EVErPREP: Towards an Event Knowledge Graph Enhanced Workflow Model for Event Log Preparation
Filipp P, Dorsch R, Harth A (2025)
Publication Type: Conference contribution
Publication year: 2025
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 533
Pages Range: 31-43
Conference Proceedings Title: Lecture Notes in Business Information Processing
Event location: Lyngby, DNK
ISBN: 9783031822247
DOI: 10.1007/978-3-031-82225-4_3
Abstract
Event data preparation is a critical yet time-consuming phase in process mining projects, often slowed down by complex relational data models and a lack of domain knowledge. This paper presents EVErPREP, a novel workflow model that leverages Event Knowledge Graphs to enhance event data preparation for event logs. EVErPREP uses Semantic Web technologies to improve the exploration, extraction, and processing of event data, ultimately improving the quality and interpretability of event data and event logs. The approach is evaluated through a case study at Munich Airport’s Baggage Handling System, demonstrating its effectiveness in reducing complexity and improving explainability in event data preparation. By providing a more structured and semantically enriched foundation for process mining, EVErPREP showcases increased efficiency and effectiveness of process mining projects through a semantically enriched foundation.
Involved external institutions
Germany (DE)How to cite
APA:
Filipp, P., Dorsch, R., & Harth, A. (2025). EVErPREP: Towards an Event Knowledge Graph Enhanced Workflow Model for Event Log Preparation. In Andrea Delgado, Tijs Slaats (Eds.), Lecture Notes in Business Information Processing (pp. 31-43). Lyngby, DNK: Springer Science and Business Media Deutschland GmbH.
MLA:
Filipp, Peter, Rene Dorsch, and Andreas Harth. "EVErPREP: Towards an Event Knowledge Graph Enhanced Workflow Model for Event Log Preparation." Proceedings of the International Workshops which were held in conjunction with the 6th International Conference on Process Mining, ICPM 2024, Lyngby, DNK Ed. Andrea Delgado, Tijs Slaats, Springer Science and Business Media Deutschland GmbH, 2025. 31-43.
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