{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T21:13:52Z","timestamp":1774732432175,"version":"3.50.1"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,9,18]],"date-time":"2024-09-18T00:00:00Z","timestamp":1726617600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,9,18]],"date-time":"2024-09-18T00:00:00Z","timestamp":1726617600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100008815","name":"Libera Universit\u00e0 di Bolzano","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100008815","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Inf Syst"],"published-print":{"date-parts":[[2025,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Predictive Process Monitoring (PPM) is a field of Process Mining that aims at predicting how an ongoing execution of a business process will develop in the future using past process executions recorded in event logs. The recent stream of publications in this field shows the need for tools able to support researchers and users in comparing and selecting the techniques that are the most suitable for them. In this paper, we present \u00a0, a dedicated tool for supporting users in building, comparing and explaining the PPM models that can then be used to perform predictions on the future of an ongoing case. \u00a0has been constructed by carefully considering the necessary capabilities of a PPM tool and by implementing them in a client-server architecture able to support modularity and scalability. The features of \u00a0support researchers and practitioners within the entire pipeline for constructing reliable PPM models. The assessment using reactive design patterns and load tests provides an evaluation of the interaction among the architectural elements, and of the scalability with multiple users accessing the prototype in a concurrent manner, respectively. By providing a rich set of different state-of-the-art approaches, \u00a0offers to Process Mining researchers and practitioners a useful and flexible instrument for comparing and selecting PPM techniques.<\/jats:p>","DOI":"10.1007\/s10844-024-00890-9","type":"journal-article","created":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T06:03:00Z","timestamp":1727157780000},"page":"259-291","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Nirdizati: an advanced predictive process monitoring toolkit"],"prefix":"10.1007","volume":"63","author":[{"given":"Williams","family":"Rizzi","sequence":"first","affiliation":[]},{"given":"Chiara","family":"Di Francescomarino","sequence":"additional","affiliation":[]},{"given":"Chiara","family":"Ghidini","sequence":"additional","affiliation":[]},{"given":"Fabrizio Maria","family":"Maggi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,9,18]]},"reference":[{"key":"890_CR1","doi-asserted-by":"publisher","unstructured":"3TU Data Center (2011). BPI Challenge 2011 Event Log. https:\/\/doi.org\/10.4121\/uuid:d9769f3d-0ab0-4fb8-803b-0d1120ffcf54","DOI":"10.4121\/uuid:d9769f3d-0ab0-4fb8-803b-0d1120ffcf54"},{"key":"890_CR2","doi-asserted-by":"publisher","unstructured":"Abb, L., Pfeiffer, P., & Fettke, P., et\u00a0al. (2023). A discussion on generalization in next-activity prediction. In Business process management workshops - BPM 2023 international workshops, Utrecht, The Netherlands, September 11-15, 2023, Revised Selected Papers, Lecture Notes in Business Information Processing (Vol. 492, pp. 18\u20133). Springer. https:\/\/doi.org\/10.1007\/978-3-031-50974-2_2","DOI":"10.1007\/978-3-031-50974-2_2"},{"key":"890_CR3","doi-asserted-by":"publisher","unstructured":"Alby, T. (ed.) (2023). Data science in practice (1st ed.). Chapman and Hall\/CRC, https:\/\/doi.org\/10.1201\/9781003426363","DOI":"10.1201\/9781003426363"},{"key":"890_CR4","doi-asserted-by":"publisher","unstructured":"Bartmann, N., Hill, S., & Corea, C., et\u00a0al (2021). Applied predictive process monitoring and hyper parameter optimization in camunda. In Intelligent information systems - CAiSE Forum 2021, Proceedings, LNBIP (Vol. 424, pp. 