{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T04:27:39Z","timestamp":1743049659769,"version":"3.40.3"},"publisher-location":"Cham","reference-count":42,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031703430"},{"type":"electronic","value":"9783031703447"}],"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\/978-3-031-70344-7_17","type":"book-chapter","created":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T08:02:43Z","timestamp":1724918563000},"page":"285-302","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Data is Moody: Discovering Data Modification Rules from Process Event Logs"],"prefix":"10.1007","author":[{"given":"Marco Bjarne","family":"Schuster","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Boris","family":"Wiegand","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jilles","family":"Vreeken","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,8,22]]},"reference":[{"key":"17_CR1","unstructured":"Agrawal, R., Srikant, R.: Mining sequential patterns. In: ICDE, pp. 3\u201314, Los Alamitos, CA, USA, 1995. IEEE Computer Society (1995)"},{"key":"17_CR2","doi-asserted-by":"crossref","unstructured":"Augusto, A., Conforti, R., Dumas, M., La\u00a0Rosa, M.: Split Miner: Discovering accurate and simple business process models from event logs. In: ICDM, pp. 1\u201310 (2017)","DOI":"10.1109\/ICDM.2017.9"},{"issue":"1","key":"17_CR3","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1109\/TNNLS.2013.2278313","volume":"25","author":"RJC Bose","year":"2013","unstructured":"Bose, R.J.C., Van Der Aalst, W.M., \u017dliobait\u0117, I., Pechenizkiy, M.: Dealing with concept drifts in process mining. IEEE Trans. Neural Netw. Learn. Syst. 25(1), 154\u2013171 (2013)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"17_CR4","doi-asserted-by":"crossref","unstructured":"Budhathoki, K., Boley, M., Vreeken, J.: Discovering reliable causal rules. In: SDM, pp. 1\u20139 (2021)","DOI":"10.1137\/1.9781611976700.1"},{"key":"17_CR5","unstructured":"Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms, 3rd edn. The MIT Press (2009)"},{"key":"17_CR6","doi-asserted-by":"crossref","unstructured":"C\u00fcppers, J., Krieger, P., Vreeken, J.: Discovering sequential patterns with predictable inter-event delays. In: AAAI, vol. 38, pp. 8346\u20138353 (2024)","DOI":"10.1609\/aaai.v38i8.28676"},{"issue":"2","key":"17_CR7","doi-asserted-by":"publisher","first-page":"278","DOI":"10.2307\/2981683","volume":"147","author":"AP Dawid","year":"1984","unstructured":"Dawid, A.P.: Present position and potential developments: some personal views - statistical theory: the prequential approach. J. R. Statist. Soc. A 147(2), 278\u2013292 (1984)","journal-title":"J. R. Statist. Soc. A"},{"key":"17_CR8","doi-asserted-by":"publisher","unstructured":"de Leoni, M., Mannhardt, F.: Road traffic fine management process (2015). https:\/\/doi.org\/10.4121\/uuid:270fd440-1057-4fb9-89a9-b699b47990f5","DOI":"10.4121\/uuid:270fd440-1057-4fb9-89a9-b699b47990f5"},{"key":"17_CR9","doi-asserted-by":"crossref","unstructured":"Fischer, J., Vreeken, J.: Differentiable pattern set mining. In: KDD, pp. 383\u2013392 (2021)","DOI":"10.1145\/3447548.3467348"},{"key":"17_CR10","doi-asserted-by":"crossref","unstructured":"Foster, M., Derrick, J., Walkinshaw, N.: Reverse-engineering EFSMs with data dependencies. In ICTSS, pp. 37\u201354 (2021)","DOI":"10.1007\/978-3-031-04673-5_3"},{"issue":"5","key":"17_CR11","doi-asserted-by":"publisher","first-page":"1679","DOI":"10.1007\/s10618-022-00846-z","volume":"36","author":"E Galbrun","year":"2022","unstructured":"Galbrun, E.: The minimum description length principle for pattern mining: a survey. Data Min. Knowl. Disc. 36(5), 1679\u20131727 (2022)","journal-title":"Data Min. Knowl. Disc."