Process Intelligence in Action (2AMI30) 2026-2027

Objectives After taking the course, students are able to:  Content Students learn how to plan, conduct and critically evaluate process intelligence projects in practice. They learn to follow research methodology to choose and combine appropriate techniques to generate and evaluate relevant insights from process logs and models. Students  work on a challenge with a real-word Read More …

DBL Data Challenge (JBG030) 2026-2027

Objectives Non Bachelor Data Science wanting to register for this course should reach out to program management via mcs.academic.advisor.bds@tue.nl for formal approval After taking the course, students are able to Content Non Bachelor Data Science wanting to register for this course should reach out to program management via mcs.academic.advisor.bds@tue.nl for formal approval

Fundamentals of Process Mining (JBI060) 2026-2027

Objectives The main learning objective of FPM is to be able to understand the relation between sequential multi-variate event data and processes and, ultimately, to learn how to properly apply data science methods in the context of processes. Specifically, the following learning objectives are intended:  to be able to identify challenges and requirements for using Read More …

Seminar Process Analytics (2IMI00) 2026-2027

Objectives This seminar combines teaching research methods (in preparation for a Master project) with providing students with recent and ongoing research in the area of event data analysis and process analysis. We study recent research articles, book chapters, and Master theses on topics along the entire analysis life-cycle. Through presentation and group discussions, we work Read More …

Capstone Data Challenge (JBG060) 2026-2027

Objectives Non Bachelor Data Science wanting to register for this course should reach out to program management via mcs.academic.advisor.bds@tue.nl for formal approval After taking this course students should be able to independently: Content Non Bachelor Data Science wanting to register for this course should reach out to program management via mcs.academic.advisor.bds@tue.nl for formal approval The objective of Read More …

Foundations of Process Mining (2AMI10) 2026-2027

Objectives After taking this course students should: Content 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. Read More …

Capita Selecta Process Analytics (2IMI05) 2026-2027

Objectives Acquire state-of-the-art scientific knowledge of a particular topic in the information systems domain. Typical topics are process modeling, workflow management, process mining, web services, service oriented architectures, language transformations, etc. Content People interested in the ‘process side’ of information systems can take the course ‘Capita selecta architecture of information systems’. This course will be Read More …

Uncharted Workflows: Process Mining Perspectives in Health

Sidorova, N., & Medeiros de Carvalho, R. (2026). Uncharted Workflows: Process Mining Perspectives in Health. In J. Mendling, S. Leemans, B. F. van Dongen, & H. Reijers (Eds.), Mining a Scientist’s Process: Essays Dedicated to Wil van der Aalst on the Occasion of His 60th Birthday (pp. 648-661). (Lecture Notes in Computer Science (LNCS); Vol. 16480). Read More …

Chameleons do not Forget: Prompt-Based Online Continual Learning for Next Activity Prediction

Hassani, M., Verbeek, T., & van Straten, S. (2026). Chameleons do not Forget: Prompt-Based Online Continual Learning for Next Activity Prediction. arXiv.org. https://doi.org/10.48550/arXiv.2604.00653 Abstract Predictive process monitoring (PPM) focuses on predicting future process trajectories, including next activity predictions. This is crucial in dynamic environments where processes change or face uncertainty. However, current frameworks often assume Read More …

Leveraging Data Augmentation and Siamese Learning for Predictive Process Monitoring

van Straten, S., Padella, A., & Hassani, M. (2026). Leveraging Data Augmentation and Siamese Learning for Predictive Process Monitoring. In C. Cappiello, O. Hartig, M. Sellami, & A. Ouni (Eds.), Cooperative Information Systems: 31st International Conference, CoopIS 2025, Marbella, Spain, October 20–22, 2025, Proceedings (pp. 70-87). (Lecture Notes in Computer Science (LNCS); Vol. 15535). Springer. https://doi.org/10.1007/978-3-032-15538-2_5 Abstract Read More …

