{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T00:05:49Z","timestamp":1774915549505,"version":"3.50.1"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032028662","type":"print"},{"value":"9783032028679","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,8,31]],"date-time":"2025-08-31T00:00:00Z","timestamp":1756598400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,31]],"date-time":"2025-08-31T00:00:00Z","timestamp":1756598400000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-02867-9_27","type":"book-chapter","created":{"date-parts":[[2025,8,30]],"date-time":"2025-08-30T05:45:33Z","timestamp":1756532733000},"page":"451-468","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Online Discovery of\u00a0Simulation Models for\u00a0Evolving Business Processes"],"prefix":"10.1007","author":[{"given":"Francesco","family":"Vinci","sequence":"first","affiliation":[]},{"given":"Gyunam","family":"Park","sequence":"additional","affiliation":[]},{"given":"Wil M. P.","family":"van der Aalst","sequence":"additional","affiliation":[]},{"given":"Massimiliano","family":"de Leoni","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,8,31]]},"reference":[{"key":"27_CR1","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-662-49851-4_1","volume-title":"Process Mining","author":"W Aalst","year":"2016","unstructured":"Aalst, W.: Data science in action. In: Process Mining, pp. 3\u201323. Springer, Heidelberg (2016). https:\/\/doi.org\/10.1007\/978-3-662-49851-4_1"},{"key":"27_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1007\/978-3-030-85469-0_25","volume-title":"Business Process Management","author":"JN Adams","year":"2021","unstructured":"Adams, J.N., van Zelst, S.J., Quack, L., Hausmann, K., van der Aalst, W.M.P., Rose, T.: A framework for explainable concept drift detection in process mining. In: Polyvyanyy, A., Wynn, M.T., Van Looy, A., Reichert, M. (eds.) BPM 2021. LNCS, vol. 12875, pp. 400\u2013416. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-85469-0_25"},{"key":"27_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1007\/978-3-030-85469-0_25","volume-title":"Business Process Management","author":"JN Adams","year":"2021","unstructured":"Adams, J.N., van Zelst, S.J., Quack, L., Hausmann, K., van der Aalst, W.M.P., Rose, T.: A framework for explainable concept drift detection in process mining. In: Polyvyanyy, A., Wynn, M.T., Van Looy, A., Reichert, M. (eds.) BPM 2021. LNCS, vol. 12875, pp. 400\u2013416. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-85469-0_25"},{"issue":"1","key":"27_CR4","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1109\/TNNLS.2013.2278313","volume":"25","author":"RPJC Bose","year":"2014","unstructured":"Bose, R.P.J.C., van der Aalst, W.M.P., \u017dliobait\u0117, I., Pechenizkiy, M.: Dealing with concept drifts in process mining. IEEE Trans. Neural Netw. Learn. Syst. 25(1), 154\u2013171 (2014). https:\/\/doi.org\/10.1109\/TNNLS.2013.2278313","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"27_CR5","doi-asserted-by":"publisher","unstructured":"Burattin, A.: Streaming process mining. In: van der Aalst, W.M.P., Carmona, J. (eds.) Process Mining Handbook. LNBIP, vol. 448, pp. 349\u2013372. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-08848-3_11","DOI":"10.1007\/978-3-031-08848-3_11"},{"key":"27_CR6","doi-asserted-by":"publisher","unstructured":"Burattin, A., Sperduti, A., van\u00a0der Aalst, W.M.P.: Control-flow discovery from event streams. In: 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 2420\u20132427 (2014). https:\/\/doi.org\/10.1109\/CEC.2014.6900341","DOI":"10.1109\/CEC.2014.6900341"},{"key":"27_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2023.102248","volume":"117","author":"M Camargo","year":"2023","unstructured":"Camargo, M., B\u00e1ron, D., Dumas, M., Gonz\u00e1lez-Rojas, O.: Learning business process simulation models: a hybrid process mining and deep learning approach. Inf. Syst. 117, 102248 (2023). https:\/\/doi.org\/10.1016\/j.is.2023.102248","journal-title":"Inf. Syst."},{"key":"27_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.: Automated discovery of business process simulation models from event logs. Decis. Support Syst. 134, 113284 (2020). https:\/\/doi.org\/10.1016\/j.dss.2020.113284","journal-title":"Decis. Support Syst."