{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T15:32:20Z","timestamp":1787758340912,"version":"build-2784847793"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031561061","type":"print"},{"value":"9783031561078","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-56107-8_14","type":"book-chapter","created":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T08:02:06Z","timestamp":1712908926000},"page":"179-190","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Detecting Anomalous Events in\u00a0Object-Centric Business Processes via\u00a0Graph Neural Networks"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-4481-1520","authenticated-orcid":false,"given":"Alessandro","family":"Niro","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3036-1478","authenticated-orcid":false,"given":"Michael","family":"Werner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,4,13]]},"reference":[{"key":"14_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-30446-1_1","volume-title":"Software Engineering and Formal Methods","author":"WMP Aalst","year":"2019","unstructured":"Aalst, W.M.P.: Object-centric process mining: dealing with divergence and convergence in event data. In: \u00d6lveczky, P.C., Sala\u00fcn, G. (eds.) SEFM 2019. LNCS, vol. 11724, pp. 3\u201325. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-30446-1_1"},{"key":"14_CR2","doi-asserted-by":"publisher","unstructured":"van der Aalst, W.M.P.: Process mining: a 360 degree overview. In: van der Aalst, W.M.P., Carmona, J. (eds.) Process Mining Handbook. LNBIP, vol. 448, pp. 3\u201334. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-08848-3_1","DOI":"10.1007\/978-3-031-08848-3_1"},{"key":"14_CR3","doi-asserted-by":"crossref","unstructured":"Adams, J.N., van\u00a0der Aalst, W.M.P.: Precision and fitness in object-centric process mining. In: 2021 3rd International Conference on Process Mining (ICPM), pp. 128\u2013135. IEEE, Eindhoven, Netherlands (2021)","DOI":"10.1109\/ICPM53251.2021.9576886"},{"key":"14_CR4","doi-asserted-by":"publisher","unstructured":"Adams, J.N., van der Aalst, W.M.P.: Addressing convergence, divergence, and deficiency issues. In: De Weerdt, J., Pufahl, L. (eds.) BPM 2023. LNBIP, vol. 492, pp. 496\u2013507. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-50974-2_37","DOI":"10.1007\/978-3-031-50974-2_37"},{"key":"14_CR5","doi-asserted-by":"crossref","unstructured":"Adams, J.N., Park, G., van\u00a0der Aalst, W.M.P.: Ocpa: a python library for object-centric process analysis. Software Impacts, p. 100438 (2022)","DOI":"10.1016\/j.simpa.2022.100438"},{"key":"14_CR6","doi-asserted-by":"crossref","unstructured":"Adams, J.N., Schuster, D., Schmitz, S., Schuh, G., van\u00a0der Aalst, W.M.P.: Defining cases and variants for object-centric event data. In: 2022 4th International Conference on Process Mining (ICPM), pp. 128\u2013135 (2022)","DOI":"10.1109\/ICPM57379.2022.9980730"},{"key":"14_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1007\/978-3-030-85469-0_16","volume-title":"Business Process Management","author":"G Bergami","year":"2021","unstructured":"Bergami, G., Maggi, F.M., Marrella, A., Montali, M.: Aligning data-aware declarative process models and event logs. In: Polyvyanyy, A., Wynn, M.T., Van Looy, A., Reichert, M. (eds.) BPM 2021. LNCS, vol. 12875, pp. 235\u2013251. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-85469-0_16"},{"key":"14_CR8","doi-asserted-by":"crossref","unstructured":"Berti, A., Herforth, J., Qafari, M., van\u00a0der Aalst, W.M.P.: Graph-Based Feature Extraction on Object-Centric Event Logs. preprint, In Review (2022)","DOI":"10.21203\/rs.3.rs-2384982\/v1"},{"issue":"1","key":"14_CR9","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.is.2012.04.004","volume":"38","author":"F Bezerra","year":"2013","unstructured":"Bezerra, F., Wainer, J.: Algorithms for anomaly detection of traces in logs of process aware information systems. Inf. Syst. 38(1), 33\u201344 (2013)","journal-title":"Inf. Syst."},{"key":"14_CR10","series-title":"Lecture Notes in Business Information Processing","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1007\/978-3-642-01862-6_13","volume-title":"Enterprise, Business-Process and Information Systems Modeling","author":"F Bezerra","year":"2009","unstructured":"Bezerra, F., Wainer, J., van der Aalst, W.M.P.: Anomaly detection using process mining. In: Halpin, T., et al. (eds.) BPMDS\/EMMSAD -2009. LNBIP, vol. 29, pp. 149\u2013161. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-642-01862-6_13"},{"key":"14_CR11","unstructured":"Bronstein, M.M., Bruna, J., Cohen, T., Veli\u010dkovi\u0107, P.: Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges (2021). arXiv:2104.13478 [cs, stat]"},{"key":"14_CR12","doi-asserted-by":"crossref","unstructured":"B\u00f6hmer, K., Rinderle-Ma, S.: Multi-perspective Anomaly Detection in Business Process Execution Events, pp. 80\u201398 (2016). mAG ID: 2538859255","DOI":"10.1007\/978-3-319-48472-3_5"},{"key":"14_CR13","doi-asserted-by":"crossref","unstructured":"B\u00f6hmer, K., Rinderle-Ma, S.: Anomaly Detection in Business Process Runtime Behavior \u2013 Challenges and Limitations (2017). arXiv:1705.06659 [cs]","DOI":"10.1007\/978-3-319-65000-5_5"},{"issue":"3","key":"14_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1541880.1541882","volume":"41","author":"V Chandola","year":"2009","unstructured":"Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41(3), 1\u201358 (2009)","journal-title":"ACM Comput. Surv."},{"key":"14_CR15","series-title":"Lecture Notes in Business Information Processing","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/978-3-030-98581-3_9","volume-title":"Process Mining Workshops","author":"A Chiorrini","year":"2022","unstructured":"Chiorrini, A., Diamantini, C., Mircoli, A., Potena, D.: Exploiting instance graphs and graph neural networks for next activity prediction. In: Munoz-Gama, J., Lu, X. (eds.) ICPM 2021. LNBIP, vol. 433, pp. 115\u2013126. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-030-98581-3_9"},{"key":"14_CR16","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.eswa.2016.04.021","volume":"59","author":"C Diamantini","year":"2016","unstructured":"Diamantini, C., Genga, L., Potena, D., van der Aalst, W.M.P.: Building instance graphs for highly variable processes. Expert Syst. Appl. 59, 101\u2013118 (2016)","journal-title":"Expert Syst. Appl."},{"key":"14_CR17","unstructured":"van Dongen, B.F.: BPI Challenge 2017 (2017). publisher: 4TU.ResearchData"},{"key":"14_CR18","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1007\/978-3-030-85082-1_16","volume-title":"New Trends in Database and Information Systems","author":"AF Ghahfarokhi","year":"2021","unstructured":"Ghahfarokhi, A.F., Park, G., Berti, A., van der Aalst, W.M.P.: OCEL: a standard for object-centric event logs. In: Bellatreche, L., et al. (eds.) ADBIS 2021. CCIS, vol. 1450, pp. 169\u2013175. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-85082-1_16"},{"issue":"sup1","key":"14_CR19","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1080\/12460125.2020.1780780","volume":"29","author":"M Harl","year":"2020","unstructured":"Harl, M., Weinzierl, S., Stierle, M., Matzner, M.: Explainable predictive business process monitoring using gated graph neural networks. J. Decis. Syst. 29(sup1), 312\u2013327 (2020)","journal-title":"J. Decis. Syst."},{"key":"14_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1007\/978-3-030-85469-0_26","volume-title":"Business Process Management","author":"S Huo","year":"2021","unstructured":"Huo, S., V\u00f6lzer, H., Reddy, P., Agarwal, P., Isahagian, V., Muthusamy, V.: Graph autoencoders for business process anomaly detection. In: Polyvyanyy, A., Wynn, M.T., Van Looy, A., Reichert, M. (eds.) BPM 2021. LNCS, vol. 12875, pp. 417\u2013433. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-85469-0_26"},{"key":"14_CR21","doi-asserted-by":"crossref","unstructured":"Junior, S.B., Ceravolo, P., Damiani, E., Omori, N.J., Tavares, G.M.: Anomaly detection on event logs with a scarcity of labels. In: 2020 2nd International Conference on Process Mining (ICPM), pp. 161\u2013168 (2020)","DOI":"10.1109\/ICPM49681.2020.00032"},{"key":"14_CR22","unstructured":"Kipf, T.N., Welling, M.: Semi-Supervised Classification with Graph Convolutional Networks (2017). arXiv:1609.02907 [cs, stat]"},{"key":"14_CR23","doi-asserted-by":"crossref","unstructured":"Ko, J., Comuzzi, M.: A Systematic Review of Anomaly Detection for Business Process Event Logs. Business & Information Systems Engineering (2023)","DOI":"10.1007\/s12599-023-00794-y"},{"key":"14_CR24","doi-asserted-by":"publisher","unstructured":"Lahann, J., Pfeiffer, P., Fettke, P.: LSTM-based anomaly detection of process instances: benchmark and tweaks. In: Montali, M., Senderovich, A., Weidlich, M. (eds.) Process Mining Workshops. ICPM 2022. LNBIP, vol. 468, pp. 229\u2013241. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-27815-0_17","DOI":"10.1007\/978-3-031-27815-0_17"},{"key":"14_CR25","unstructured":"Liu, K., et al.: BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs (2022). arXiv:2206.10071 [cs]"},{"key":"14_CR26","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1016\/j.eswa.2019.04.052","volume":"131","author":"HTC Nguyen","year":"2019","unstructured":"Nguyen, H.T.C., Lee, S., Kim, J., Ko, J., Comuzzi, M.: Autoencoders for improving quality of process event logs. Expert Syst. Appl. 131, 132\u2013147 (2019)","journal-title":"Expert Syst. Appl."},{"issue":"11","key":"14_CR27","doi-asserted-by":"publisher","first-page":"1875","DOI":"10.1007\/s10994-018-5702-8","volume":"107","author":"T Nolle","year":"2018","unstructured":"Nolle, T., Luettgen, S., Seeliger, A., M\u00fchlh\u00e4user, M.: Analyzing business process anomalies using autoencoders. Mach. Learn. 107(11), 1875\u20131893 (2018)","journal-title":"Mach. Learn."},{"key":"14_CR28","doi-asserted-by":"crossref","unstructured":"Nolle, T., Luettgen, S., Seeliger, A., M\u00fchlh\u00e4user, M.: BINet: multi-perspective business process anomaly classification. Inf. Syst. 103, 101458 (2022)","DOI":"10.1016\/j.is.2019.101458"},{"key":"14_CR29","doi-asserted-by":"crossref","unstructured":"Sommers, D., Menkovski, V., Fahland, D.: Process discovery using graph neural networks. In: 2021 3rd International Conference on Process Mining (ICPM), pp. 40\u201347 (2021)","DOI":"10.1109\/ICPM53251.2021.9576849"},{"key":"14_CR30","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1007\/978-3-030-55814-7_25","volume-title":"ADBIS, TPDL and EDA 2020 Common Workshops and Doctoral Consortium","author":"GM Tavares","year":"2020","unstructured":"Tavares, G.M., Barbon, S.: Analysis of language inspired trace representation for anomaly detection. In: Bellatreche, L., et al. (eds.) TPDL\/ADBIS -2020. CCIS, vol. 1260, pp. 296\u2013308. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-55814-7_25"},{"key":"14_CR31","doi-asserted-by":"crossref","unstructured":"Veli\u010dkovi\u0107, P.: Everything is Connected: Graph Neural Networks (2023). arXiv:2301.08210 [cs, stat]","DOI":"10.1016\/j.sbi.2023.102538"},{"key":"14_CR32","series-title":"Lecture Notes in Business Information Processing","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1007\/978-3-030-94343-1_3","volume-title":"Business Process Management Workshops","author":"S Weinzierl","year":"2022","unstructured":"Weinzierl, S.: Exploring gated graph sequence neural networks for predicting next process activities. In: Marrella, A., Weber, B. (eds.) BPM 2021. LNBIP, vol. 436, pp. 30\u201342. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-030-94343-1_3"},{"key":"14_CR33","doi-asserted-by":"crossref","unstructured":"Yuan, X., Zhou, N., Yu, S., Huang, H., Chen, Z., Xia, F.: Higher-order structure based anomaly detection on attributed networks. In: 2021 IEEE International Conference on Big Data (Big Data), pp. 2691\u20132700 (2021)","DOI":"10.1109\/BigData52589.2021.9671990"}],"container-title":["Lecture Notes in Business Information Processing","Process Mining Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-56107-8_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T08:03:46Z","timestamp":1712909026000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-56107-8_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031561061","9783031561078"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-56107-8_14","relation":{},"ISSN":["1865-1348","1865-1356"],"issn-type":[{"value":"1865-1348","type":"print"},{"value":"1865-1356","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"13 April 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Process Mining","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpm2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icpmconference.org\/2023\/","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":"85","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":"38","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":"1","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":"45% - 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":"3","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":"1.82","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":"15 sub-reviewers supported the PC members under their invitation","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)"}}]}}