{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T14:27:05Z","timestamp":1776090425379,"version":"3.50.1"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030461324","type":"print"},{"value":"9783030461331","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-46133-1_44","type":"book-chapter","created":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T22:03:00Z","timestamp":1745964180000},"page":"735-751","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Automated Data Transformation with Inductive Programming and Dynamic Background Knowledge"],"prefix":"10.1007","author":[{"given":"Lidia","family":"Contreras-Ochando","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"C\u00e8sar","family":"Ferri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jos\u00e9","family":"Hern\u00e1ndez-Orallo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fernando","family":"Mart\u00ednez-Plumed","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mar\u00eda Jos\u00e9","family":"Ram\u00edrez-Quintana","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Susumu","family":"Katayama","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,4,30]]},"reference":[{"key":"44_CR1","unstructured":"Bhupatiraju, S., Singh, R., Mohamed, A.r., Kohli, P.: Deep API programmer: learning to program with APIs. arXiv preprint arXiv:1704.04327 (2017)"},{"key":"44_CR2","doi-asserted-by":"publisher","unstructured":"Contreras-Ochando, L.: DataWrangling-DSI: BETA - Extended Results (2019). https:\/\/doi.org\/10.5281\/zenodo.2557385","DOI":"10.5281\/zenodo.2557385"},{"key":"44_CR3","unstructured":"Contreras-Ochando, L., Ferri, C., Hern\u00e1ndez-Orallo, J., Mart\u00ednez-Plumed, F., Ram\u00edrez-Quintana, M.J., Katayama, S.: General-purpose declarative inductive programming with domain-specific background knowledge for data wrangling automation. arXiv preprint arXiv:1809.10054 (2018)"},{"key":"44_CR4","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1007\/978-3-319-40566-7_4","volume-title":"Inductive Logic Programming","author":"A Cropper","year":"2016","unstructured":"Cropper, A., Tamaddoni-Nezhad, A., Muggleton, S.H.: Meta-interpretive learning of data transformation programs. In: Inoue, K., Ohwada, H., Yamamoto, A. (eds.) ILP 2015. LNCS (LNAI), vol. 9575, pp. 46\u201359. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-40566-7_4"},{"key":"44_CR5","unstructured":"Devlin, J., Bunel, R.R., Singh, R., Hausknecht, M., Kohli, P.: Neural program meta-induction. In: NIPS, pp. 2077\u20132085 (2017)"},{"key":"44_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1007\/3-540-44716-4_15","volume-title":"Functional and Logic Programming","author":"C Ferri-Ram\u00edrez","year":"2001","unstructured":"Ferri-Ram\u00edrez, C., Hern\u00e1ndez-Orallo, J., Ram\u00edrez-Quintana, M.J.: Incremental learning of functional logic programs. In: Kuchen, H., Ueda, K. (eds.) FLOPS 2001. LNCS, vol. 2024, pp. 233\u2013247. Springer, Heidelberg (2001). https:\/\/doi.org\/10.1007\/3-540-44716-4_15"},{"issue":"1","key":"44_CR7","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1007\/s10462-009-9108-7","volume":"29","author":"P Flener","year":"2008","unstructured":"Flener, P., Schmid, U.: An introduction to inductive programming. Artif. Intell. Rev. 29(1), 45\u201362 (2008)","journal-title":"Artif. Intell. Rev."},{"key":"44_CR8","doi-asserted-by":"crossref","unstructured":"Gulwani, S.: Automating string processing in spreadsheets using input-output examples. In: Proceedings of the 38th Principles of Programming Languages, pp. 317\u2013330 (2011)","DOI":"10.1145\/1926385.1926423"},{"issue":"8","key":"44_CR9","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1145\/2240236.2240260","volume":"55","author":"S Gulwani","year":"2012","unstructured":"Gulwani, S., Harris, W.R., Singh, R.: Spreadsheet data manipulation using examples. Commun. ACM 55(8), 97\u2013105 (2012)","journal-title":"Commun. ACM"},{"issue":"11","key":"44_CR10","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1145\/2736282","volume":"58","author":"S Gulwani","year":"2015","unstructured":"Gulwani, S., Hernandez-Orallo, J., Kitzelmann, E., Muggleton, S.H., Schmid, U., Zorn, B.: Inductive programming meets the real world. Commun. ACM 58(11), 90\u201399 (2015)","journal-title":"Commun. ACM"},{"issue":"10","key":"44_CR11","doi-asserted-by":"publisher","first-page":"1165","DOI":"10.14778\/3231751.3231766","volume":"11","author":"Y He","year":"2018","unstructured":"He, Y., Chu, X., Ganjam, K., Zheng, Y., Narasayya, V., Chaudhuri, S.: Transform-data-by-example (TDE): an extensible search engine for data transformations. Proc. VLDB Endow. 11(10), 1165\u20131177 (2018)","journal-title":"Proc. VLDB Endow."},{"key":"44_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1007\/978-3-642-11931-6_4","volume-title":"Approaches and Applications of Inductive Programming","author":"R Henderson","year":"2010","unstructured":"Henderson, R.: Incremental learning in inductive programming. In: Schmid, U., Kitzelmann, E., Plasmeijer, R. (eds.) AAIP 2009. LNCS, vol. 5812, pp. 74\u201392. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-11931-6_4"},{"key":"44_CR13","doi-asserted-by":"crossref","unstructured":"Kandel, S., Paepcke, A., Hellerstein, J., Heer, J.: Wrangler: interactive visual specification of data transformation scripts. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 3363\u20133372. ACM (2011)","DOI":"10.1145\/1978942.1979444"},{"issue":"4","key":"44_CR14","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1177\/1473871611415994","volume":"10","author":"S Kandel","year":"2011","unstructured":"Kandel, S., et al.: Research directions in data wrangling: visualizations and transformations for usable and credible data. Inf. Vis. 10(4), 271\u2013288 (2011)","journal-title":"Inf. Vis."},{"key":"44_CR15","doi-asserted-by":"crossref","unstructured":"Katayama, S.: An analytical inductive functional programming system that avoids unintended programs. In: Proceedings of the ACM SIGPLAN 2012 Workshop on Partial Evaluation and Program Manipulation PEPM, pp. 43\u201352. ACM (2012)","DOI":"10.1145\/2103746.2103758"},{"key":"44_CR16","unstructured":"Kietz, J.U., Wrobel, S.: Controlling the complexity of learning in logic through syntactic and task-oriented models. In: Inductive Logic Programming. Citeseer (1992)"},{"key":"44_CR17","unstructured":"Menon, A., Tamuz, O., Gulwani, S., Lampson, B., Kalai, A.: A machine learning framework for programming by example. In: ICML, pp. 187\u2013195 (2013)"},{"issue":"5","key":"44_CR18","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1145\/3191513","volume":"61","author":"T Mitchell","year":"2018","unstructured":"Mitchell, T., et al.: Never-ending learning. Commun. ACM 61(5), 103\u2013115 (2018)","journal-title":"Commun. ACM"},{"key":"44_CR19","unstructured":"Mitchell, T.M.: The need for biases in learning generalizations. Rutgers Univ., New Jersey (1980)"},{"key":"44_CR20","unstructured":"Mitchell, T.M., et al.: Theo: a framework for self-improving systems. In: Architectures for Intelligence: The Twenty-Second Carnegie Mellon Symposium on Congnition, pp. 323\u2013355 (1991)"},{"key":"44_CR21","unstructured":"Parisotto, E., Mohamed, A.r., Singh, R., Li, L., Zhou, D., Kohli, P.: Neuro-symbolic program synthesis. arXiv preprint arXiv:1611.01855 (2016)"},{"key":"44_CR22","doi-asserted-by":"crossref","unstructured":"Shu, C., Zhang, H.: Neural programming by example. In: AAAI, pp. 1539\u20131545 (2017)","DOI":"10.1609\/aaai.v31i1.10734"},{"key":"44_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"398","DOI":"10.1007\/978-3-319-21690-4_23","volume-title":"Computer Aided Verification","author":"R Singh","year":"2015","unstructured":"Singh, R., Gulwani, S.: Predicting a correct program in programming by example. In: Kroening, D., P\u0103s\u0103reanu, C.S. (eds.) CAV 2015. LNCS, vol. 9206, pp. 398\u2013414. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-21690-4_23"},{"key":"44_CR24","doi-asserted-by":"crossref","unstructured":"Singh, R., Gulwani, S.: Transforming spreadsheet data types using examples. In: Proceedings of the 43rd Principles of Programming Languages, pp. 343\u2013356 (2016)","DOI":"10.1145\/2837614.2837668"},{"key":"44_CR25","first-page":"369","volume":"4","author":"A Srinivasan","year":"2003","unstructured":"Srinivasan, A., King, R.D., Bain, M.E.: An empirical study of the use of relevance information in inductive logic programming. JMLR 4, 369\u2013383 (2003)","journal-title":"JMLR"},{"key":"44_CR26","doi-asserted-by":"crossref","unstructured":"Wu, B., Szekely, P., Knoblock, C.A.: Learning data transformation rules through examples: preliminary results. In: Information Integration on the Web, p. 8 (2012)","DOI":"10.1145\/2331801.2331809"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-46133-1_44","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T22:03:08Z","timestamp":1745964188000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-46133-1_44"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030461324","9783030461331"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-46133-1_44","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"30 April 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"W\u00fcrzburg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ecmlpkdd2019.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"733","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":"130","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":"0","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":"18% - 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":"3.04","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":"5.3","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"ECML PKDD Workshops Information: single-blind review, submissions: 200, full papers accepted: 70, short papers accepted: 46","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}