{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T16:03:46Z","timestamp":1781021026979,"version":"3.54.1"},"reference-count":55,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/100008522","name":"Research Open Access Publishing Fund of the University of Illinois at Chicago","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100008522","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2020]]},"DOI":"10.1109\/access.2020.3033450","type":"journal-article","created":{"date-parts":[[2020,10,23]],"date-time":"2020-10-23T19:34:43Z","timestamp":1603481683000},"page":"194410-194427","source":"Crossref","is-referenced-by-count":11,"title":["Adversarial System Variant Approximation to Quantify Process Model Generalization"],"prefix":"10.1109","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0954-0938","authenticated-orcid":false,"given":"Julian","family":"Theis","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7881-6542","authenticated-orcid":false,"given":"Houshang","family":"Darabi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","author":"ross","year":"2014","journal-title":"Markov Chains"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.2307\/2684568"},{"key":"ref33","first-page":"7299","article-title":"Relational recurrent neural networks","author":"santoro","year":"2018","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref32","first-page":"1","article-title":"Neural machine translation by jointly learning to align and translate","author":"bahdanau","year":"2015","journal-title":"Proc 3rd Int Conf Learn Represent (ICLR)"},{"key":"ref31","article-title":"Neural text generation: Past, present and beyond","author":"lu","year":"2018","journal-title":"arXiv 1803 07133"},{"key":"ref30","first-page":"1","article-title":"Maskgan: Better text generation via filling in the _","author":"fedus","year":"2018","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/57.1.97"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1063\/1.1699114"},{"key":"ref35","article-title":"The relativistic discriminator: A key element missing from standard GAN","author":"jolicoeur-martineau","year":"2018","journal-title":"arXiv 1807 00734"},{"key":"ref34","first-page":"5998","article-title":"Attention is all you need","author":"vaswani","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref28","first-page":"1","article-title":"Relgan: Relational generative adversarial networks for text generation","author":"nie","year":"2019","journal-title":"Proc Int Conf Learn Represent (ICLR)"},{"key":"ref27","first-page":"1","volume":"166","author":"weijters","year":"2006","journal-title":"Process Mining with the Heuristics Miner-Algorithm"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3210080"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/COASE.2019.8842900"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-16071-9"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2937085"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/5.24143"},{"key":"ref21","first-page":"55","author":"van der aalst","year":"2016","journal-title":"Process Modeling and Analysis"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-018-1214-x"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2841877"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2017.04.005"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/s00165-010-0161-4"},{"key":"ref50","first-page":"247","article-title":"Regularization for deep learning","author":"goodfellow","year":"2016","journal-title":"Deep Learning"},{"key":"ref51","first-page":"265","article-title":"Tensorflow: A system for large-scale machine learning","author":"abadi","year":"2016","journal-title":"Proc of USENIX Symp on Operating Systems Design and Implementation (OSDI)"},{"key":"ref55","first-page":"111","article-title":"A tool for generating event logs from multi-perspective declare models","author":"skydanienko","year":"2018","journal-title":"Proc BPM (Dissertation\/Demos\/Ind )"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4612-4380-9_16"},{"key":"ref53","volume":"2","author":"mcdonald","year":"2009","journal-title":"Handbook of Biological Statistics"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/52.3-4.591"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/s12599-018-0567-8"},{"key":"ref11","first-page":"2672","article-title":"Generative adversarial nets","author":"goodfellow","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref40","first-page":"6345","article-title":"Metropolis-hastings generative adversarial networks","author":"turner","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00453"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/3188745.3232194"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1142\/S0218843014400012"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1045"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2013.130"},{"key":"ref17","article-title":"Flexible evolutionary algorithms for mining structured process models","author":"buijs","year":"2014"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-45348-4_5"},{"key":"ref19","first-page":"125","author":"van der aalst","year":"2016","journal-title":"Getting the Dataset"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/3331155"},{"key":"ref3","first-page":"124","article-title":"Process mining and robotic process automation: A perfect match","author":"geyer-klingeberg","year":"2018","journal-title":"Proc BPM (Dissertation\/Demos\/Ind )"},{"key":"ref6","first-page":"243","author":"van der aalst","year":"2016","journal-title":"Conformance checking"},{"key":"ref5","article-title":"Automated discovery of business process simulation models from event logs","author":"camargo","year":"2019","journal-title":"arXiv 1910 05404"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-60651-3_8"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-45348-4_3"},{"key":"ref49","first-page":"13","article-title":"Process mining for Python (PM4PY): Bridging the gap between process-and data science","author":"berti","year":"2019","journal-title":"Proc 1st Int Conf Process Mining (ICPM)"},{"key":"ref9","first-page":"256","article-title":"Process mining and the black swan: An empirical analysis of the influence of unobserved behavior on the quality of mined process models","author":"rehse","year":"2017","journal-title":"Proc Int Conf Bus Process Manage"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-15618-2_16"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2007.07.001"},{"key":"ref48","article-title":"Aligning observed and modeled behavior","author":"adriansyah","year":"2014"},{"key":"ref47","first-page":"87","article-title":"Alignment-based metrics in conformance checking (Summary)","author":"van dongen","year":"2016","journal-title":"Proc 7th Int Workshop Enterprise Modeling Inf Syst Archit (EMISA)"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2017.06.002"},{"key":"ref41","first-page":"1","article-title":"Generative multi-adversarial networks","author":"durugkar","year":"2019","journal-title":"Proc 5th Int Conf Learn Represent (ICLR)"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2012.02.004"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/s12599-018-0541-5"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8948470\/09237923.pdf?arnumber=9237923","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T01:09:01Z","timestamp":1641949741000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9237923\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":55,"URL":"https:\/\/doi.org\/10.1109\/access.2020.3033450","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]}}}