},{"key":"27_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1007\/978-3-642-34156-4_10","volume-title":"Advances in Intelligent Data Analysis XI","author":"J Carmona","year":"2012","unstructured":"Carmona, J., Gavald\u00e0, R.: Online techniques for dealing with concept drift in process mining. In: Hollm\u00e9n, J., Klawonn, F., Tucker, A. (eds.) IDA 2012. LNCS, vol. 7619, pp. 90\u2013102. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-34156-4_10"},{"key":"27_CR10","doi-asserted-by":"publisher","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.: A framework for measuring the quality of business process simulation models. Inf. Syst. 127, 102447 (2025). https:\/\/doi.org\/10.1016\/j.is.2024.102447","journal-title":"Inf. Syst."},{"key":"27_CR11","doi-asserted-by":"publisher","unstructured":"Klijn, E.L., Mannhardt, F., Fahland, D.: Multi-perspective concept drift detection: including the actor perspective. In: Guizzardi, G., Santoro, F., Mouratidis, H., Soffer, P. (eds.) Advanced Information Systems Engineering. CAiSE 2024. LNCS, vol. 14663, pp. 141\u2013157. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-61057-8_9","DOI":"10.1007\/978-3-031-61057-8_9"},{"key":"27_CR12","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/s44311-025-00012-w","volume":"2","author":"A Kraus","year":"2025","unstructured":"Kraus, A., van der Aa, H.: Machine learning-based detection of concept drift in business processes. Proc. Sci. 2, 5 (2025). https:\/\/doi.org\/10.1007\/s44311-025-00012-w","journal-title":"Proc. Sci."},{"key":"27_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1007\/978-3-642-38697-8_17","volume-title":"Application and Theory of Petri Nets and Concurrency","author":"SJJ Leemans","year":"2013","unstructured":"Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Discovering block-structured process models from event logs - a constructive approach. In: Colom, J.-M., Desel, J. (eds.) PETRI NETS 2013. LNCS, vol. 7927, pp. 311\u2013329. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-38697-8_17"},{"key":"27_CR14","doi-asserted-by":"publisher","unstructured":"de\u00a0Leoni, M., Vinci, F., Leemans, S.J.J., Mannhardt, F.: Investigating the influence of data-aware process states on activity probabilities in simulation models: does accuracy improve? In: Di Francescomarino, C., Burattin, A., Janiesch, C., Sadiq, S. (eds.) Business Process Management. BPM 2023. LNCS, vol. 14159, pp. 129\u2013145. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-41620-0_8","DOI":"10.1007\/978-3-031-41620-0_8"},{"key":"27_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"383","DOI":"10.1007\/978-3-030-85469-0_24","volume-title":"Business Process Management","author":"Y Lu","year":"2021","unstructured":"Lu, Y., Chen, Q., Poon, S.: A robust and accurate approach to detect process drifts from event streams. In: Polyvyanyy, A., Wynn, M.T., Van Looy, A., Reichert, M. (eds.) BPM 2021. LNCS, vol. 12875, pp. 383\u2013399. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-85469-0_24"},{"key":"27_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2024.102472","volume":"128","author":"F Meneghello","year":"2025","unstructured":"Meneghello, F., Di Francescomarino, C., Ghidini, C., Ronzani, M.: Runtime integration of machine learning and simulation for business processes: time and decision mining predictions. Inf. Syst. 128, 102472 (2025). https:\/\/doi.org\/10.1016\/j.is.2024.102472","journal-title":"Inf. Syst."},{"key":"27_CR17","first-page":"1","volume":"22","author":"J Montiel","year":"2021","unstructured":"Montiel, J., et al.: River: machine learning for streaming data in python. J. Mach. Learn. Res. 22, 1\u20138 (2021)","journal-title":"J. Mach. Learn. Res."},{"key":"27_CR18","doi-asserted-by":"publisher","unstructured":"Navarin, N., Cambiaso, M., Burattin, A., Maggi, F.M., Oneto, L., Sperduti, A.: Towards online discovery of data-aware declarative process models from event streams. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp.\u00a01\u20138 (2020). https:\/\/doi.org\/10.1109\/IJCNN48605.2020.9207500","DOI":"10.1109\/IJCNN48605.2020.9207500"},{"issue":"5","key":"27_CR19","doi-asserted-by":"publisher","first-page":"13851416","DOI":"10.1007\/s10115-022-01666-9","volume":"64","author":"W Rizzi","year":"2022","unstructured":"Rizzi, W., Di Francescomarino, C., Ghidini, C., Maggi, F.M.: How do i update my model? On the resilience of predictive process monitoring models to change. Knowl. Inf. Syst. 64(5), 13851416 (2022). https:\/\/doi.org\/10.1007\/s10115-022-01666-9","journal-title":"Knowl. Inf. Syst."},{"issue":"3","key":"27_CR20","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1016\/j.is.2008.09.002","volume":"34","author":"A Rozinat","year":"2009","unstructured":"Rozinat, A., Mans, R., Song, M., van der Aalst, W.: Discovering simulation models. Inf. Syst. 34(3), 305\u2013327 (2009). https:\/\/doi.org\/10.1016\/j.is.2008.09.002","journal-title":"Inf. Syst."},{"issue":"9","key":"27_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3472752","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 Comput. Surv. 54(9), 1\u201338 (2021). https:\/\/doi.org\/10.1145\/3472752","journal-title":"ACM Comput. Surv."},{"key":"27_CR22","doi-asserted-by":"publisher","unstructured":"Scheibel, B., Rinderle-Ma, S.: An end-to-end approach for online decision mining and decision drift analysis in process-aware information systems. In: Cabanillas, C., P\u00e9rez, F. (eds.) Intelligent Information Systems. CAiSE 2023. LNBIP, vol. 477, pp. 17\u201325. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-34674-3_3","DOI":"10.1007\/978-3-031-34674-3_3"},{"key":"27_CR23","doi-asserted-by":"publisher","unstructured":"Schuster, D.: Incremental process discovery. Dissertation, RWTH Aachen University, Aachen (2024). https:\/\/doi.org\/10.18154\/RWTH-2024-06483","DOI":"10.18154\/RWTH-2024-06483"},{"key":"27_CR24","doi-asserted-by":"publisher","unstructured":"Schuster, D., F\u00f6cking, N., van Zelst, S.J., van\u00a0der Aalst, W.M.P.: Incremental discovery of process models using trace fragments. In: Di Francescomarino, C., Burattin, A., Janiesch, C., Sadiq, S. (eds.) Business Process Management. BPM 2023. LNCS, vol. 14159, pp. 55\u201373. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-41620-0_4","DOI":"10.1007\/978-3-031-41620-0_4"},{"key":"27_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.softx.2023.101373","volume":"22","author":"D Schuster","year":"2023","unstructured":"Schuster, D., van Zelst, S.J., van der Aalst, W.M.: Cortado: a dedicated process mining tool for interactive process discovery. SoftwareX 22, 101373 (2023). https:\/\/doi.org\/10.1016\/j.softx.2023.101373","journal-title":"SoftwareX"},{"key":"27_CR26","unstructured":"van Zelst, S.: Process mining with streaming data. Ph.D. thesis (2019)"},{"key":"27_CR27","unstructured":"Vinci, F., Park, G., van\u00a0der Aalst, W., de\u00a0Leoni, M.: Online discovery of simulation models for evolving business processes (extended version) (2025). https:\/\/arxiv.org\/abs\/2506.10049"},{"issue":"2","key":"27_CR28","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1007\/s10115-017-1060-2","volume":"54","author":"SJ van Zelst","year":"2017","unstructured":"van Zelst, S.J., van Dongen, B.F., van der Aalst, W.M.P.: Event stream-based process discovery using abstract representations. Knowl. Inf. Syst. 54(2), 407\u2013435 (2017). https:\/\/doi.org\/10.1007\/s10115-017-1060-2","journal-title":"Knowl. Inf. Syst."}],"container-title":["Lecture Notes in Computer Science","Business Process Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-02867-9_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,30]],"date-time":"2025-08-30T05:45:34Z","timestamp":1756532734000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-02867-9_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,31]]},"ISBN":["9783032028662","9783032028679"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-02867-9_27","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,31]]},"assertion":[{"value":"31 August 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BPM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Business Process Management","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Seville","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31 August 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bpm2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.bpm2025seville.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}