Eric is the scientific programmer in the PA group. As such, he is the custodian of the process mining framework ProM. In you want access to the ProM repository, or have any questions related to ProM and its development, ask Eric. Recently, he has been working on a decomposition framework for both process discovery as conformance checking in ProM, Log Skeletons and on the S-component-based discovery technique called DiSCover. Earlier, he also worked on ExSpect and Woflan.

Position:	Scientific Programmer
Room:	MF 7.065
Tel (internal):	3755
Links:	Presentations Projects Publications
External links:	Personal home page Google scholar page Scopus page ORCID page DBLP page TU/e page

Recent courses

• Foundations of Data Analytics (2IAB1) 2023 - Learning goals Working with data data exploration statistical techniques data visualisation data mining data organization and data retrieval Programming (customizable, reproducible) Communication skills (visualisations, a poster and a pitch in the assignments) Systematic way to approach problems (“scientific method”)
• Foundations of Process Mining (2AMI10) 2023 - Data science is the profession of the future, because organizations that are unable to use (big) data in a smart way will not survive. It is not sufficient to focus on data storage and data analysis. The data scientist also needs to relate data to process analysis. Process mining bridges the gap between traditional model-based Read More ...

Recent presentations

• ProM Development, landscape, and release - Download as PDF
• Log Skeletons: A log visualizer and classifier - Download as PDF
• Block Layout for (accepting) Petri nets - Download as PDF
• DrFurby Classifier - Download as PDF
• Divide and Conquer - Download as PDF
• Decomposed Discovery - Download as PDF
• Distributed discovery and replay - Download as PPTX

Recent projects

• AutoTwin - Description The AutoTwin project addresses the technological shortcoming and economic liability of the development and usage of digital twins that are accepted as the accelerator and enabler of Circular Economy in businesses and production by conduction research in 3 areas: introducing a breakthrough method for automated process-aware discovery towards autonomous Digital Twins generation, to support Read More ...
• Philips Flagship - Description The Data Science Centre Eindhoven (DSC/e) is TU/e’s response to the growing volume and importance of data and the need for data & process scientists (http://www.tue.nl/dsce/). The DSC/e has recently started a long-term strategic cooperation with Philips Research Eindhoven on three topics: data science, health and lighting. As a first concrete action, 70 PhD Read More ...
• BPR4GDPR - Business Process Re-engineering for General Data Protection Regulation Description The goal of BPR4GDPR is to provide a holistic framework able to support end-to-end GDPR-compliant intra- and interorganisational ICT-enabled processes at various scales, while also being generic enough, fulfilling operational requirements covering diverse application domains. To this end, proposed solutions will have a strong semantic foundation Read More ...

Recent publications

• Advanced Process Discovery Techniques - Augusto, A., Carmona, J., & Verbeek, H. M. W. (2022). Advanced Process Discovery Techniques. In Process Mining Handbook (pp. 76-107). (Lecture Notes in Business Information Processing (LNBIP); Vol. 448). Springer. https://doi.org/10.1007/978-3-031-08848-3_3 Abstract Given the challenges associated to the process discovery task, more than a hundred research studies addressed the problem over the past two decades. Read More ...
• Discovering an S-Coverable WF-net using DiSCover - Verbeek, H. M. W. (2022). Discovering an S-Coverable WF-net using DiSCover. In A. Burattin, A. Polyvyanyy, & B. Weber (Eds.), Proceedings of the 2022 4th International Conference on Process Mining (ICPM 2022) (pp. 64-71). IEEE Press. https://doi.org/10.1109/ICPM57379.2022.9980723 Abstract Although many algorithms exist that can discover a WF-net from an event log, only a few (if Read More ...
• Discover Context-Rich Local Process Models (Extended Abstract) - Brunings, M., Fahland, D., & Verbeek, E. (2022). Discover Context-Rich Local Process Models (Extended Abstract). In M. Hassani, A. Koschmider, M. Comuzzi, F. M. Maggi, & L. Pufahl (Eds.), ICPM 2022 Doctoral Consortium and Demo Track 2022: Proceedings of the ICPM Doctoral Consortium and Demo Track 2022 (ICPM-D 2022), Bolzano, Italy, October, 2022 (pp. 100-103). Read More ...
• CPN IDE: An Extensible Replacement for CPN Tools That Uses Access/CPN - Verbeek, E., & Fahland, D. (2021). CPN IDE: An Extensible Replacement for CPN Tools That Uses Access/CPN. In M. Jans, G. Janssenswillen, A. Kalenkova , & F. M. Maggi (Eds.), ICPM 2021 Doctoral Consortium and Demo Track 2021: Proceedings of the ICPM Doctoral Consortium and Demo Track 2021 co-located with 10th International Conference on Process Read More ...
• The Log Skeleton Visualizer in ProM 6.9 - Verbeek, H.M.W. The Log Skeleton Visualizer in ProM 6.9: The winning contribution to the process discovery contest 2019. Int J Softw Tools Technol Transfer, 24(4), 549-561. https://doi.org/10.1007/s10009-021-00618-y Abstract Process discovery is an important area in the field of process mining. To help advance this area, a process discovery contest (PDC) has been set up, which Read More ...
• Log skeletons: a classification approach to process discovery - Verbeek, H. M. W., & Medeiros de Carvalho, R. (2018). Log skeletons: a classification approach to process discovery. arXiv.org. http://arxiv.org/abs/1806.08247 Abstract To test the effectiveness of process discovery algorithms, a Process Discovery Contest (PDC) has been set up. This PDC uses a classification approach to measure this effectiveness: The better the discovered model can classify Read More ...
• Interactive process mining - Dixit, P. A. M. (2019). Interactive process mining Eindhoven: Technische Universiteit Eindhoven
• Improving merging conditions for recomposing conformance checking - Lee, W. L. J., Munoz-Gama, J., Verbeek, H. M. W., van der Aalst, W. M. P., & Sepúlveda, M. (2019). Improving merging conditions for recomposing conformance checking. In F. Daniel, Q. Z. Sheng, & H. Motahari (Eds.), Business Process Management Workshops – BPM 2018 International Workshops, Revised Papers (pp. 31-43). (Lecture Notes in Business Information Read More ...
• Incremental computation of synthesis rules for free-choice Petri nets - Dixit, Prabhakar M., Verbeek, H.M.W. & van der Aalst, Wil M.P. (2018). Incremental computation of synthesis rules for free-choice Petri nets. In Peter Csaba Ölveczky & Kyungmin Bae (Eds.), Formal Aspects of Component Software – 15th International Conference, FACS 2018, Proceedings (pp. 97-117). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence Read More ...
• Interactive data-driven process model construction - Dixit, P. M., Verbeek, H.M.W., Buijs, J. C.A.M. & van der Aalst, W. M.P. (2018). Interactive data-driven process model construction. In Xiaoyong Du, Guoliang Li, Zhanhuai Li, Juan C. Trujillo, Tok Wang Ling, Karen C. Davis & Mong Li Lee (Eds.), Conceptual Modeling – 37th International Conference, ER 2018, Proceedings (pp. 251-265). (Lecture Notes in Read More ...