{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T20:22:38Z","timestamp":1771014158489,"version":"3.50.1"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,5,20]],"date-time":"2025-05-20T00:00:00Z","timestamp":1747699200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,5,20]],"date-time":"2025-05-20T00:00:00Z","timestamp":1747699200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"PNRR project FAIR - Future AI Research","award":["PE0000001"],"award-info":[{"award-number":["PE0000001"]}]},{"name":"PNRR project FAIR - Future AI Research","award":["PE0000001"],"award-info":[{"award-number":["PE0000001"]}]},{"name":"PNRR project FAIR - Future AI Research","award":["PE0000001"],"award-info":[{"award-number":["PE0000001"]}]},{"name":"PNRR project FAIR - Future AI Research","award":["PE0000001"],"award-info":[{"award-number":["PE0000001"]}]},{"name":"PNRR project FAIR - Future AI Research","award":["PE0000001"],"award-info":[{"award-number":["PE0000001"]}]},{"name":"PNRR project FAIR - Future AI Research","award":["PE0000001"],"award-info":[{"award-number":["PE0000001"]}]},{"name":"PNRR project FAIR - Future AI Research","award":["PE0000001"],"award-info":[{"award-number":["PE0000001"]}]},{"name":"PNRR project FAIR - Future AI Research","award":["PE0000001"],"award-info":[{"award-number":["PE0000001"]}]},{"name":"PRIN project PINPOINT","award":["rot. 2020FNEB27, CUP H23C22000280006 and H45E2100021000"],"award-info":[{"award-number":["rot. 2020FNEB27, CUP H23C22000280006 and H45E2100021000"]}]},{"name":"PRIN project PINPOINT","award":["rot. 2020FNEB27, CUP H23C22000280006 and H45E2100021000"],"award-info":[{"award-number":["rot. 2020FNEB27, CUP H23C22000280006 and H45E2100021000"]}]},{"name":"PRIN project PINPOINT","award":["rot. 2020FNEB27, CUP H23C22000280006 and H45E2100021000"],"award-info":[{"award-number":["rot. 2020FNEB27, CUP H23C22000280006 and H45E2100021000"]}]},{"name":"PRIN project PINPOINT","award":["rot. 2020FNEB27, CUP H23C22000280006 and H45E2100021000"],"award-info":[{"award-number":["rot. 2020FNEB27, CUP H23C22000280006 and H45E2100021000"]}]},{"name":"PRIN project PINPOINT","award":["rot. 2020FNEB27, CUP H23C22000280006 and H45E2100021000"],"award-info":[{"award-number":["rot. 2020FNEB27, CUP H23C22000280006 and H45E2100021000"]}]},{"name":"PRIN project PINPOINT","award":["rot. 2020FNEB27, CUP H23C22000280006 and H45E2100021000"],"award-info":[{"award-number":["rot. 2020FNEB27, CUP H23C22000280006 and H45E2100021000"]}]},{"name":"PRIN project PINPOINT","award":["rot. 2020FNEB27, CUP H23C22000280006 and H45E2100021000"],"award-info":[{"award-number":["rot. 2020FNEB27, CUP H23C22000280006 and H45E2100021000"]}]},{"name":"PRIN project PINPOINT","award":["rot. 2020FNEB27, CUP H23C22000280006 and H45E2100021000"],"award-info":[{"award-number":["rot. 2020FNEB27, CUP H23C22000280006 and H45E2100021000"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Process Sci"],"DOI":"10.1007\/s44311-025-00017-5","type":"journal-article","created":{"date-parts":[[2025,5,20]],"date-time":"2025-05-20T15:19:34Z","timestamp":1747754374000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Generating multiperspective process traces using conditional variational autoencoders"],"prefix":"10.1007","volume":"2","author":[{"given":"Riccardo","family":"Graziosi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Massimiliano","family":"Ronzani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrei","family":"Buliga","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chiara","family":"Di Francescomarino","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francesco","family":"Folino","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chiara","family":"Ghidini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francesca","family":"Meneghello","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luigi","family":"Pontieri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,5,20]]},"reference":[{"key":"17_CR1","doi-asserted-by":"publisher","unstructured":"Adamo