{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T00:07:33Z","timestamp":1774915653448,"version":"3.50.1"},"reference-count":17,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T00:00:00Z","timestamp":1688083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T00:00:00Z","timestamp":1688083200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100007210","name":"RWTH Aachen University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100007210","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":[[2023,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Process mining provides a collection of techniques to gain insights into business processes by analyzing event logs. Organizations can gain various insights into their business processes by using process mining techniques. Such techniques use event logs extracted from relational databases supporting the business process as input. However, extracting event logs is challenging due to the size of the data, and it remains ad-hoc. Existing commercial tools partly support the extraction of event logs, but they are proprietary and focus on the mainstream processes such as Purchase-To-Pay (P2P) and Order-To-Cash (O2C). Moreover, the extracted event logs suffer from well-known deficiency, convergence, and divergence issues. For example, due to convergence events are unintentionally duplicated causing unreliable or confusing performance diagnostics. In this paper, we propose an approach to extract event logs while avoiding the aforementioned issues. More in detail, we extract object-centric event logs by using an abstraction layer of the database, called Graph of Relationships (GoRs), designing blueprints with domain knowledge, and converting the database and blueprint into object-centric event logs.We fully implemented the proposed approach, which can extract object-centric event logs from SAP ERP systems, and evaluate the utility and scalability of the proposed approach.<\/jats:p>","DOI":"10.1007\/s10844-023-00799-9","type":"journal-article","created":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T11:02:42Z","timestamp":1688122962000},"page":"835-857","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A generic approach to extract object-centric event data from databases supporting SAP ERP"],"prefix":"10.1007","volume":"61","author":[{"given":"Alessandro","family":"Berti","sequence":"first","affiliation":[]},{"given":"Gyunam","family":"Park","sequence":"additional","affiliation":[]},{"given":"Majid","family":"Rafiei","sequence":"additional","affiliation":[]},{"given":"Wil M. P.","family":"van der Aalst","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,30]]},"reference":[{"key":"799_CR1","doi-asserted-by":"publisher","unstructured":"Berti, A., & van der Aalst, W. M. P. (2022). OC-PM: analyzing object-centric event logs and process models. CoRR. https:\/\/doi.org\/10.48550\/arXiv.2209.09725.arXiv:2209.09725","DOI":"10.48550\/arXiv.2209.09725."},{"key":"799_CR2","doi-asserted-by":"publisher","unstructured":"Berti, A., Park, G., & Rafiei, M., et al. (2021). An event data extraction approach from SAP ERP for process mining. In: J. Munoz-Gama, & X. Lu (Eds.), Process Mining Workshops - ICPM 2021 International Workshops, Eindhoven, The Netherlands, October 31 - November 4, 2021, Revised Selected Papers, Lecture Notes in Business Information Processing, (vol 433, pp. 255-267). Springer, New York City. https:\/\/doi.org\/10.1007\/978-3-030-98581-3_19","DOI":"10.1007\/978-3-030-98581-3_19"},{"key":"799_CR3","doi-asserted-by":"publisher","unstructured":"de Murillas, E. G. L., Reijers, H. A., & van der Aalst, W. M. P. (2019). Connecting databases with process mining: a meta model and toolset. Software & Systems Modeling 18(2), 1209\u20131247. https:\/\/doi.org\/10.1007\/s10270-018-0664-7","DOI":"10.1007\/s10270-018-0664-7"},{"key":"799_CR4","doi-asserted-by":"publisher","unstructured":"de Murillas, E. G. L., van der Aalst, W. M. P., & Reijers, H. A. (2015). Process mining on databases: Unearthing historical data from redo logs. In: H. R. Motahari-Nezhad, J. Recker, & M. Weidlich (Eds.), Business Process Management - 13th International Conference, BPM 2015, Innsbruck, Austria, August 31- September 3, 2015, Proceedings, Lecture Notes in Computer Science, (vol 9253, pp. 367\u2013385). 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