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Extracting event data from document-driven enterprise systems
Umeå University, Faculty of Science and Technology, Department of Computing Science. Free University of Bozen-Bolzano, Bolzano, Italy.ORCID iD: 0000-0001-5174-9693
Hasselt University, Hasselt, Belgium; Maastricht University, MD, Maastricht, Netherlands.
Virtual Vehicle Research GmbH, Graz, Austria.
Free University of Bozen-Bolzano, Bolzano, Italy.
2023 (English)In: Advanced information systems engineering: 35th International conference, CAiSE 2023, Zaragoza, Spain, June 12–16, 2023, proceedings / [ed] Marta Indulska; Iris Reinhartz-Berger; Carlos Cetina; Oscar Pastor, Springer, 2023, p. 193-209Conference paper, Published paper (Refereed)
Abstract [en]

The preparation of input event data is one of the most critical phases in process mining projects. Different frameworks have been developed to offer methodologies and/or supporting toolkits for data preparation. One of these frameworks, called OnProm, relies on sophisticated semantic technologies to extract event logs from relational databases. The toolkit consists of a series of general steps, meant to work on arbitrary, legacy databases. However, in many settings, the input database is not a legacy one but is structured with conceptually understandable object types and relationships that can be effectively employed to support business users in the extraction process. This is, for example, the case for document-driven enterprise systems. In this paper, we focus on this class of systems and propose a guided approach, erprep, to support a group of business and technical users in setting up OnProm with minimal effort. We demonstrate the approach in a real-life use case.

Place, publisher, year, edition, pages
Springer, 2023. p. 193-209
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 13901
Keywords [en]
Data preparation, ERP systems, event log extraction, Ontology-based event modeling
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:umu:diva-212113DOI: 10.1007/978-3-031-34560-9_12ISI: 001284775300012Scopus ID: 2-s2.0-85163937628ISBN: 978-3-031-34559-3 (print)ISBN: 978-3-031-34560-9 (electronic)OAI: oai:DiVA.org:umu-212113DiVA, id: diva2:1782756
Conference
35th International Conference on Advanced Information Systems Engineering, CAiSE 2023, Zaragoza, Spain, June 12–16, 2023
Available from: 2023-07-17 Created: 2023-07-17 Last updated: 2025-04-24Bibliographically approved

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Calvanese, Diego

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