129\u201313). Heidelberg: Springer. https:\/\/doi.org\/10.1007\/978-3-030-79108-7_15","DOI":"10.1007\/978-3-030-79108-7_15"},{"key":"890_CR5","unstructured":"Bergstra J, Bardenet R, & Bengio Y, et\u00a0al (2011). Algorithms for hyper-parameter optimization. In Advances in neural information processing systems 24: 25th annual conference on neural information processing systems 2011, Proceedings (pp. 2546\u20132554). http:\/\/papers.nips.cc\/paper\/4443-algorithms-for-hyper-parameter-optimization"},{"key":"890_CR6","doi-asserted-by":"publisher","first-page":"281","DOI":"10.5555\/2503308.2188395","volume":"13","author":"J Bergstra","year":"2012","unstructured":"Bergstra, J., & Bengio, Y. (2012). Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13, 281\u2013305. https:\/\/doi.org\/10.5555\/2503308.2188395","journal-title":"Journal of Machine Learning Research"},{"key":"890_CR7","unstructured":"Berti, A., van Zelst, S.J., & van\u00a0der Aalst, W.M.P. (2019). Process mining for python (pm4py): Bridging the gap between process- and data science. CoRR arXiv:1905.06169"},{"key":"890_CR8","unstructured":"Bon\u00e9r, J., Farley, D., & Kuhn, R., et al. (2014). The reactive manifesto. http:\/\/www.reactivemanifesto.org\/"},{"key":"890_CR9","doi-asserted-by":"publisher","unstructured":"Buliga, A., Di Francescomarino, C., & Ghidini C, et\u00a0al (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, Lecture Notes in Computer Science (Vol. 13901, pp. 558\u2013574). Springer. https:\/\/doi.org\/10.1007\/978-3-031-34560-9_33","DOI":"10.1007\/978-3-031-34560-9_33"},{"key":"890_CR10","doi-asserted-by":"publisher","unstructured":"Calvanese, D., Kalayci, T.E., & Montali, M., et\u00a0al. (2017). Ontology-based data access for extracting event logs from legacy data: The onprom tool and methodology. In Business information systems - 20th international conference, BIS 2017, Proceedings, LNBIP (Vol. 288, pp. 220\u2013236). Heidelberg: Springer. https:\/\/doi.org\/10.1007\/978-3-319-59336-4_16","DOI":"10.1007\/978-3-319-59336-4_16"},{"issue":"5\/6","key":"890_CR11","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1504\/IJCSE.2006.014772","volume":"2","author":"M Castellanos","year":"2006","unstructured":"Castellanos, M., Salazar, N., Casati, F., et al. (2006). Predictive business operations management. IJCSE, 2(5\/6), 292\u2013301. https:\/\/doi.org\/10.1504\/IJCSE.2006.014772","journal-title":"IJCSE"},{"key":"890_CR12","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1016\/j.is.2015.07.003","volume":"56","author":"M de Leoni","year":"2016","unstructured":"de Leoni, M., van der Aalst, W. M. P., & Dees, M. (2016). A general process mining framework for correlating, predicting and clustering dynamic behavior based on event logs. Information Systems, 56, 235\u201325. https:\/\/doi.org\/10.1016\/j.is.2015.07.003","journal-title":"Information Systems"},{"key":"890_CR13","doi-asserted-by":"publisher","unstructured":"Di Francescomarino, C., & Ghidini, C. (2022). Predictive process monitoring. In Process Mining Handbook, LNBIP (Vol. 448, p. 320\u2013346). Heidelberg: Springer. https:\/\/doi.org\/10.1007\/978-3-031-08848-3_10","DOI":"10.1007\/978-3-031-08848-3_10"},{"issue":"Part","key":"890_CR14","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.is.2018.01.003","volume":"74","author":"C Di Francescomarino","year":"2018","unstructured":"Di Francescomarino, C., Dumas, M., Federici, M., et al. (2018). Genetic algorithms for hyperparameter optimization in predictive business process monitoring. Inf Syst, 74(Part), 67\u20138. https:\/\/doi.org\/10.1016\/j.is.2018.01.003","journal-title":"Inf Syst"},{"issue":"6","key":"890_CR15","doi-asserted-by":"publisher","first-page":"896","DOI":"10.1109\/TSC.2016.2645153","volume":"12","author":"C Di Francescomarino","year":"2019","unstructured":"Di Francescomarino, C., Dumas, M., Maggi, F. M., et al. (2019). Clustering-based predictive process monitoring. IEEE Transactions on Services Computing, 12(6), 