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 …
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
Objectives The winter school will bring students together from different technical majors to work jointly on a business development process, where they learn to: Content The lecturing contents will include:
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 …
General learning goals
Objectives After taking this course students should be able to: have a detailed understanding of the entire process mining spectrum and the methodology for process mining analysis
Objectives In this meeting the student gets insight into the study program.
Objectives In this meeting the student gets insight into the study program.
Objectives In this meeting the student gets insight into the study program.
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 …
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 …
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 …
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 …
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 …
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 …
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 …
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 …
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
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 …
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 …
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 …
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 …
On March 1st, 2026, Nikos Perdikogiannis started as a PhD student in the PA group. He will be working under the supervision of Renata Medeiros de Carvalho, who could recruit a PhD student as a result of the Perseverance Award she received. A big welcome to Nikos!
On February 1st, 2026, Dip Ghosh started as a PhD student in the PA group. He will be working under the supervision of Wim Nuijten, A big welcome to Dip!
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 …