G, Borgo S, Di Francescomarino C, Ghidini C, Guarino N, Sanfilippo EM (2017) Business processes and their participants: An ontological perspective. In: AI*IA 2017 Advances in Artificial Intelligence - XVIth International Conference of the Italian Association for Artificial Intelligence, Bari, Italy, November 14-17, 2017, Proceedings, Springer, Berlin-Heidelberg, LNCS, vol 10640, pp 215\u2013228.https:\/\/doi.org\/10.1007\/978-3-319-70169-1_16","DOI":"10.1007\/978-3-319-70169-1_16"},{"key":"17_CR2","doi-asserted-by":"publisher","unstructured":"Agarwal P, Gupta A, Sindhgatta R, Dechu S (2022) Goal-oriented next best activity recommendation using reinforcement learning. CoRR abs\/2205.03219. https:\/\/doi.org\/10.48550\/ARXIV.2205.03219","DOI":"10.48550\/ARXIV.2205.03219"},{"key":"17_CR3","doi-asserted-by":"publisher","unstructured":"Ali MA, Dumas M, Milani F (2024) Enhancing the accuracy of predictors of activity sequences of business processes. In: Research Challenges in Information Science - 18th International Conference, RCIS 2024, Guimar\u00e3es, Portugal, May 14-17, 2024, Proceedings, Part I, Springer, Berlin-Heidelberg, Lecture Notes in Business Information Processing, vol 513, pp 149\u2013165. https:\/\/doi.org\/10.1007\/978-3-031-59465-6_10","DOI":"10.1007\/978-3-031-59465-6_10"},{"key":"17_CR4","unstructured":"Bengio Y, Ducharme R, Vincent P, Janvin C (2003) A neural probabilistic language model. J Mach Learn Res 3:1137\u20131155. https:\/\/jmlr.org\/papers\/v3\/bengio03a.html"},{"issue":"1","key":"17_CR5","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1109\/TNNLS.2013.2278313","volume":"25","author":"RPJC Bose","year":"2014","unstructured":"Bose RPJC, van der Aalst WMP, \u017dliobait\u0117 I, Pechenizkiy M (2014) Dealing with concept drifts in process mining. IEEE Trans Neural Netw Learn Syst 25(1):154\u2013171. https:\/\/doi.org\/10.1109\/TNNLS.2013.2278313","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"17_CR6","doi-asserted-by":"publisher","unstructured":"Bowman SR, Vilnis L, Vinyals O, Dai AM, J\u00f3zefowicz R, Bengio S (2016) Generating sentences from a continuous space. In: Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning, CoNLL 2016, Berlin, Germany, August 11-12, 2016, ACL, Kerrville, pp 10\u201321. https:\/\/doi.org\/10.18653\/V1\/K16-1002","DOI":"10.18653\/V1\/K16-1002"},{"key":"17_CR7","doi-asserted-by":"publisher","unstructured":"Buliga A, Di Francescomarino C, Ghidini C, Maggi FM (2023) Counterfactuals and ways to build them: Evaluating approaches in predictive process monitoring. In: Advanced Information Systems Engineering - 35th International Conference, CAiSE 2023, Zaragoza, Spain, June 12-16, 2023, Proceedings, Springer, Berlin-Heidelberg, Lecture Notes in Computer Science, vol 13901, pp 558\u2013574. https:\/\/doi.org\/10.1007\/978-3-031-34560-9_33","DOI":"10.1007\/978-3-031-34560-9_33"},{"key":"17_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2020.113284","volume":"134","author":"M Camargo","year":"2020","unstructured":"Camargo M, Dumas M, Gonz\u00e1lez-Rojas O (2020) Automated discovery of business process simulation models from event logs. Decis Support Syst 134:113284. https:\/\/doi.org\/10.1016\/j.dss.2020.113284","journal-title":"Decis Support Syst"},{"key":"17_CR9","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.577","volume":"7","author":"M Camargo","year":"2021","unstructured":"Camargo M, Dumas M, Rojas OG (2021) Discovering generative models from event logs: data-driven simulation vs deep learning. PeerJ Comput Sci 7:e577. https:\/\/doi.org\/10.7717\/peerj-cs.577","journal-title":"PeerJ Comput Sci"},{"key":"17_CR10","doi-asserted-by":"publisher","unstructured":"Camargo M, Dumas M, Rojas OG (2022) Learning accurate