{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T15:36:14Z","timestamp":1778600174675,"version":"3.51.4"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030854683","type":"print"},{"value":"9783030854690","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-85469-0_10","type":"book-chapter","created":{"date-parts":[[2021,8,27]],"date-time":"2021-08-27T04:02:49Z","timestamp":1630036969000},"page":"123-140","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Incremental Predictive Process Monitoring: The Next Activity Case"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0427-8945","authenticated-orcid":false,"given":"Stephen","family":"Pauwels","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4943-6978","authenticated-orcid":false,"given":"Toon","family":"Calders","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,8,28]]},"reference":[{"issue":"2","key":"10_CR1","doi-asserted-by":"publisher","first-page":"450","DOI":"10.1016\/j.is.2010.09.001","volume":"36","author":"WM Van der Aalst","year":"2011","unstructured":"Van der Aalst, W.M., Schonenberg, M.H., Song, M.: Time prediction based on process mining. Inf. Syst. 36(2), 450\u2013475 (2011)","journal-title":"Inf. Syst."},{"key":"10_CR2","first-page":"49","volume":"2016","author":"A Berti","year":"2016","unstructured":"Berti, A.: Improving process mining prediction results in processes that change over time. Data Anal. 2016, 49 (2016)","journal-title":"Data Anal."},{"key":"10_CR3","doi-asserted-by":"crossref","unstructured":"Bifet, A., Gavalda, R.: SIAM: learning from time-changing data with adaptive windowing. In: Proceedings of the 2007 SIAM International Conference on Data Mining (SDM), pp. 443\u2013448. SIAM (2007)","DOI":"10.1137\/1.9781611972771.42"},{"issue":"1","key":"10_CR4","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1109\/TNNLS.2013.2278313","volume":"25","author":"RJC Bose","year":"2013","unstructured":"Bose, R.J.C., Van Der Aalst, W.M., \u017dliobait\u0117, I., Pechenizkiy, M.: Dealing with concept drifts in process mining. IEEE Trans. Neural Netw. Learn. Syst. 25(1), 154\u2013171 (2013)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10_CR5","series-title":"Lecture Notes in Business Information Processing","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1007\/978-3-319-74030-0_12","volume-title":"Business Process Management Workshops","author":"A Burattin","year":"2018","unstructured":"Burattin, A., Carmona, J.: A framework for online conformance checking. In: Teniente, E., Weidlich, M. (eds.) BPM 2017. LNBIP, vol. 308, pp. 165\u2013177. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-74030-0_12"},{"issue":"6","key":"10_CR6","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1109\/TSC.2015.2459703","volume":"8","author":"A Burattin","year":"2015","unstructured":"Burattin, A., Cimitile, M., Maggi, F.M., Sperduti, A.: Online discovery of declarative process models from event streams. IEEE Trans. Serv. Comput. 8(6), 833\u2013846 (2015). https:\/\/doi.org\/10.1109\/TSC.2015.2459703","journal-title":"IEEE Trans. Serv. Comput."},{"key":"10_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1007\/978-3-030-26619-6_19","volume-title":"Business Process Management","author":"M Camargo","year":"2019","unstructured":"Camargo, M., Dumas, M., Gonz\u00e1lez-Rojas, O.: Learning accurate LSTM models of business processes. In: Hildebrandt, T., van Dongen, B.F., R\u00f6glinger, M., Mendling, J. (eds.) BPM 2019. LNCS, vol. 11675, pp. 286\u2013302. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-26619-6_19"},{"key":"10_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1007\/978-3-319-98648-7_27","volume-title":"Business Process Management","author":"C Di Francescomarino","year":"2018","unstructured":"Di Francescomarino, C., Ghidini, C., Maggi, F.M., Milani, F.: Predictive process monitoring methods: which one suits me best? In: Weske, M., Montali, M., Weber, I., vom Brocke, J. (eds.) BPM 2018. LNCS, vol. 11080, pp. 462\u2013479. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-98648-7_27"},{"key":"10_CR9","unstructured":"Di Francescomarino, C., Ghidini, C., Maggi, F.M., Rizzi, W., Persia, C.D.: Incremental predictive process monitoring: How to deal with the variability of real environments. arXiv preprint arXiv:1804.03967 (2018)"},{"key":"10_CR10","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1007\/978-3-030-35166-3_25","volume-title":"AI*IA 2019 \u2013 Advances in Artificial Intelligence","author":"N Di Mauro","year":"2019","unstructured":"Di Mauro, N., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019. LNCS (LNAI), vol. 11946, pp. 348\u2013361. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-35166-3_25"},{"key":"10_CR11","doi-asserted-by":"crossref","unstructured":"Domingos, P., Hulten, G.: Mining high-speed data streams. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 71\u201380 (2000)","DOI":"10.1145\/347090.347107"},{"key":"10_CR12","doi-asserted-by":"publisher","unstructured":"van Dongen, B.: BPI challenge (2012). https:\/\/doi.org\/10.4121\/uuid:3926db30-f712-4394-aebc-75976070e91f","DOI":"10.4121\/uuid:3926db30-f712-4394-aebc-75976070e91f"},{"key":"10_CR13","doi-asserted-by":"publisher","unstructured":"van Dongen, B.: BPI challenge (2015). https:\/\/doi.org\/10.4121\/uuid:31a308ef-c844-48da-948c-305d167a0ec1","DOI":"10.4121\/uuid:31a308ef-c844-48da-948c-305d167a0ec1"},{"key":"10_CR14","doi-asserted-by":"publisher","unstructured":"van Dongen, B.: Real-life event logs - hospital log, March 2011. https:\/\/doi.org\/10.4121\/uuid:d9769f3d-0ab0-4fb8-803b-0d1120ffcf54","DOI":"10.4121\/uuid:d9769f3d-0ab0-4fb8-803b-0d1120ffcf54"},{"key":"10_CR15","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, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decis. Support Syst. 100, 129\u2013140 (2017)","journal-title":"Decis. Support Syst."},{"issue":"4","key":"10_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2523813","volume":"46","author":"J Gama","year":"2014","unstructured":"Gama, J., \u017dliobait\u0117, I., Bifet, A., Pechenizkiy, M., Bouchachia, A.: A survey on concept drift adaptation. ACM Comput. Surv. (CSUR) 46(4), 1\u201337 (2014)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"10_CR17","unstructured":"Gepperth, A., Hammer, B.: Incremental learning algorithms and applications. In: European Symposium on Artificial Neural Networks (ESANN) (2016)"},{"key":"10_CR18","doi-asserted-by":"crossref","unstructured":"Lin, L., Wen, L., Wang, J.: MM-PRED: a deep predictive model for multi-attribute event sequence. In: Proceedings of the 2019 SIAM International Conference on Data Mining, pp. 118\u2013126. SIAM (2019)","DOI":"10.1137\/1.9781611975673.14"},{"key":"10_CR19","first-page":"1","volume":"17","author":"M Maisenbacher","year":"2017","unstructured":"Maisenbacher, M., Weidlich, M.: Handling concept drift in predictive process monitoring. SCC 17, 1\u20138 (2017)","journal-title":"SCC"},{"key":"10_CR20","doi-asserted-by":"crossref","unstructured":"McCloskey, M., Cohen, N.J.: Catastrophic interference in connectionist networks: the sequential learning problem. In: Psychology of Learning and Motivation, vol. 24, pp. 109\u2013165. Elsevier (1989)","DOI":"10.1016\/S0079-7421(08)60536-8"},{"key":"10_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"330","DOI":"10.1007\/978-3-319-46397-1_26","volume-title":"Conceptual Modeling","author":"A Ostovar","year":"2016","unstructured":"Ostovar, A., Maaradji, A., La Rosa, M., ter Hofstede, A.H.M., van Dongen, B.F.V.: Detecting drift from event streams of unpredictable business processes. In: Comyn-Wattiau, I., Tanaka, K., Song, I.-Y., Yamamoto, S., Saeki, M. (eds.) ER 2016. LNCS, vol. 9974, pp. 330\u2013346. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46397-1_26"},{"key":"10_CR22","doi-asserted-by":"crossref","unstructured":"Pasquadibisceglie, V., Appice, A., Castellano, G., Malerba, D.: Using convolutional neural networks for predictive process analytics. In: 2019 International Conference on Process Mining (ICPM), pp. 129\u2013136. IEEE (2019)","DOI":"10.1109\/ICPM.2019.00028"},{"key":"10_CR23","series-title":"Lecture Notes in Business Information Processing","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1007\/978-3-030-58638-6_11","volume-title":"Business Process Management Forum","author":"V Pasquadibisceglie","year":"2020","unstructured":"Pasquadibisceglie, V., Appice, A., Castellano, G., Malerba, D.: Predictive process mining meets computer vision. In: Fahland, D., Ghidini, C., Becker, J., Dumas, M. (eds.) BPM 2020. LNBIP, vol. 392, pp. 176\u2013192. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58638-6_11"},{"key":"10_CR24","series-title":"Lecture Notes in Business Information Processing","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1007\/978-3-030-58638-6_10","volume-title":"Business Process Management Forum","author":"S Pauwels","year":"2020","unstructured":"Pauwels, S., Calders, T.: Bayesian network based predictions of business processes. In: Fahland, D., Ghidini, C., Becker, J., Dumas, M. (eds.) BPM 2020. LNBIP, vol. 392, pp. 159\u2013175. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58638-6_10"},{"key":"10_CR25","doi-asserted-by":"publisher","unstructured":"Rokach, L., Maimon, O.: Clustering methods. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook. Springer, Boston (2005). https:\/\/doi.org\/10.1007\/0-387-25465-X_15","DOI":"10.1007\/0-387-25465-X_15"},{"key":"#cr-split#-10_CR26.1","unstructured":"Serr\u00e0 Juli\u00e0, J., Sur\u00eds, D., Miron, M., Karatzoglou, A.: Overcoming catastrophic forgetting with hard attention to the task. In: Dy, J., Krause, A., (eds.) Proceedings of the 35th International Conference on Machine Learning (ICML 2018), 10-15 July 2018, Stockholmsm\u00e4ssan, Sweden [Massachusetts: PMLR"},{"key":"#cr-split#-10_CR26.2","unstructured":"2018], pp. 4548-4557. Proceedings of Machine Learning Research (2018)"},{"issue":"6","key":"10_CR27","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1007\/s10270-020-00789-3","volume":"19","author":"N Tax","year":"2020","unstructured":"Tax, N., Teinemaa, I., van Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. Softw. Syst. Model. 19(6), 1345\u20131365 (2020)","journal-title":"Softw. Syst. Model."},{"key":"10_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"477","DOI":"10.1007\/978-3-319-59536-8_30","volume-title":"Advanced Information Systems Engineering","author":"N Tax","year":"2017","unstructured":"Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Dubois, E., Pohl, K. (eds.) CAiSE 2017. LNCS, vol. 10253, pp. 477\u2013492. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-59536-8_30"},{"key":"10_CR29","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1007\/978-3-030-58666-9_14","volume-title":"Business Process Management","author":"F Taymouri","year":"2020","unstructured":"Taymouri, F., Rosa, M.L., Erfani, S., Bozorgi, Z.D., Verenich, I.: Predictive business process monitoring via generative adversarial nets: the case of next event prediction. In: Fahland, D., Ghidini, C., Becker, J., Dumas, M. (eds.) BPM 2020. LNCS, vol. 12168, pp. 237\u2013256. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58666-9_14"},{"issue":"2","key":"10_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3301300","volume":"13","author":"I Teinemaa","year":"2019","unstructured":"Teinemaa, I., Dumas, M., Rosa, M.L., Maggi, F.M.: Outcome-oriented predictive process monitoring: review and benchmark. ACM Trans. Knowl. Discov Data (TKDD) 13(2), 1\u201357 (2019)","journal-title":"ACM Trans. Knowl. Discov Data (TKDD)"},{"key":"10_CR31","doi-asserted-by":"publisher","first-page":"119787","DOI":"10.1109\/ACCESS.2019.2937085","volume":"7","author":"J Theis","year":"2019","unstructured":"Theis, J., Darabi, H.: Decay replay mining to predict next process events. IEEE Access 7, 119787\u2013119803 (2019)","journal-title":"IEEE Access"},{"key":"10_CR32","unstructured":"Verenich, I.: Helpdesk, mendeley data, v1 (2016). https:\/\/doi.org\/10.17632\/39bp3vv62t.1"},{"key":"10_CR33","unstructured":"Weinzierl, S., et al.: An empirical comparison of deep-neural-network architectures for next activity prediction using context-enriched process event logs. arXiv preprint arXiv:2005.01194 (2020)"}],"container-title":["Lecture Notes in Computer Science","Business Process Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-85469-0_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T18:00:20Z","timestamp":1709834420000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-85469-0_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030854683","9783030854690"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-85469-0_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"28 August 2021","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":"Rome","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bpm2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/bpm2021.diag.uniroma1.it\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"92","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"16","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"17% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"For the BPM forum 16 papers were accepted; and for the BPM and RPA Forum 8 papers were accepted from 14 submissions","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}