{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T18:10:49Z","timestamp":1779214249584,"version":"3.51.4"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031953965","type":"print"},{"value":"9783031953972","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-95397-2_10","type":"book-chapter","created":{"date-parts":[[2025,6,13]],"date-time":"2025-06-13T04:23:35Z","timestamp":1749788615000},"page":"159-175","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Predicting Unseen Process Behavior Based on\u00a0Log Injection"],"prefix":"10.1007","author":[{"given":"Qian","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Karolin","family":"Winter","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stefanie","family":"Rinderle-Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,6,14]]},"reference":[{"key":"10_CR1","doi-asserted-by":"crossref","unstructured":"Bakhshi, A., Hassannayebi, E., Sadeghi, A.H.: Optimizing sepsis care through heuristics methods in process mining: a trajectory analysis. CoRR abs\/2303.14328 (2023)","DOI":"10.1016\/j.health.2023.100187"},{"key":"10_CR2","doi-asserted-by":"crossref","unstructured":"Brunk, J., Stierle, M., Papke, L., Revoredo, K., Matzner, M., Becker, J.: Cause vs. effect in context-sensitive prediction of business process instances. Inf. Syst. 95, 101635 (2021)","DOI":"10.1016\/j.is.2020.101635"},{"key":"10_CR3","unstructured":"Bukhsh, Z.A., Saeed, A., Dijkman, R.M.: ProcessTransformer: predictive business process monitoring with transformer network. CoRR abs\/2104.00721 (2021)"},{"key":"10_CR4","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1016\/j.eswa.2016.08.040","volume":"65","author":"A Burattin","year":"2016","unstructured":"Burattin, A., Maggi, F.M., Sperduti, A.: Conformance checking based on multi-perspective declarative process models. Expert Syst. Appl. 65, 194\u2013211 (2016)","journal-title":"Expert Syst. Appl."},{"key":"10_CR5","doi-asserted-by":"crossref","unstructured":"Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Business Process Management, pp. 286\u2013302 (2019)","DOI":"10.1007\/978-3-030-26619-6_19"},{"issue":"1","key":"10_CR6","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1007\/s44311-024-00002-4","volume":"1","author":"P Ceravolo","year":"2024","unstructured":"Ceravolo, P., Comuzzi, M., De Weerdt, J., Di Francescomarino, C., Maggi, F.M.: Predictive process monitoring: concepts, challenges, and future research directions. Process Sci. 1(1), 2 (2024)","journal-title":"Process Sci."},{"key":"10_CR7","doi-asserted-by":"crossref","unstructured":"Chamorro, A.E.M., Nepomuceno-Chamorro, I.A., Resinas, M., Ruiz-Cort\u00e9s, A.: Updating prediction models for predictive process monitoring. In: Advanced Information Systems Engineering, pp. 304\u2013318 (2022)","DOI":"10.1007\/978-3-031-07472-1_18"},{"key":"10_CR8","doi-asserted-by":"crossref","unstructured":"Chamorro, A.E.M., Revoredo, K., Resinas, M., del-R\u00edo-Ortega, A., Santoro, F.M., Ruiz-Cort\u00e9s, A.: Context-aware process performance indicator prediction. IEEE Access 8, 222050\u2013222063 (2020)","DOI":"10.1109\/ACCESS.2020.3044670"},{"key":"10_CR9","doi-asserted-by":"crossref","unstructured":"Chen, Q., Winter, K., Rinderle-Ma, S.: Predicting unseen process behavior based on context information from compliance constraints. In: BPM Forum, pp. 127\u2013144 (2023)","DOI":"10.1007\/978-3-031-41623-1_8"},{"key":"10_CR10","doi-asserted-by":"crossref","unstructured":"Ehrendorfer, M., Mangler, J., Rinderle-Ma, S.: Assessing the impact of context data on process outcomes during runtime. In: ICSOC, pp. 3\u201318 (2021)","DOI":"10.1007\/978-3-030-91431-8_1"},{"key":"10_CR11","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., Fettke, P.: Predicting process behaviour using deep learning. Decis. Support Syst. 100, 129\u2013140 (2017)","journal-title":"Decis. Support Syst."