Vacancy: Assistant Professor In Process Mining of Variable Processes

Introduction Are you fascinated by understanding how complex, highly variable processes behave in the real world? Are you eager to develop new methods that push the boundaries of process mining research? Process mining research focuses on analyzing abstract objects—often referred to as cases—as they move through a system. These objects generate events, which typically serve Read More …

Mining a Scientist’s Process: Essays Dedicated to Wil van der Aalst on the Occasion of His 60th Birthday

Mendling, J., Leemans, S. J. J., Dongen, van, B. F., & Reijers, H. A. (Eds.) (2026). Mining a Scientist’s Process: Essays Dedicated to Wil van der Aalst on the Occasion of His 60th Birthday. (Lecture Notes in Computer Science; Vol. 16480). Springer. https://doi.org/10.1007/978-3-032-17618-9

Vacancy: PhD in Process-Aware AI Agents for Autonomous Manufacturing

Are you fascinated by the intersection of probabilistic reasoning, process modelling, and artificial intelligence grounded in contextual knowledge? Do you want to develop AI agents that can autonomously reason, plan, and act in complex manufacturing environments based on knowledge graphs? Join a European research consortium and help shape the future of intelligent manufacturing. Job Description Read More …

Object-centric process management: A research manifesto

Seidel, A., Weske, M., Montali, M., Rivkin, A., Reichert, M., van der Werf, J. M. E. M., van der Aalst, W. M. P., Breitmayer, M., Liss, L., van Detten, J. N., Jalali, A., Khayatbashi, S., König, M., Lichtenstein, T., Rinderle-Ma, S., Weber, B., Soffer, P., Rossi, L., Calegari Garcia, D., Delgado, A., Dijkman, R., Winkler, Read More …

What is an Object-Centric Case? An Exploration

Fahland, D., & Montali, M. (2026). What is an Object-Centric Case? An Exploration. In J. Mendling, S. Leemans, B. F. van Dongen, & H. Reijers (Eds.), Mining a Scientist’s Process: Essays Dedicated to Wil van der Aalst on the Occasion of His 60th Birthday (pp. 398-425). (Lecture Notes in Computer Science; Vol. 16480 LNCS). Springer. https://doi.org/10.1007/978-3-032-17618-9_28 Read More …

Explainable conformance checking: Understanding patterns of anomalous behavior

Mozafari Mehr, A. S., M. de Carvalho, R., & van Dongen, B. (2023). Explainable conformance checking: Understanding patterns of anomalous behavior. Engineering Applications of Artificial Intelligence, 126(Part B.), Article 106827. https://doi.org/10.1016/j.engappai.2023.106827 Abstract Anomaly detection in the execution of business processes in the organizations has a high level of complexity due to the consideration of various Read More …

Improving quality processes of Philips business units

Problem statement Important feedback on the quality status of Philips equipment in the field comes from customer complaints, software logs, service notes, and replaced parts. Millions of records are collected worldwide. To identify improvement opportunities, data is captured, monitored, and analysed. Philips has started using AI agents to make such data analysis more effective and Read More …

Explainable Optimization: Leveraging Large Language Models for User-Friendly Explanations

Biemans, B. C. M., Troubil, P., Grau, I., & Nuijten, W. P. M. (2025). Explainable Optimization: Leveraging Large Language Models for User-Friendly Explanations. In R. Guidotti, U. Schmid, & L. Longo (Eds.), Explainable Artificial Intelligence: Third World Conference, xAI 2025, Istanbul, Turkey, July 9–11, 2025, Proceedings (Vol. Part III, pp. 44-67). (Communications in Computer and Read More …

Revisiting Expected Possession Value in Football: Introducing a U-Net Architecture, Reward and Risk for Passes, and a Benchmark

Overmeer, T., Janssen, T., & Nuijten, W. P. M. (2025). Revisiting Expected Possession Value in Football: Introducing a U-Net Architecture, Reward and Risk for Passes, and a Benchmark. In R. Arellano, P. Morouco, & C. Capelli (Eds.), Proceedings of the 13th International Conference on Sport Sciences Research and Technology Support, IcSPORTS 2025 (pp. 100-109). (International Read More …