business process simulation models from event logs via automated process discovery and deep learning. In: Advanced Information Systems Engineering - 34th Int. Conf., CAiSE 2022, Proc., Springer, Berlin-Heidelberg, LNCS, vol 13295, pp 55\u201371. https:\/\/doi.org\/10.1007\/978-3-031-07472-1_4","DOI":"10.1007\/978-3-031-07472-1_4"},{"key":"17_CR11","doi-asserted-by":"publisher","unstructured":"Camargo M, Dumas M, Rojas OG (2019) Learning accurate LSTM models of business processes. In: Business Process Management - 17th Int. Conf., BPM 2019, Proc., Springer, Berlin-Heidelberg, LNCS, vol 11675, pp 286\u2013302.https:\/\/doi.org\/10.1007\/978-3-030-26619-6_19","DOI":"10.1007\/978-3-030-26619-6_19"},{"key":"17_CR12","doi-asserted-by":"publisher","unstructured":"Chapela-Campa D, Benchekroun I, Baron O, Dumas M, Krass D, Senderovich A (2023) Can I trust my simulation model? measuring the quality of business process simulation models. In: Business Process Management - 21st Int. Conf., BPM 2023, Proc., Springer, Berlin-Heidelberg, LNCS, vol 14159, pp 20\u201337. https:\/\/doi.org\/10.1007\/978-3-031-41620-0_2","DOI":"10.1007\/978-3-031-41620-0_2"},{"issue":"102","key":"17_CR13","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1016\/J.IS.2024.102447","volume":"127","author":"D Chapela-Campa","year":"2025","unstructured":"Chapela-Campa D, Benchekroun I, Baron O, Dumas M, Krass D, Senderovich A (2025) A framework for measuring the quality of business process simulation models. Inf Syst 127(102):447. https:\/\/doi.org\/10.1016\/J.IS.2024.102447","journal-title":"Inf Syst"},{"key":"17_CR14","doi-asserted-by":"publisher","DOI":"10.4121\/UUID:270FD440-1057-4FB9-89A9-B699B47990F5","author":"M de Leoni","year":"2015","unstructured":"de Leoni M, Mannhardt F (2015). Road traffic fine management process. https:\/\/doi.org\/10.4121\/UUID:270FD440-1057-4FB9-89A9-B699B47990F5","journal-title":"Road traffic fine management process."},{"key":"17_CR15","doi-asserted-by":"publisher","unstructured":"Di\u00a0Francescomarino C, Ghidini C (2022) Predictive process monitoring. In: Process Mining Handbook, LNBIP, Springer International Publishing, Berlin-Heidelberg, pp 320\u2013346.https:\/\/doi.org\/10.1007\/978-3-031-08848-3_10","DOI":"10.1007\/978-3-031-08848-3_10"},{"key":"17_CR16","doi-asserted-by":"publisher","unstructured":"Evermann J, Rehse J, Fettke P (2016) A deep learning approach for predicting process behaviour at runtime. In: Business Process Management Workshops - BPM 2016 International Workshops, Rio de Janeiro, Brazil, September 19, 2016, Revised Papers, Springer, Berlin-Heidelberg, LNBIP, vol 281, pp 327\u2013338. https:\/\/doi.org\/10.1007\/978-3-319-58457-7","DOI":"10.1007\/978-3-319-58457-7"},{"key":"17_CR17","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/j.dss.2017.04.003","volume":"100","author":"J Evermann","year":"2017","unstructured":"Evermann J, Rehse JR, Fettke P (2017) Predicting process behaviour using deep learning. Decis Support Syst 100:129\u2013140. https:\/\/doi.org\/10.1016\/j.dss.2017.04.003","journal-title":"Decis Support Syst"},{"key":"17_CR18","doi-asserted-by":"publisher","unstructured":"Fu H, Li C, Liu X, Gao J, Celikyilmaz A, Carin L (2019) Cyclical annealing schedule: A simple approach to mitigating KL vanishing. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume 1 (Long and Short Papers), ACL, Kerrville, pp 240\u2013250. https:\/\/doi.org\/10.18653\/V1\/N19-1021","DOI":"10.18653\/V1\/N19-1021"},{"key":"17_CR19","doi-asserted-by":"publisher","unstructured":"Graziosi R, Ronzani M, Buliga A, Di\u00a0Francescomarino C, Folino F, Ghidini C, Meneghello F, Pontieri L (2024) Generating the traces you need: A conditional generative model for process mining data. In: 2024 6th International Conference on Process Mining (ICPM), IEEE, Piscataway, pp 