DOI":"10.1145\/2723372.2749431"},{"key":"22_CR9","unstructured":"Cong, G., Fan, W., Geerts, F., Jia, X., Ma, S.: Improving Data Quality: Consistency and Accuracy (2007)"},{"key":"22_CR10","doi-asserted-by":"publisher","unstructured":"Dallachiesa, M., et al.: NADEEF: a commodity data cleaning system. In: Proceedings of the 2013 International Conference on Management of Data - SIGMOD 2013, New York, USA, p. 541. ACM Press, New York (2013). https:\/\/doi.org\/10.1145\/2463676.2465327. http:\/\/dl.acm.org\/citation.cfm?doid=2463676.2465327","DOI":"10.1145\/2463676.2465327"},{"key":"22_CR11","doi-asserted-by":"publisher","unstructured":"Fan, W., Li, J., Ma, S., Tang, N., Yu, W.: Towards certain fixes with editing rules and master data. VLDB J. 21(2), 213\u2013238 (2012). https:\/\/doi.org\/10.1007\/s00778-011-0253-7","DOI":"10.1007\/s00778-011-0253-7"},{"key":"22_CR12","doi-asserted-by":"publisher","unstructured":"Geerts, F., Mecca, G., Papotti, P., Santoro, D.: The LLUNATIC data-cleaning framework. Proc. VLDB Endow. 6(9), 625\u2013636 (2013). https:\/\/doi.org\/10.14778\/2536360.2536363. https:\/\/dl.acm.org\/doi\/10.14778\/2536360.2536363","DOI":"10.14778\/2536360.2536363"},{"key":"22_CR13","doi-asserted-by":"publisher","unstructured":"Ilyas, I.F., Chu, X.: Trends in cleaning relational data: consistency and deduplication. Found. Trends\u00ae Databases 5(4), 281\u2013393 (2015). https:\/\/doi.org\/10.1561\/1900000045. http:\/\/www.nowpublishers.com\/article\/Details\/DBS-045","DOI":"10.1561\/1900000045"},{"key":"22_CR14","doi-asserted-by":"publisher","unstructured":"Khayyat, Z., et al.: BigDansing: a system for big data cleansing. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, Melbourne, Victoria, Australia, pp. 1215\u20131230. ACM (2015). https:\/\/doi.org\/10.1145\/2723372.2747646. https:\/\/dl.acm.org\/doi\/10.1145\/2723372.2747646","DOI":"10.1145\/2723372.2747646"},{"key":"22_CR15","doi-asserted-by":"publisher","unstructured":"Kolahi, S., Lakshmanan, L.V.S.: On approximating optimum repairs for functional dependency violations. In: Proceedings of the 12th International Conference on Database Theory - ICDT 2009, St. Petersburg, Russia, p. 53. ACM Press (2009). https:\/\/doi.org\/10.1145\/1514894.1514901. http:\/\/portal.acm.org\/citation.cfm?doid=1514894.1514901","DOI":"10.1145\/1514894.1514901"},{"key":"22_CR16","doi-asserted-by":"crossref","unstructured":"Konda, P., et al.: Magellan: Toward Building Entity Matching Management Systems (2016)","DOI":"10.14778\/2994509.2994535"},{"key":"22_CR17","doi-asserted-by":"publisher","unstructured":"Mahdavi, M., Abedjan, Z.: Baran: effective error correction via a unified context representation and transfer learning. Proc. VLDB Endow. 13(12), 1948\u20131961 (2020). https:\/\/doi.org\/10.14778\/3407790.3407801. https:\/\/dl.acm.org\/doi\/10.14778\/3407790.3407801","DOI":"10.14778\/3407790.3407801"},{"key":"22_CR18","unstructured":"Mouelhi, A.E., J\u00e9gou, P., Terrioux, C., Zanuttini, B.: Sur la complexit\u00e9 des algorithmes de backtracking et quelques nouvelles classes polynomiales pour CSP (2012)"},{"key":"22_CR19","doi-asserted-by":"crossref","unstructured":"Nadjeh, N., Abdellaoui, S., Nader, F.: CSP-DC: data cleaning via constraint satisfaction problem solving. In: Proceedings of the 15th International Conference on Agents and Artificial Intelligence, Portugal, vol. 2, pp. 478\u2013488 (2023)","DOI":"10.5220\/0011897000003393"},{"key":"22_CR20","unstructured":"Nibel, N., Abdellaoui, S., Nader, F.: Bigcleaner: a new big data cleaning approach (under review). J. Data Inf. Qual. 1\u201325 (2023)"},{"key":"22_CR21","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1007\/3-540-44886-1_11","volume-title":"Advances in Artificial Intelligence","author":"W Pang","year":"2003","unstructured":"Pang, W., Goodwin, S.D.: A graph based backtracking algorithm for solving general CSPs. In: Xiang, Y., Chaib-draa, B. (eds.) Advances in Artificial Intelligence. LNCS, vol. 2671, pp. 114\u2013128. Springer, Heidelberg (2003). https:\/\/doi.org\/10.1007\/3-540-44886-1_11"},{"key":"22_CR22","doi-asserted-by":"crossref","unstructured":"Poole, D.L., Mackworth, A.K.: Artificial Intelligence: Foundations of Computational Agents. Cambridge University Press (2010). google-Books-ID: B7khAwAAQBAJ","DOI":"10.1017\/CBO9780511794797"},{"key":"22_CR23","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1007\/11754602_7","volume-title":"Recent Advances in Constraints","author":"I Razgon","year":"2006","unstructured":"Razgon, I.: Complexity analysis of heuristic CSP search algorithms. In: Hnich, B., Carlsson, M., Fages, F., Rossi, F. (eds.) CSCLP 2005. LNCS, vol. 3978, pp. 88\u201399. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11754602_7"},{"key":"22_CR24","doi-asserted-by":"publisher","unstructured":"Rekatsinas, T., Chu, X., Ilyas, I.F., R\u00e9, C.: HoloClean: holistic data repairs with probabilistic inference. Proc. VLDB Endow. 10(11), 1190\u20131201 (2017). https:\/\/doi.org\/10.14778\/3137628.3137631. https:\/\/dl.acm.org\/doi\/10.14778\/3137628.3137631","DOI":"10.14778\/3137628.3137631"},{"key":"22_CR25","doi-asserted-by":"publisher","unstructured":"Rezig, E.K., Ouzzani, M., Aref, W.G., Elmagarmid, A.K., Mahmood, A.R., Stonebraker, M.: Horizon: scalable dependency-driven data cleaning. Proc. VLDB Endow. 14(11), 2546\u20132554 (2021). https:\/\/doi.org\/10.14778\/3476249.3476301. https:\/\/dl.acm.org\/doi\/10.14778\/3476249.3476301","DOI":"10.14778\/3476249.3476301"},{"key":"22_CR26","doi-asserted-by":"publisher","unstructured":"Song, S., Zhu, H., Wang, J.: Constraint-variance tolerant data repairing. In: Proceedings of the 2016 International Conference on Management of Data, San Francisco, California, USA, pp. 877\u2013892. ACM (2016). https:\/\/doi.org\/10.1145\/2882903.2882955. https:\/\/dl.acm.org\/doi\/10.1145\/2882903.2882955","DOI":"10.1145\/2882903.2882955"},{"key":"22_CR27","doi-asserted-by":"crossref","unstructured":"Song, S., Zhu, H., Wang, J.: Constraint-variance tolerant data repairing. In: Proceedings of the 2016 International Conference on Management of Data, pp. 877\u2013892 (2016)","DOI":"10.1145\/2882903.2882955"},{"key":"22_CR28","doi-asserted-by":"publisher","unstructured":"Volkovs, M., Fei Chiang, Szlichta, J., Miller, R.J.: Continuous data cleaning. In: 2014 IEEE 30th International Conference on Data Engineering, Chicago, IL, pp. 244\u2013255. IEEE (2014). https:\/\/doi.org\/10.1109\/ICDE.2014.6816655. http:\/\/ieeexplore.ieee.org\/document\/6816655\/","DOI":"10.1109\/ICDE.2014.6816655"},{"key":"22_CR29","doi-asserted-by":"publisher","unstructured":"Chu, X., Ilyas, I.F., Papotti, P.: Holistic data cleaning: putting violations into context. In: 2013 IEEE 29th International Conference on Data Engineering (ICDE), Brisbane, QLD, pp. 458\u2013469. IEEE (2013). https:\/\/doi.org\/10.1109\/ICDE.2013.6544847. http:\/\/ieeexplore.ieee.org\/document\/6544847\/","DOI":"10.1109\/ICDE.2013.6544847"},{"key":"22_CR30","doi-asserted-by":"crossref","unstructured":"Yakout, M., Elmagarmid, A.K., Neville, J., Ouzzani, M., Ilyas, I.F.: Guided data repair. arXiv preprint arXiv:1103.3103 (2011)","DOI":"10.14778\/1952376.1952378"}],"container-title":["Lecture Notes in Computer Science","Agents and Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-55326-4_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,14]],"date-time":"2024-03-14T08:50:11Z","timestamp":1710406211000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-55326-4_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031553257","9783031553264"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-55326-4_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"15 March 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICAART","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Agents and Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lisbon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 February 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 February 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icaart2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icaart.scitevents.org\/?y=2023","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"PRIMORIS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"306","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"23","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"111","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"8% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}