},{"key":"10_CR12","doi-asserted-by":"crossref","unstructured":"Folino, F., Guarascio, M., Pontieri, L.: Discovering context-aware models for predicting business process performances. In: OTM, pp. 287\u2013304 (2012)","DOI":"10.1007\/978-3-642-33606-5_18"},{"key":"10_CR13","doi-asserted-by":"crossref","unstructured":"Francescomarino, C.D., Ghidini, C.: Predictive process monitoring. In: van\u00a0der Aalst, W.M.P., Carmona, J. (eds.) Process Mining Handbook, pp. 320\u2013346 (2022)","DOI":"10.1007\/978-3-031-08848-3_10"},{"key":"10_CR14","doi-asserted-by":"crossref","unstructured":"Francescomarino, C.D., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management, pp. 252\u2013268 (2017)","DOI":"10.1007\/978-3-319-65000-5_15"},{"issue":"1","key":"10_CR15","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1007\/s10115-017-1142-1","volume":"57","author":"M Hashmi","year":"2018","unstructured":"Hashmi, M., Governatori, G., Lam, H.-P., Wynn, M.T.: Are we done with business process compliance: state of the art and challenges ahead. Knowl. Inf. Syst. 57(1), 79\u2013133 (2018). https:\/\/doi.org\/10.1007\/s10115-017-1142-1","journal-title":"Knowl. Inf. Syst."},{"key":"10_CR16","doi-asserted-by":"crossref","unstructured":"K\u00e4ppel, M., Jablonski, S.: Model-agnostic event log augmentation for predictive process monitoring. In: Indulska, M., Reinhartz-Berger, I., Cetina, C., Pastor, O. (eds.) Advanced Information Systems Engineering, pp. 381\u2013397 (2023)","DOI":"10.1007\/978-3-031-34560-9_23"},{"issue":"2","key":"10_CR17","first-page":"68","volume":"36","author":"A Koschmider","year":"2016","unstructured":"Koschmider, A., Speidel, S.: Predictive behavior analysis for smart environments. EMISA Forum 36(2), 68\u201371 (2016)","journal-title":"EMISA Forum"},{"key":"10_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2023.114043","volume":"175","author":"S Levich","year":"2023","unstructured":"Levich, S., Lutz, B., Neumann, D.: Utilizing the omnipresent: incorporating digital documents into predictive process monitoring using deep neural networks. Decis. Support Syst. 175, 114043 (2023)","journal-title":"Decis. Support Syst."},{"key":"10_CR19","doi-asserted-by":"crossref","unstructured":"Maisenbacher, M., Weidlich, M.: Handling concept drift in predictive process monitoring. In: Services Computing, pp.\u00a01\u20138 (2017)","DOI":"10.1109\/SCC.2017.10"},{"key":"10_CR20","doi-asserted-by":"crossref","unstructured":"Mangat, A.S., Rinderle-Ma, S.: Next-activity prediction for non-stationary processes with unseen data variability. In: EDOC, pp. 145\u2013161 (2022)","DOI":"10.1007\/978-3-031-17604-3_9"},{"issue":"4","key":"10_CR21","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1007\/s00607-015-0441-1","volume":"98","author":"F Mannhardt","year":"2016","unstructured":"Mannhardt, F., de Leoni, M., Reijers, H.A., van der Aalst, W.: Balanced multi-perspective checking of process conformance. Computing 98(4), 407\u2013437 (2016)","journal-title":"Computing"},{"key":"10_CR22","doi-asserted-by":"crossref","unstructured":"Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Advances in Artificial Intelligence, pp. 348\u2013361 (2019)","DOI":"10.1007\/978-3-030-35166-3_25"},{"key":"10_CR23","doi-asserted-by":"crossref","unstructured":"Park, G., Benzin, J., van\u00a0der Aalst, W.M.P.: Detecting context-aware deviations in process executions. In: Business Process Management Forum, pp. 190\u2013206 (2022)","DOI":"10.1007\/978-3-031-16171-1_12"},{"key":"10_CR24","doi-asserted-by":"crossref","unstructured":"Pauwels, S., Calders, T.: Incremental predictive process monitoring: the next activity case. In: Business Process Management, pp. 123\u2013140 (2021)","DOI":"10.1007\/978-3-030-85469-0_10"},{"key":"10_CR25","doi-asserted-by":"crossref","unstructured":"Peeperkorn, J., vanden Broucke, S., Weerdt, J.D.: Validation set sampling strategies for predictive process monitoring. Inf. Syst. 121, 102330 (2024)","DOI":"10.1016\/j.is.2023.102330"},{"issue":"9","key":"10_CR26","doi-asserted-by":"publisher","first-page":"1005","DOI":"10.1007\/s00607-018-0593-x","volume":"100","author":"M Polato","year":"2018","unstructured":"Polato, M., Sperduti, A., Burattin, A., Leoni, M.: Time and activity