},{"key":"17_CR12","doi-asserted-by":"crossref","unstructured":"Gr\u00fcnwald, P.: The Minimum Description Length Principle. MIT Press (2007)","DOI":"10.7551\/mitpress\/4643.001.0001"},{"key":"17_CR13","doi-asserted-by":"crossref","unstructured":"Hlupic, V., Robinson, S.: Business process modelling and analysis using discrete-event simulation. In: WSC, pp. 1363\u20131369 (1998)","DOI":"10.1109\/WSC.1998.746003"},{"key":"17_CR14","unstructured":"Krismayer, T.: Automatic Mining of Constraints for Event-based Systems Monitoring. Ph.D. thesis, Johannes Kepler University Linz (2020)"},{"key":"17_CR15","doi-asserted-by":"publisher","unstructured":"Mannhardt, F.: Sepsis cases - event log (2016). https:\/\/doi.org\/10.4121\/uuid:915d2bfb-7e84-49ad-a286-dc35f063a460","DOI":"10.4121\/uuid:915d2bfb-7e84-49ad-a286-dc35f063a460"},{"key":"17_CR16","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1007\/s00607-015-0441-1","volume":"98","author":"F Mannhardt","year":"2016","unstructured":"Mannhardt, F., De Leoni, M., Reijers, H.A., Van Der Aalst, W.M.: Balanced multi-perspective checking of process conformance. Computing 98, 407\u2013437 (2016)","journal-title":"Computing"},{"key":"17_CR17","unstructured":"Mannila, H., Toivonen, H., Verkamo, A.I.: Discovering frequent episodes in sequences. In: KDD, pp. 210\u2013215 (1995)"},{"issue":"3","key":"17_CR18","doi-asserted-by":"publisher","first-page":"1277","DOI":"10.1007\/s10115-018-1286-7","volume":"60","author":"A Marx","year":"2019","unstructured":"Marx, A., Vreeken, J.: Telling cause from effect by local and global regression. Knowl. Inf. Syst. 60(3), 1277\u20131305 (2019)","journal-title":"Knowl. Inf. Syst."},{"key":"17_CR19","unstructured":"Marx, A., Vreeken, J.: Formally justifying MDL-based inference of cause and effect. In: ITCI (2022)"},{"key":"17_CR20","doi-asserted-by":"crossref","unstructured":"Mozafari\u00a0Mehr, A.S., de\u00a0Carvalho, R.M., van Dongen, B.: Detecting privacy, data and control-flow deviations in business processes. In: CAiSE, pp. 82\u201391 (2021)","DOI":"10.1007\/978-3-030-79108-7_10"},{"key":"17_CR21","doi-asserted-by":"crossref","unstructured":"Nolle, T., Seeliger, A., M\u00fchlh\u00e4user, M.: Binet: multivariate business process anomaly detection using deep learning. In: BPM, pp. 271\u2013287 (2018)","DOI":"10.1007\/978-3-319-98648-7_16"},{"issue":"11","key":"17_CR22","first-page":"1424","volume":"16","author":"J Pei","year":"2004","unstructured":"Pei, J., et al.: Mining sequential patterns by pattern-growth: the prefixspan approach. IEEE TKDE 16(11), 1424\u20131440 (2004)","journal-title":"IEEE TKDE"},{"key":"17_CR23","doi-asserted-by":"crossref","unstructured":"Petitjean, F., Li, T., Tatti, N., Webb, G.: Skopus: mining top-k sequential patterns under leverage. Data Min. Knowl. Disc. 30 (2016)","DOI":"10.1007\/s10618-016-0467-9"},{"issue":"5","key":"17_CR24","doi-asserted-by":"publisher","first-page":"1885","DOI":"10.1007\/s10618-022-00856-x","volume":"36","author":"HM Proen\u00e7a","year":"2022","unstructured":"Proen\u00e7a, H.M., Gr\u00fcnwald, P., B\u00e4ck, T., van Leeuwen, M.: Robust subgroup discovery. Data Min. Knowl. Disc. 36(5), 1885\u20131970 (2022)","journal-title":"Data Min. Knowl. Disc."},{"key":"17_CR25","first-page":"1372","volume":"512","author":"HM Proen\u00e7a","year":"2020","unstructured":"Proen\u00e7a, H.M., van Leeuwen, M.: Interpretable multiclass classification by MDL-based rule lists. JIS 512, 1372\u20131393 (2020)","journal-title":"JIS"},{"issue":"1","key":"17_CR26","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1016\/0005-1098(78)90005-5","volume":"14","author":"J Rissanen","year":"1978","unstructured":"Rissanen, J.: Modeling by shortest data description. Automatica 14(1), 465\u2013471 (1978)","journal-title":"Automatica"},{"issue":"2","key":"17_CR27","doi-asserted-by":"publisher","first-page":"416","DOI":"10.1214\/aos\/1176346150","volume":"11","author":"J Rissanen","year":"1983","unstructured":"Rissanen, J.: A universal prior for integers and estimation by minimum description length. Ann. Stat. 11(2), 416\u2013431 (1983)","journal-title":"Ann. Stat."},{"key":"17_CR28","first-page":"629","volume":"30","author":"J Rissanen","year":"1984","unstructured":"Rissanen, J.: Universal coding, information, prediction, and estimation. IEEE TIT 30, 629\u2013636 (1984)","journal-title":"IEEE TIT"},{"issue":"1","key":"17_CR29","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1016\/0012-365X(92)90155-9","volume":"105","author":"V Rodionov","year":"1992","unstructured":"Rodionov, V.: On the number of labeled acyclic digraphs. Discret. Math. 105(1), 319\u2013321 (1992)","journal-title":"Discret. Math."