896\u2013909. https:\/\/doi.org\/10.1109\/TSC.2016.2645153","journal-title":"IEEE Transactions on Services Computing"},{"issue":"6","key":"890_CR16","doi-asserted-by":"publisher","first-page":"199","DOI":"10.3390\/A15060199","volume":"15","author":"G El-Khawaga","year":"2022","unstructured":"El-Khawaga, G., Abu-Elkheir, M., & Reichert, M. (2022). XAI in the context of predictive process monitoring: An empirical analysis framework. Algorithms, 15(6), 199. https:\/\/doi.org\/10.3390\/A15060199","journal-title":"Algorithms"},{"key":"890_CR17","unstructured":"Federici, M., Rizzi, W., & Di Francescomarino, C., et\u00a0al. (2015). A ProM operational support provider for predictive monitoring of business processes. In Proceedings of the BPM Demo Session 2015, CEUR Workshop Proceedings (Vol. 1418, pp 1\u20135). CEUR-WS.org, RWTH Aachen. http:\/\/ceur-ws.org\/Vol-1418\/paper1.pdf"},{"key":"890_CR18","doi-asserted-by":"publisher","unstructured":"Folino, F., Guarascio, M., & Pontieri, L. (2012). Discovering context-aware models for predicting business process performances. In On the move to meaningful internet systems: OTM 2012, confederated international conferences: CoopIS, DOA-SVI, and ODBASE 2012, Proceedings, Part I (pp. 287\u2013304)https:\/\/doi.org\/10.1007\/978-3-642-33606-5_18","DOI":"10.1007\/978-3-642-33606-5_18"},{"key":"890_CR19","unstructured":"Galanti, R., de Leoni, M., & Marazzi, A., et\u00a0al. (2021). Integration of an explainable predictive process monitoring system into ibm process mining suite (extended abstract). In ICPM 2021 Doctoral Consortium and Demo Track 2021, Proceedings, CEUR Workshop Proceedings (Vol. 3098, pp 53\u201354). CEUR-WS.org. https:\/\/ceur-ws.org\/Vol-3098\/demo_216.pdf"},{"issue":"2","key":"890_CR20","doi-asserted-by":"publisher","first-page":"521","DOI":"10.1007\/S10844-022-00744-2","volume":"60","author":"A Giannoulidis","year":"2023","unstructured":"Giannoulidis, A., & Gounaris, A. (2023). A context-aware unsupervised predictive maintenance solution for fleet management. Journal of Intelligent Information System, 60(2), 521\u2013547. https:\/\/doi.org\/10.1007\/S10844-022-00744-2","journal-title":"Journal of Intelligent Information System"},{"key":"890_CR21","doi-asserted-by":"publisher","unstructured":"Hundogan, O., Lu, X., & Du, Y., et\u00a0al. (2023). CREATED: generating viable counterfactual sequences for predictive process analytics. In Advanced information systems engineering - 35th international conference, CAiSE 2023, Zaragoza, Spain, June 12-16, 2023, Proceedings, Lecture Notes in Computer Science (Vol. 13901, pp. 541\u2013557). Springer, https:\/\/doi.org\/10.1007\/978-3-031-34560-9_32","DOI":"10.1007\/978-3-031-34560-9_32"},{"issue":"6","key":"890_CR22","doi-asserted-by":"publisher","first-page":"7029","DOI":"10.1016\/j.eswa.2010.12.012","volume":"38","author":"M La Rosa","year":"2011","unstructured":"La Rosa, M., Reijers, H. A., van der Aalst, W. M. P., et al. (2011). APROMORE: An advanced process model repository. Expert Systems with Applications, 38(6), 7029\u20137040. https:\/\/doi.org\/10.1016\/j.eswa.2010.12.012","journal-title":"Expert Systems with Applications"},{"key":"890_CR23","doi-asserted-by":"publisher","unstructured":"Leontjeva, A., Conforti, R., & Di Francescomarino, C., et\u00a0al. (2015). Complex symbolic sequence encodings for predictive monitoring of business processes. In Business process management - 13th international conference, BPM 2015, Proceedings, LNCS (Vol. 9253, pp. 297\u2013313). Heidelberg: Springer.https:\/\/doi.org\/10.1007\/978-3-319-23063-4_21","DOI":"10.1007\/978-3-319-23063-4_21"},{"key":"890_CR24","unstructured":"Lundberg, S.M., & Lee, S. (2017). A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017 (pp. 4765\u20134774). https:\/\/proceedings.neurips.cc\/paper\/2017\/hash\/8a20a8621978632d76c43dfd28b67767-Abstract.html"},{"issue":"1","key":"890_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13755-015-0011-0","volume":"3","author":"G Luo","year":"2015","unstructured":"Luo, G. (2015). Mlbcd: A machine