In system alignments we trust! Explainable alignments via projections

Sommers, D., Sidorova, N., & van Dongen, B. (2026). In system alignments we trust! Explainable alignments via projections. Information Systems, 136, Article 102631. https://doi.org/10.1016/j.is.2025.102631 Abstract Alignments are a well-known process mining technique for reconciling system logs and normative process models. Evidence of certain behaviors in a real system may only be present in one representation Read More …

A Ground Truth Approach for Assessing Process Mining Techniques

Sommers, D., Sidorova, N., & van Dongen, B. F. (2025). A Ground Truth Approach for Assessing Process Mining Techniques. arXiv.org. https://doi.org/10.48550/arXiv.2501.14345 Abstract The assessment of process mining techniques using real-life data is often compromised by the lack of ground truth knowledge, the presence of non-essential outliers in system behavior and recording errors in event logs. Read More …

Timed Anti-Alignments for Acyclic Marked Graphs

Bavaro, S., Chatain, T., & van Dongen, B. F. (2025). Timed Anti-Alignments for Acyclic Marked Graphs. In M. Köhler-Bußmeier, D. Moldt, H. Rölke, R. Bergenthum, A. Rivkin, J. M. E. M. van der Werf, J. Desel, & L. Petrucci (Eds.), PN-WS 2025 : Joint Workshop Proceedings of PNSE, ATAED, and PeNGE at PETRI NETS 2025: Read More …

Why Do Users Struggle to Get Insights from Process Mining?

Tentina, I., Zerbato, F., Mannhardt, F., & van Dongen, B. F. (2025). Why Do Users Struggle to Get Insights from Process Mining? In J. De Weerdt, J.-R. Rehse, & H. Reijers (Eds.), 2025 7th International Conference on Process Mining, ICPM 2025 Article 11220617 Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICPM66919.2025.11220617 (best student paper award) Abstract Read More …

MANTA: Materializing Views on Event Data for Context Exploration in Process Analysis

Basmer, M., Ueck, H., Fahland, D., & Weidlich, M. (2025). MANTA: Materializing Views on Event Data for Context Exploration in Process Analysis. In A. Senderovich, C. Cabanillas, I. Vanderfeesten, & H. A. Reijers (Eds.), Business Process Management: 23rd International Conference, BPM 2025, Seville, Spain, August 31 – September 5, 2025, Proceedings (pp. 51-68). (Lecture Notes in Computer Science Read More …

From Process Mining to Thinking Assistants in Logistics

van Montfort, J., Bernard, H. F., & Fahland, D. (2025). From Process Mining to Thinking Assistants in Logistics. In J. vom Brocke, J. Mendling, & M. Rosemann (Eds.), Business Process Management Cases Vol. 3: Implementation in Practice (pp. 73-86). Springer Nature. https://doi.org/10.1007/978-3-031-80793-0_6 Abstract (a) Situation faced: Vanderlande Industries BV, or Vanderlande for short (VI), is Read More …

Laura Didden

Laura Didden is a Ph.D. student at the Eindhoven University of Technology – Data Science Domain – Process Analytics cluster. She works under the supervision of Francesca Zerbato. Position: PhD Room: MF 7.117 Tel (internal): Links: External links TU/e page Recent courses Recent presentations Recent projects Recent publications

Best Paper Award for Irina Tentina, Iris Beerepoot, Xixi Lu, Hajo Reijers, Boudewijn van Dongen and Vinicius Stein Dani

Irina Tentina, Iris Beerepoot, Xixi Lu, Hajo Reijers, Boudewijn van Dongen and Vinicius Stein Dani have received the Best Paper Award At the EduPM 2025 workshop at ICPM 2025 with the paper titled “Challenges in Teaching Process Mining: Insights from Process Mining Educators”. Congratulations to Irina, Iris, Xixi, Hajo, Boudewijn and Vinicius!