{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T08:43:49Z","timestamp":1770540229300,"version":"3.49.0"},"publisher-location":"Cham","reference-count":13,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031278143","type":"print"},{"value":"9783031278150","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,3,26]],"date-time":"2023-03-26T00:00:00Z","timestamp":1679788800000},"content-version":"vor","delay-in-days":84,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Constraint monitoring aims to monitor the violation of constraints in business processes, e.g., an invoice should be cleared within 48\u00a0h after the corresponding goods receipt, by analyzing event data. Existing techniques for constraint monitoring assume that a single case notion exists in a business process, e.g., a patient in a healthcare process, and each event is associated with the case notion. However, in reality, business processes are <jats:italic>object-centric<\/jats:italic>, i.e., multiple case notions (objects) exist, and an event may be associated with multiple objects. For instance, an Order-To-Cash (O2C) process involves <jats:italic>order<\/jats:italic>, <jats:italic>item<\/jats:italic>, <jats:italic>delivery<\/jats:italic>, etc., and they interact when executing an event, e.g., packing multiple items together for a delivery. The existing techniques produce misleading insights when applied to such object-centric business processes. In this work, we propose an approach to monitoring constraints in object-centric business processes. To this end, we introduce <jats:italic>Object-Centric Constraint Graphs<\/jats:italic> (OCCGs) to represent constraints that consider the interaction of objects. Next, we evaluate the constraints represented by OCCGs by analyzing Object-Centric Event Logs (OCELs) that store the interaction of different objects in events. We have implemented a web application to support the proposed approach and conducted two case studies using a real-life SAP ERP system.<\/jats:p>","DOI":"10.1007\/978-3-031-27815-0_35","type":"book-chapter","created":{"date-parts":[[2023,3,25]],"date-time":"2023-03-25T10:03:04Z","timestamp":1679738584000},"page":"479-492","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Monitoring Constraints in\u00a0Business Processes Using Object-Centric Constraint Graphs"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9394-6513","authenticated-orcid":false,"given":"Gyunam","family":"Park","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0955-6940","authenticated-orcid":false,"given":"Wil M. P.","family":"van der Aalst","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,3,26]]},"reference":[{"key":"35_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":"35_CR2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-49851-4","volume-title":"Process Mining - Data Science in Action","author":"WMP van der Aalst","year":"2016","unstructured":"van der Aalst, W.M.P.: Process Mining - Data Science in Action. 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