learning tool for big clinical data. Health Information Science and Systems, 3(1), 1\u201319. https:\/\/doi.org\/10.1186\/s13755-015-0011-0","journal-title":"Health Information Science and Systems"},{"key":"890_CR26","doi-asserted-by":"publisher","unstructured":"Maita, A.R.C., Fantinato, M., & Peres, S.M., et\u00a0al. (2023). Towards a business-oriented approach to visualization-supported interpretability of prediction results in process mining. In Proceedings of the 25th International Conference on Enterprise Information Systems, ICEIS 2023 (Vol. 1, pp. 395\u2013406). Prague, Czech Republic: SciTePress. https:\/\/doi.org\/10.5220\/0011976000003467","DOI":"10.5220\/0011976000003467"},{"issue":"1","key":"890_CR27","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1007\/S10844-022-00765-X","volume":"61","author":"J Peeperkorn","year":"2023","unstructured":"Peeperkorn, J., & vanden Broucke S, Weerdt JD,. (2023). Can recurrent neural networks learn process model structure? Journal of Intelligent Information System, 61(1), 27\u201351. https:\/\/doi.org\/10.1007\/S10844-022-00765-X","journal-title":"Journal of Intelligent Information System"},{"key":"890_CR28","doi-asserted-by":"publisher","unstructured":"Pfeiffer, P., Lahann, J., & Fettke, P. (2022). The label ambiguity problem in process prediction. In Business process management workshops - BPM 2022 International Workshops, M\u00fcnster, Germany, September 11-16, 2022, Revised Selected Papers, Lecture Notes in Business Information Processing (Vol. 460, pp. 37\u201344). Springer https:\/\/doi.org\/10.1007\/978-3-031-25383-6_4","DOI":"10.1007\/978-3-031-25383-6_4"},{"key":"890_CR29","doi-asserted-by":"publisher","unstructured":"Polato, M., Sperduti, A., & Burattin, A., et\u00a0al. (2014). Data-aware remaining time prediction of business process instances. In 2014 International Joint Conference on Neural Networks, IJCNN 2014 (pp. 816\u2013823).https:\/\/doi.org\/10.1109\/IJCNN.2014.6889360","DOI":"10.1109\/IJCNN.2014.6889360"},{"key":"890_CR30","doi-asserted-by":"publisher","unstructured":"Polato, M., Sperduti, A., Burattin, A., et al. (2018). Time and activity sequence prediction of business process instances. Computing, 100(9), 1005\u20131031. https:\/\/doi.org\/10.1007\/s00607-018-0593-x","DOI":"10.1007\/s00607-018-0593-x"},{"issue":"1","key":"890_CR31","doi-asserted-by":"publisher","first-page":"739","DOI":"10.1109\/TSC.2021.3139807","volume":"16","author":"E Rama-Maneiro","year":"2023","unstructured":"Rama-Maneiro, E., Vidal, J. C., & Lama, M. (2023). Deep learning for predictive business process monitoring: Review and benchmark. IEEE Transactions on Services Computing, 16(1), 739\u2013756. https:\/\/doi.org\/10.1109\/TSC.2021.3139807","journal-title":"IEEE Transactions on Services Computing"},{"key":"890_CR32","doi-asserted-by":"publisher","unstructured":"Ribeiro, M.T., Singh, S., & Guestrin, C. (2016). \u201cwhy should I trust you?\u201d: Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1135\u20131144). New York City: ACM, https:\/\/doi.org\/10.1145\/2939672.2939778","DOI":"10.1145\/2939672.2939778"},{"key":"890_CR33","doi-asserted-by":"publisher","unstructured":"Rizzi, W., Comuzzi, M., & Di Francescomarino, C., et\u00a0al. (2022a). Explainable predictive process monitoring: A user evaluation. https:\/\/doi.org\/10.48550\/arXiv.2202.07760","DOI":"10.48550\/arXiv.2202.07760"},{"issue":"5","key":"890_CR34","doi-asserted-by":"publisher","first-page":"1385","DOI":"10.1007\/S10115-022-01666-9","volume":"64","author":"W Rizzi","year":"2022","unstructured":"Rizzi, W., Di Francescomarino, C., Ghidini, C., et al. (2022b). How do I update my model? on the resilience of predictive process monitoring models to change. Knowledge and Information Systems, 64(5), 1385\u20131416. https:\/\/doi.org\/10.1007\/S10115-022-01666-9","journal-title":"Knowledge and Information Systems"},{"issue":"1","key":"890_CR35","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1007\/S10844-022-00775-9","volume":"61","author":"MF