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Awad, \u201cCorrelating unlabeled events at runtime,\u201d arXiv\npreprint https:\/\/arxiv.org\/abs\/2004.09971, 2020."},{"key":"ref5","unstructured":"E. Brzychczy, T. Pe\u0142ech-Pilichowski, and Z. Dworakowski, \u201cCase id\ndetection based on time series data \u2013 the mining use case,\u201d 2024.\n[Online]. Available: https:\/\/arxiv.org\/abs\/2410.23846"},{"key":"ref6","doi-asserted-by":"crossref","unstructured":"R. Andrews, C. G. van Dun, M. T. Wynn, W. Kratsch, M. K. R\u00f6glinger,\nand A. H. ter Hofstede, \u201cQuality-informed semi-automated event log\ngeneration for process mining,\u201d Decision Support Systems, vol. 132, p.\n113265, 2020.","DOI":"10.1016\/j.dss.2020.113265"},{"key":"ref7","doi-asserted-by":"crossref","unstructured":"D. Sanchez-Charles, J. Carmona, and R. 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M. van der Aalst, \u201cActivity mining\nby global trace segmentation,\u201d in Business Process Management Workshops: BPM 2009 International Workshops, Ulm, Germany, September\n7, 2009. Revised Papers 7. Springer, 2010, pp. 128\u2013139.","DOI":"10.1007\/978-3-642-12186-9_13"},{"key":"ref11","doi-asserted-by":"crossref","unstructured":"N. Tax, N. Sidorova, R. Haakma, and W. M. van der Aalst, \u201cEvent\nabstraction for process mining using supervised learning techniques,\u201d\nin Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016:\nVolume 1. Springer, 2018, pp. 251\u2013269.","DOI":"10.1007\/978-3-319-56994-9_18"},{"key":"ref12","doi-asserted-by":"crossref","unstructured":"N. Martin, B. Depaire, and A. Caris, \u201cThe use of process mining in\na business process simulation context: Overview and challenges,\u201d in\n2014 IEEE Symposium on Computational Intelligence and Data Mining\n(CIDM). IEEE, 2014, pp. 381\u2013388.","DOI":"10.1109\/CIDM.2014.7008693"},{"key":"ref13","doi-asserted-by":"crossref","unstructured":"W. M. van der Aalst, \u201cObject-centric process mining: dealing with\ndivergence and convergence in event data,\u201d in Software Engineering\nand Formal Methods: 17th International Conference, SEFM 2019, Oslo,\nNorway, September 18\u201320, 2019, Proceedings 17. Springer, 2019, pp.\n3\u201325.","DOI":"10.1007\/978-3-030-30446-1_1"},{"key":"ref14","doi-asserted-by":"crossref","unstructured":"W. M. van der Aalst and A. Berti, \u201cDiscovering object-centric Petri\nnets,\u201d Fundamenta informaticae, vol. 175, no. 1-4, pp. 1\u201340, 2020.","DOI":"10.3233\/FI-2020-1946"},{"key":"ref15","doi-asserted-by":"crossref","unstructured":"A. F. Ghahfarokhi, G. Park, A. Berti, and W. M. van der Aalst, \u201cOcel:\na standard for object-centric event logs,\u201d in European Conference on\nAdvances in Databases and Information Systems. Springer, 2021, pp.\n169\u2013175.","DOI":"10.1007\/978-3-030-85082-1_16"},{"key":"ref16","doi-asserted-by":"crossref","unstructured":"M. J. Jans, M. Alles, and M. A. Vasarhelyi, \u201cProcess mining of event\nlogs in auditing: Opportunities and challenges,\u201d International Journal of\nAccounting Information Systems, vol. 12, no. 1, pp. 1\u201320, 2011.","DOI":"10.2139\/ssrn.2488737"},{"key":"ref17","doi-asserted-by":"crossref","unstructured":"A. Burattin, A. Sperduti, and W. M. van der Aalst, \u201cControl-flow\ndiscovery from event streams,\u201d in Proceedings of the IEEE Congress\non Evolutionary Computation (CEC), 2014, pp. 2420\u20132427.","DOI":"10.1109\/CEC.2014.6900341"},{"key":"ref18","unstructured":"D. Fahland, \u201cExtracting and pre-processing event logs,\u201d CoRR, vol.\nabs\/2211.04338, 2022."},{"key":"ref19","doi-asserted-by":"crossref","unstructured":"W. M. van der Aalst, A. J. Weijters, and L. Maruster, \u201cWorkflow mining:\nDiscovering process models from event logs,\u201d IEEE Transactions on\nKnowledge and Data Engineering, vol. 16, no. 9, pp. 1128\u20131142, 2004.","DOI":"10.1109\/TKDE.2004.47"},{"key":"ref20","unstructured":"A. J. Weijters, W. M. van der Aalst, and A. de Medeiros, \u201cProcess\nmining with the heuristics miner algorithm,\u201d in Technical Report WP,\nvol. 166, 2003, pp. 1\u201334."},{"key":"ref21","unstructured":"S. J. van Zelst and W. M. van der Aalst, \u201cLog skeletons: A classification approach to process discovery,\u201d in International Conference on\nAdvanced Information Systems Engineering (CAiSE). Springer, 2018,\npp. 309\u2013324."},{"key":"ref22","doi-asserted-by":"crossref","unstructured":"A. Rozinat and W. M. van der Aalst, \u201cConformance checking of\nprocesses based on monitoring real behavior,\u201d Information Systems,\nvol. 33, no. 1, pp. 64\u201395, 2008.","DOI":"10.1016\/j.is.2007.07.001"},{"key":"ref23","doi-asserted-by":"crossref","unstructured":"A. Ilyas, J. M. da Trindade, R. Castro Fernandez, and S. Madden,\n\u201cExtracting syntactical patterns from databases,\u201d in Proceedings of the\n2018 International Conference on Management of Data (SIGMOD).\nACM, 2018, pp. 1773\u20131788.","DOI":"10.1109\/ICDE.2018.00014"},{"key":"ref24","unstructured":"A. Burattin and W. M. van der Aalst, \u201cPLG2: Multiperspective process\nrandomization with online and offline simulations,\u201d in International\nConference on Business Process Management. 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