25\u201332.https:\/\/doi.org\/10.1109\/ICPM63005.2024.10680621","DOI":"10.1109\/ICPM63005.2024.10680621"},{"issue":"5786","key":"17_CR20","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1126\/science.1127647","volume":"313","author":"GE Hinton","year":"2006","unstructured":"Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504\u2013507. https:\/\/doi.org\/10.1126\/science.1127647","journal-title":"Science"},{"key":"17_CR21","doi-asserted-by":"publisher","unstructured":"Jalayer A, Kahani M, Beheshti A, Pourmasoumi A, Motahari-Nezhad HR (2020) Attention mechanism in predictive business process monitoring. In: 24th International Enterprise Distributed Object Computing Conference (EDOC), IEEE, Piscataway, pp 181\u2013186. https:\/\/doi.org\/10.1109\/EDOC49727.2020.00030","DOI":"10.1109\/EDOC49727.2020.00030"},{"key":"17_CR22","doi-asserted-by":"publisher","unstructured":"Ketyk\u00f3 I, Mannhardt F, Hassani M, van Dongen BF (2022) What averages do not tell: predicting real life processes with sequential deep learning. In: SAC \u201922: The 37th ACM\/SIGAPP Symposium on Applied Computing, Virtual Event, April 25 - 29, 2022, ACM, New York, pp 1128\u20131131. https:\/\/doi.org\/10.1145\/3477314.3507179","DOI":"10.1145\/3477314.3507179"},{"key":"17_CR23","unstructured":"Kingma DP, Welling M (2014) Auto-encoding variational bayes. In: 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings. arXiv:1312.6114"},{"key":"17_CR24","doi-asserted-by":"publisher","unstructured":"Kirchdorfer L, Bl\u00fcmel R, Kampik T, Van\u00a0der Aa H, Stuckenschmidt H (2024) Agentsimulator: An agent-based approach for data-driven business process simulation. In: 2024 6th International Conference on Process Mining (ICPM), pp 97\u2013104. https:\/\/doi.org\/10.1109\/ICPM63005.2024.10680660","DOI":"10.1109\/ICPM63005.2024.10680660"},{"key":"17_CR25","doi-asserted-by":"publisher","unstructured":"Klijn EL, Mannhardt F, Fahland D (2024) Multi-perspective concept drift detection: Including the actor perspective. In: Advanced Information Systems Engineering - 36th International Conference, CAiSE 2024, Limassol, Cyprus, June 3-7, 2024, Proceedings, Springer, Berlin-Heidelberg, LNCS, vol 14663, pp 141\u2013157. https:\/\/doi.org\/10.1007\/978-3-031-61057-8_9","DOI":"10.1007\/978-3-031-61057-8_9"},{"key":"17_CR26","doi-asserted-by":"publisher","unstructured":"Krajsic P, Franczyk B (2021) Variational autoencoder for anomaly detection in event data in online process mining. In: Proc. of the 23rd Int. Conf. on Enterprise Information Systems, ICEIS 2021, Volume 1, SCITEPRESS, Set\u00fabal, pp 567\u2013574.https:\/\/doi.org\/10.5220\/0010375905670574","DOI":"10.5220\/0010375905670574"},{"issue":"1\u20132","key":"17_CR27","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1002\/nav.3800020109","volume":"2","author":"HW Kuhn","year":"1955","unstructured":"Kuhn HW (1955) The hungarian method for the assignment problem. Nav Res Logist Q 2(1\u20132):83\u201397. https:\/\/doi.org\/10.1002\/nav.3800020109","journal-title":"Nav Res Logist Q"},{"key":"17_CR28","doi-asserted-by":"publisher","unstructured":"Li K, Yang S, Sullivan TM, Burd RS, Marsic I (2024) ProcessGAN: Generating privacy-preserving time-aware process data with conditional generative adversarial nets. ACM Trans Knowl Discov Data 18(9). https:\/\/doi.org\/10.1145\/3687464","DOI":"10.1145\/3687464"},{"key":"17_CR29","doi-asserted-by":"publisher","unstructured":"Lin L, Wen L, Wang J (2019) Mm-pred: A deep predictive model for multi-attribute event sequence. In: Proceedings of the 2019 SIAM International Conference on Data Mining, SDM 2019, Calgary, Alberta, Canada, May 2-4, 2019, SIAM, Philadelphia, pp 118\u2013126. https:\/\/doi.org\/10.1137\/1.9781611975673.14","DOI":"10.1137\/1.9781611975673.14"},{"key":"17_CR30","unstructured":"Mannhardt F (2018) Multi-perspective process mining. In: Proceedings of the Dissertation Award, Demonstration, and Industrial Track at BPM 2018 