Sani","year":"2023","unstructured":"Sani, M. F., Vazifehdoostirani, M., Park, G., et al. (2023). Performance-preserving event log sampling for predictive monitoring. Journal of Intelligent Information System, 61(1), 53\u201382. https:\/\/doi.org\/10.1007\/S10844-022-00775-9","journal-title":"Journal of Intelligent Information System"},{"issue":"4","key":"890_CR36","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1007\/s10009-004-0165-6","volume":"7","author":"I Schieferdecker","year":"2005","unstructured":"Schieferdecker, I., Din, G., & Apostolidis, D. (2005). Distributed functional and load tests for web services. Int J Softw Tools Technol Transf, 7(4), 351\u2013360. https:\/\/doi.org\/10.1007\/s10009-004-0165-6","journal-title":"Int J Softw Tools Technol Transf"},{"key":"890_CR37","doi-asserted-by":"publisher","unstructured":"Senderovich, A., Di Francescomarino, C., & Ghidini, C., et\u00a0al (2017). Intra and inter-case features in predictive process monitoring: A tale of two dimensions. In Business process management - 15th international conference, BPM 2017, Proceedings, LNCS (Vol. 10445, pp. 306\u2013323). Heidelberg: Springer, https:\/\/doi.org\/10.1007\/978-3-319-65000-5_18","DOI":"10.1007\/978-3-319-65000-5_18"},{"issue":"1","key":"890_CR38","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1007\/s10009-013-0291-0","volume":"17","author":"M Shafique","year":"2015","unstructured":"Shafique, M., & Labiche, Y. (2015). A systematic review of state-based test tools. Int J Softw Tools Technol Transf, 17(1), 59\u20137. https:\/\/doi.org\/10.1007\/s10009-013-0291-0","journal-title":"Int J Softw Tools Technol Transf"},{"key":"890_CR39","doi-asserted-by":"publisher","unstructured":"Stevens, A., Smedt, J.D., & Peeperkorn, J., et\u00a0al (2022). Assessing the robustness in predictive process monitoring through adversarial attacks. In 4th International Conference on Process Mining, ICPM 2022 (pp. 56\u201363). Bolzano, Italy: IEEE, https:\/\/doi.org\/10.1109\/ICPM57379.2022.9980753","DOI":"10.1109\/ICPM57379.2022.9980753"},{"key":"890_CR40","unstructured":"Stierle, M., Brunk, J., & Weinzierl, S., et\u00a0al (2021). Bringing light into the darkness - A systematic literature review on explainable predictive business process monitoring techniques. In 28th European conference on information systems - liberty, equality, and fraternity in a digitizing world, ECIS 2020. https:\/\/aisel.aisnet.org\/ecis2021_rip\/8"},{"issue":"Part D","key":"890_CR41","doi-asserted-by":"publisher","first-page":"10702","DOI":"10.1016\/J.ENGAPPAI.2023.107028","volume":"126","author":"GM Tavares","year":"2023","unstructured":"Tavares, G. M., Oyamada, R. S., Barbon, S., et al. (2023). Trace encoding in process mining: A survey and benchmarking. Engineering Applications of Artificial Intelligence, 126(Part D), 10702. https:\/\/doi.org\/10.1016\/J.ENGAPPAI.2023.107028","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"890_CR42","doi-asserted-by":"publisher","unstructured":"Tax, N., Verenich, I., & Rosa, M.L., et\u00a0al (2017). Predictive business process monitoring with LSTM neural networks. In Advanced information systems engineering - 29th international conference, CAiSE 2017, Proceedings, LNCS (Vol. 10253, pp 477\u2013492). Heidelberg: Springer. https:\/\/doi.org\/10.1007\/978-3-319-59536-8_30","DOI":"10.1007\/978-3-319-59536-8_30"},{"key":"890_CR43","doi-asserted-by":"publisher","unstructured":"Teinemaa, I., Dumas, M., La Rosa, M., et al. (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":"890_CR44","doi-asserted-by":"publisher","unstructured":"Thornton, C., Hutter, F., & Hoos, H.H., et\u00a0al. (2013). Auto-WEKA: Combined selection and hyperparameter optimization of classification algorithms. In Proc.