sequence prediction of business process instances. Computing 100(9), 1005\u20131031 (2018). https:\/\/doi.org\/10.1007\/s00607-018-0593-x","journal-title":"Computing"},{"issue":"1","key":"10_CR27","first-page":"739","volume":"16","author":"E Rama-Maneiro","year":"2023","unstructured":"Rama-Maneiro, E., Vidal, J.C., Lama, M.: Deep learning for predictive business process monitoring: review and benchmark. TSC 16(1), 739\u2013756 (2023)","journal-title":"TSC"},{"issue":"1","key":"10_CR28","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1109\/TKDE.2023.3286017","volume":"36","author":"E Rama-Maneiro","year":"2024","unstructured":"Rama-Maneiro, E., Vidal, J.C., Lama, M.: Embedding graph convolutional networks in recurrent neural networks for predictive monitoring. IEEE Trans. Knowl. Data Eng. 36(1), 137\u2013151 (2024)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"5","key":"10_CR29","doi-asserted-by":"publisher","first-page":"1385","DOI":"10.1007\/s10115-022-01666-9","volume":"64","author":"W Rizzi","year":"2022","unstructured":"Rizzi, W., Francescomarino, C.D., 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), 1385\u20131416 (2022)","journal-title":"Knowl. Inf. Syst."},{"key":"10_CR30","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big Data 6, 60 (2019)","journal-title":"J. Big Data"},{"key":"10_CR31","doi-asserted-by":"crossref","unstructured":"Stertz, F., Rinderle-Ma, S., Mangler, J.: Analyzing process concept drifts based on sensor event streams during runtime. In: BPM, pp. 202\u2013219 (2020)","DOI":"10.1007\/978-3-030-58666-9_12"},{"key":"10_CR32","doi-asserted-by":"crossref","unstructured":"Tax, N., Verenich, I., Rosa, M.L., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: CAiSE, pp. 477\u2013492 (2017)","DOI":"10.1007\/978-3-319-59536-8_30"},{"key":"10_CR33","doi-asserted-by":"crossref","unstructured":"Teinemaa, I., Dumas, M., Maggi, F.M., Francescomarino, C.D.: Predictive business process monitoring with structured and unstructured data. In: BPM, pp. 401\u2013417 (2016)","DOI":"10.1007\/978-3-319-45348-4_23"},{"key":"10_CR34","doi-asserted-by":"crossref","unstructured":"Teinemaa, I., Dumas, M., Rosa, M.L., Maggi, F.M.: Outcome-oriented predictive process monitoring: review and benchmark. TKDD 13(2), 17:1\u201317:57 (2019)","DOI":"10.1145\/3301300"},{"key":"10_CR35","unstructured":"Weinzierl, S., Revoredo, K., Matzner, M.: Predictive business process monitoring with context information from documents. In: ECIS (2019)"},{"key":"10_CR36","doi-asserted-by":"crossref","unstructured":"Yeshchenko, A., Durier, F., Revoredo, K., Mendling, J., Santoro, F.M.: Context-aware predictive process monitoring: the impact of news sentiment. In: OTM, pp. 586\u2013603 (2018)","DOI":"10.1007\/978-3-030-02610-3_33"}],"container-title":["Lecture Notes in Business Information Processing","Enterprise, Business-Process and Information Systems Modeling"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-95397-2_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,13]],"date-time":"2025-06-13T04:23:47Z","timestamp":1749788627000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-95397-2_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031953965","9783031953972"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-95397-2_10","relation":{},"ISSN":["1865-1348","1865-1356"],"issn-type":[{"value":"1865-1348","type":"print"},{"value":"1865-1356","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"14 June 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BPMDS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Business Process Modeling, Development and Support","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vienna","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Austria","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":"16 June 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 June 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bpmds2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/bpmds\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}