co-located with 16th International Conference on Business Process Management (BPM 2018), Sydney, Australia, September 9-14, 2018. CEUR Workshop Proceedings, vol 2196. CEUR-WS.org, Aachen, pp 41\u201345"},{"key":"17_CR31","doi-asserted-by":"publisher","unstructured":"Mehdiyev N, Evermann J, Fettke P (2017) A multi-stage deep learning approach for business process event prediction. In: 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, IEEE Computer Society, Piscataway, pp 119\u2013128. https:\/\/doi.org\/10.1109\/CBI.2017.46","DOI":"10.1109\/CBI.2017.46"},{"key":"17_CR32","doi-asserted-by":"publisher","DOI":"10.4121\/UUID:915D2BFB-7E84-49AD-A286-DC35F063A460","author":"F Mannhardt","year":"2016","unstructured":"Mannhardt F (2016). Sepsis cases - event log. https:\/\/doi.org\/10.4121\/UUID:915D2BFB-7E84-49AD-A286-DC35F063A460","journal-title":"Sepsis cases - event log."},{"key":"17_CR33","doi-asserted-by":"publisher","unstructured":"Meneghello F, Di Francescomarino C, Ghidini C (2023) Runtime integration of machine learning and simulation for business processes. In: 5th Int. Conf on Process Mining, ICPM 2023, IEEE, Piscataway, pp 9\u201316.https:\/\/doi.org\/10.1109\/ICPM60904.2023.10271993","DOI":"10.1109\/ICPM60904.2023.10271993"},{"key":"17_CR34","doi-asserted-by":"publisher","unstructured":"Nolle T, Seeliger A, M\u00fchlh\u00e4user M (2018) Binet: Multivariate business process anomaly detection using deep learning. In: Business Process Management - 16th Int. Conf., BPM 2018, Proc., Springer, Berlin-Heidelberg, LNCS, vol 11080, pp 271\u2013287. https:\/\/doi.org\/10.1007\/978-3-319-98648-7_16","DOI":"10.1007\/978-3-319-98648-7_16"},{"key":"17_CR35","unstructured":"Object Management Group BPMN Technical Committee et\u00a0al (2011) Business process model and notation, version 2.0. OMG, Object Management Group lNeedham, MA"},{"key":"17_CR36","doi-asserted-by":"publisher","unstructured":"Pesic M, Schonenberg H, van\u00a0der Aalst WM (2007) Declare: full support for loosely-structured processes. In: 11th IEEE International Enterprise Distributed Object Computing Conference (EDOC 2007), p 287.https:\/\/doi.org\/10.1109\/EDOC.2007.14","DOI":"10.1109\/EDOC.2007.14"},{"key":"17_CR37","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-024-18767-y","author":"P Sharma","year":"2024","unstructured":"Sharma P, Kumar M, Sharma HK, Biju SM (2024) Generative adversarial networks (GANs): Introduction, Taxonomy, Variants, Limitations, and Applications. Multimed Tools Appl. https:\/\/doi.org\/10.1007\/s11042-024-18767-y","journal-title":"Multimed Tools Appl"},{"issue":"1","key":"17_CR38","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1016\/j.dss.2008.07.002","volume":"46","author":"M Song","year":"2008","unstructured":"Song M, van der Aalst WM (2008) Towards comprehensive support for organizational mining. Decis Support Syst 46(1):300\u2013317. https:\/\/doi.org\/10.1016\/j.dss.2008.07.002","journal-title":"Decis Support Syst"},{"key":"17_CR39","unstructured":"Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014,\u00a0MIT Press,\u00a0Cambridge, pp 3104\u20133112. https:\/\/proceedings.neurips.cc\/paper\/2014\/hash\/a14ac55a4f27472c5d894ec1c3c743d2-Abstract.html"},{"key":"17_CR40","doi-asserted-by":"publisher","unstructured":"Sani MF, Gonzalez JJG, van Zelst SJ, van\u00a0der Aalst WMP (2020) Conformance checking approximation using simulation. In: 2nd Int. Conf. on Process Mining, ICPM 2020, IEEE, Piscataway, pp 105\u2013112. https:\/\/doi.org\/10.1109\/ICPM49681.2020.00025","DOI":"10.1109\/ICPM49681.2020.00025"},{"key":"17_CR41","unstructured":"Sohn K, Lee H, Yan X (2015) Learning structured output representation using deep conditional generative models. In: Advances in Neural Information Processing Systems 28:3483\u20133491. https:\/\/proceedings.neurips.cc\/paper\/2015\/hash\/8d55a249e6baa5c06772297520da2051-Abstract.html"},{"key":"17_CR42","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1007\/S10270-020-00789-3","volume":"19","author":"N