},{"issue":"9","key":"17_CR30","first-page":"1","volume":"54","author":"DMV Sato","year":"2021","unstructured":"Sato, D.M.V., De Freitas, S.C., Barddal, J.P., Scalabrin, E.E.: A survey on concept drift in process mining. ACM CSUR 54(9), 1\u201338 (2021)","journal-title":"ACM CSUR"},{"key":"17_CR31","doi-asserted-by":"crossref","unstructured":"Sch\u00f6nig, S., Di\u00a0Ciccio, C., Maggi, F.M., Mendling, J.: Discovery of multi-perspective declarative process models. In: ICSOC, pp. 87\u2013103 (2016)","DOI":"10.1007\/978-3-319-46295-0_6"},{"key":"17_CR32","doi-asserted-by":"crossref","unstructured":"Sommers, D., Menkovski, V., Fahland, D.: Process discovery using graph neural networks. In: ICPM, pp. 40\u201347 (2021)","DOI":"10.1109\/ICPM53251.2021.9576849"},{"key":"17_CR33","doi-asserted-by":"crossref","unstructured":"Tatti, N.: Significance of episodes based on minimal windows. In: ICDM, pp. 513\u2013522 (2009)","DOI":"10.1109\/ICDM.2009.23"},{"key":"17_CR34","doi-asserted-by":"crossref","unstructured":"Tatti, N., Cule, B.: Mining closed episodes with simultaneous events. In: KDD, pp. 1172\u20131180 (2011)","DOI":"10.1145\/2020408.2020589"},{"key":"17_CR35","doi-asserted-by":"crossref","unstructured":"Tatti, N., Cule, B.: Mining closed strict episodes. Data Min. Knowl. Disc. (2011)","DOI":"10.1109\/ICDM.2010.89"},{"key":"17_CR36","doi-asserted-by":"crossref","unstructured":"Tatti, N., Vreeken, J.: The long and the short of it: summarizing event sequences with serial episodes. In: KDD, pp. 462\u2013470. ACM (2012)","DOI":"10.1145\/2339530.2339606"},{"key":"17_CR37","doi-asserted-by":"crossref","unstructured":"Taymouri, F., La\u00a0Rosa, M., Erfani, S.: A deep adversarial model for suffix and remaining time prediction of event sequences. In: SDM, pp. 522\u2013530 (2021)","DOI":"10.1137\/1.9781611976700.59"},{"key":"17_CR38","doi-asserted-by":"publisher","unstructured":"van\u00a0der Aalst, W.: Process Mining: Data Science in Action, 2nd edn. Springer, Heidelberg (2016). https:\/\/doi.org\/10.1007\/978-3-662-49851-4","DOI":"10.1007\/978-3-662-49851-4"},{"key":"17_CR39","doi-asserted-by":"crossref","unstructured":"Walkinshaw, N., Hall, M.: Inferring computational state machine models from program executions. In: ICSME (2016)","DOI":"10.1109\/ICSME.2016.74"},{"key":"17_CR40","doi-asserted-by":"crossref","unstructured":"Wiegand, B., Klakow, D., Vreeken, J.: Discovering interpretable data-to-sequence generators. In: AAAI, pp. 4237\u20134244 (2022)","DOI":"10.1609\/aaai.v36i4.20343"},{"key":"17_CR41","doi-asserted-by":"crossref","unstructured":"Yang, L., van Leeuwen, M.: Truly unordered probabilistic rule sets for multi-class classification. In: ECML PKDD, pp. 87\u2013103 (2022)","DOI":"10.1007\/978-3-031-26419-1_6"},{"issue":"1\u20132","key":"17_CR42","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1023\/A:1007652502315","volume":"42","author":"MJ Zaki","year":"2001","unstructured":"Zaki, M.J.: SPADE: an efficient algorithm for mining frequent sequences. Mach. Learn. 42(1\u20132), 31\u201360 (2001)","journal-title":"Mach. Learn."}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases. Research Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-70344-7_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T08:09:02Z","timestamp":1724918942000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-70344-7_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031703430","9783031703447"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-70344-7_17","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":"22 August 2024","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":"Vilnius","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lithuania","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2024.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}