\u00a0of KDD-2013 (pp. 847\u2013855). https:\/\/doi.org\/10.1145\/2487575.2487629","DOI":"10.1145\/2487575.2487629"},{"key":"890_CR45","doi-asserted-by":"publisher","unstructured":"van der Aalst, & W.M.P., Carmona J., (2022). Process mining handbook, lecture notes in business information processing (Vol. 448). Springer. https:\/\/doi.org\/10.1007\/978-3-031-08848-3","DOI":"10.1007\/978-3-031-08848-3"},{"key":"890_CR46","doi-asserted-by":"publisher","unstructured":"van Dongen, B.F., de\u00a0Medeiros, A.K.A., & Verbeek, H.M.W., et\u00a0al. (2005). The ProM framework: A new era in process mining tool support. In ICATPN 2005 (pp. 444\u2013454).https:\/\/doi.org\/10.1007\/11494744_25","DOI":"10.1007\/11494744_25"},{"key":"890_CR47","doi-asserted-by":"publisher","unstructured":"Verbeek, H.M.W., Buijs, J.C.A.M., & van Dongen, B.F., et\u00a0al. (2010). XES, XESame, and ProM 6. In Information systems evolution - CAiSE Forum 2010, Selected Extended Papers, LNBIP (Vol. 72, pp. 60\u201375). Heidelberg: Springer.https:\/\/doi.org\/10.1007\/978-3-642-17722-4_5","DOI":"10.1007\/978-3-642-17722-4_5"},{"key":"890_CR48","doi-asserted-by":"publisher","unstructured":"Verenich, I., M\u00f5skovski, S., & Raboczi, S., et\u00a0al. (2018). Predictive process monitoring in Apromore. In Information systems in the big data era - CAiSE Forum 2018, Proceedings, LNBIP (Vol. 317, pp. 244\u2013253). Heidelberg: Springerhttps:\/\/doi.org\/10.1007\/978-3-319-92901-9_21","DOI":"10.1007\/978-3-319-92901-9_21"},{"issue":"4","key":"890_CR49","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3331449","volume":"10","author":"I Verenich","year":"2019","unstructured":"Verenich, I., Dumas, M., Rosa, M. L., et al. (2019). Survey and cross-benchmark comparison of remaining time prediction methods in business process monitoring. ACM Transactions on Intelligent Systems and Technology, 10(4), 1\u201334. https:\/\/doi.org\/10.1145\/3331449","journal-title":"ACM Transactions on Intelligent Systems and Technology"},{"key":"890_CR50","doi-asserted-by":"publisher","unstructured":"Weytjens, H. & Weerdt, J.D. (2021). Creating unbiased public benchmark datasets with data leakage prevention for predictive process monitoring. In Business process management workshops - BPM 2021 international Workshops, Revised Selected Papers, LNBIP (Vol. 436, pp. 18\u201329). Heidelberg: Springer. https:\/\/doi.org\/10.1007\/978-3-030-94343-1_2","DOI":"10.1007\/978-3-030-94343-1_2"},{"key":"890_CR51","doi-asserted-by":"publisher","unstructured":"Wistuba, M., Schilling, N., & Schmidt-Thieme, L. (2015). Hyperparameter search space pruning - a new component for sequential model-based hyperparameter optimization. In Machine learning and knowledge discovery in databases - European conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part II, Lecture Notes in Computer Science (Vol. 9285, pp. 104\u2013119). Springer. https:\/\/doi.org\/10.1007\/978-3-319-23525-7_7","DOI":"10.1007\/978-3-319-23525-7_7"}],"container-title":["Journal of Intelligent Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10844-024-00890-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10844-024-00890-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10844-024-00890-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,8]],"date-time":"2025-03-08T10:23:01Z","timestamp":1741429381000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10844-024-00890-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,18]]},"references-count":51,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["890"],"URL":"https:\/\/doi.org\/10.1007\/s10844-024-00890-9","relation":{},"ISSN":["0925-9902","1573-7675"],"issn-type":[{"value":"0925-9902","type":"print"},{"value":"1573-7675","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,18]]},"assertion":[{"value":"5 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 September 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 September 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 September 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}},{"value":"Not applicable","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"Not applicable","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"Not applicable","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}}]}}