Tax","year":"2018","unstructured":"Tax N, Teinemaa I, van Zelst SJ (2018) An interdisciplinary comparison of sequence modeling methods for next-element prediction. Softw Syst Model 19:1345\u20131365. https:\/\/doi.org\/10.1007\/S10270-020-00789-3","journal-title":"Softw Syst Model"},{"key":"17_CR43","doi-asserted-by":"publisher","unstructured":"Tax N, Verenich I, Rosa ML, Dumas M (2017) Predictive business process monitoring with LSTM neural networks. In: Advanced Information Systems Engineering - 29th International Conference, CAiSE 2017, Essen, Germany, June 12-16, 2017, Proceedings, vol 10253, pp 477\u2013492. https:\/\/doi.org\/10.1007\/978-3-319-59536-8_30","DOI":"10.1007\/978-3-319-59536-8_30"},{"key":"17_CR44","doi-asserted-by":"publisher","unstructured":"Taymouri F, Rosa ML, Erfani SM (2021) A deep adversarial model for suffix and remaining time prediction of event sequences. In: Demeniconi C, Davidson I (eds) Proceedings of the 2021 SIAM International Conference on Data Mining, SDM 2021, Virtual Event, April 29 - May 1, 2021, SIAM, Philadelphia, pp 522\u2013530.https:\/\/doi.org\/10.1137\/1.9781611976700.59","DOI":"10.1137\/1.9781611976700.59"},{"key":"17_CR45","doi-asserted-by":"publisher","unstructured":"Taymouri F, Rosa ML, Erfani SM, Bozorgi ZD, Verenich I (2020) Predictive business process monitoring via generative adversarial nets: The case of next event prediction. In: Business Process Management - 18th Int. Conf., BPM 2020, Proc., Springer, Berlin-Heidelberg, LNCS, vol 12168, pp 237\u2013256.https:\/\/doi.org\/10.1007\/978-3-030-58666-9_14","DOI":"10.1007\/978-3-030-58666-9_14"},{"key":"17_CR46","doi-asserted-by":"publisher","unstructured":"Teinemaa I, Dumas M, La\u00a0Rosa M, Maggi FM (2019) Outcome-oriented predictive process monitoring: Review and benchmark. ACM Trans Knowl Discov Data 13(2). https:\/\/doi.org\/10.1145\/3301300","DOI":"10.1145\/3301300"},{"key":"17_CR47","doi-asserted-by":"publisher","unstructured":"Tomar S, Gupta A (2023) A review on mode collapse reducing gans with gan\u2019s algorithm and theory. In: GANs for Data Augmentation in Healthcare, Springer International Publishing, Berlin-Heidelberg, pp 21\u201340. https:\/\/doi.org\/10.1007\/978-3-031-43205-7_2","DOI":"10.1007\/978-3-031-43205-7_2"},{"key":"17_CR48","doi-asserted-by":"publisher","unstructured":"van\u00a0der Aalst WMP (2016) Process Mining - Data Science in Action, Second Edition. Springer, Berlin-Heidelberg. https:\/\/doi.org\/10.1007\/978-3-662-49851-4","DOI":"10.1007\/978-3-662-49851-4"},{"key":"17_CR49","doi-asserted-by":"publisher","unstructured":"van Dongen B (2012) Bpi challenge 2012. 4TU.ResearchData.\u00a0Eindhoven University of Technology. https:\/\/doi.org\/10.4121\/UUID:3926DB30-F712-4394-AEBC-75976070E91F","DOI":"10.4121\/UUID:3926DB30-F712-4394-AEBC-75976070E91F"}],"container-title":["Process Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44311-025-00017-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44311-025-00017-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44311-025-00017-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,20]],"date-time":"2025-05-20T15:19:48Z","timestamp":1747754388000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44311-025-00017-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,20]]},"references-count":49,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["17"],"URL":"https:\/\/doi.org\/10.1007\/s44311-025-00017-5","relation":{},"ISSN":["2948-2178"],"issn-type":[{"value":"2948-2178","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,20]]},"assertion":[{"value":"16 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 May 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 May 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"8"}}