2025
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 …
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 …
Perseverance Award for Renata Medeiros de Carvalho
During the M&CS End of Year Lunch on Thursday December 18, Renata Carvalho de Medeiros received a Perseverance Award from the M&CS Board. This award allows her to recruit another PhD candidate. Congratulations to Renata!
New PHD student: Laura Didden
On November 1st, 2025, Laura Didden started as a PhD student in the PA group. Laura will be working under the supervision of Francesca Zerbato. A big welcome to Laura!
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!
Best Student Paper Award for Irina Tentina, Francesca Zerbato, Felix Mannhardt and Boudewijn van Dongen
Irina Tentina, Francesca Zerbato, Felix Mannhardt and Boudewijn van Dongen have received the Best Student Paper Award at ICPM 2025 for their paper titled “Why do users struggle to get insights from Process Mining?“. Congratulations to Irina, Francesca, Felix and Boudewijn!
Best Reviewer Award at ICPM 2025 for Francesca Zerbato
Francesca Zerbato has received one of the two Best Reviewer Awards at ICPM 2025. Congratulations to Francesca!
Task-Free Continual Learning with Dynamic Loss for Online Next Activity Prediction
Verbeek, T., Yao, R., & Hassani, M. (2025). Task-Free Continual Learning with Dynamic Loss for Online Next Activity Prediction. In A. Delgado, & T. Slaats (Eds.), Process Mining Workshops: ICPM 2024 International Workshops, Lyngby, Denmark, October 14–18, 2024, Revised Selected Papers (pp. 693-705). (Lecture Notes in Business Information Processing; Vol. 533). Springer. https://doi.org/10.1007/978-3-031-82225-4_51 Abstract Continual learning, known Read More …
Sustainable Traffic Flow Prediction using Contrastive Learning and Spatial Encoding
Xu, Y., & Hassani, M. (2025). Sustainable Traffic Flow Prediction using Contrastive Learning and Spatial Encoding. ACM SIGAPP Applied Computing Review, 25(2), 31-46. https://doi.org/10.1145/3746626.3746629 Abstract Urban traffic networks evolve continuously as cities add and relocate sensors, yet most prediction pipelines must be retrained from scratch to exploit these new data sources. Such retraining is costly Read More …
Leveraging Data Augmentation and Siamese Learning for Predictive Process Monitoring
van Straten, S., Padella, A., & Hassani, M. (2025). Leveraging Data Augmentation and Siamese Learning for Predictive Process Monitoring. arXiv.org. https://doi.org/10.48550/arXiv.2507.18293 Abstract Predictive Process Monitoring (PPM) enables forecasting future events or outcomes of ongoing business process instances based on event logs. However, deep learning PPM approaches are often limited by the low variability and small Read More …
Winter School (2IEIT0) 2025-2026
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:
Foundations of Data Analytics (2IAB1) 2025-2026
General learning goals
EIT_Data Science entry point (2IMC93) 2025-2026
Objectives In this meeting the student gets insight into the study program.
EIT_DS exit point (2IMC98) 2025-2026
Objectives In this meeting the student gets insight into the study program.
DBL Data Challenge (JBG030) 2025-2026
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
BDMA (2IMC99) 2025-2026
Objectives In this meeting the student gets insight into the study program.
Fundamentals of Process Mining (JBI060) 2025-2026
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) 2025-2026
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 …
Advanced Process Mining (2AMI20) 2025-2026
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
Capstone Data Challenge (JBG060) 2025-2026
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) 2025-2026
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) 2025-2026
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 …
New PhD student: Mădălina Rogozan
On September 15, 2025, Mădălina Rogozan started as a PhD student in the PA group. Mădălina will work under the supervision of Natalia Sidorova on the AIDE project on Conformance Checking: Timed, Stochastic, and Systemic Trace Insights. A big welcome to Mădălina!
Process Mining using Graph Neural Network
Process Mining is increasingly shifting to graph-based representations of event data, enabling the application of Graph Neural Networks (GNNs) for various process mining tasks. Recent Master projects developed the first GNN-based process discovery algorithm which trains a GNN to translate an event log into a process model. The GNN achieves state-of-the-art performance in terms of Read More …
Generating Actionable Insights using Object-Centric Process Mining (Visual Analytics, Root-Cause Analysis, Agentic AI)
Industrial practice requires process mining techniques to not just produce models, dashboard, and data visualizations, but to help analysts get insightful answers to relevant questions. The emerging paradigm of object-centric process mining allows a new way to generate such insightful answers by embedding process mining results in the original domain data and context where the Read More …
Realizing High-Performance Object-Centric Event Data
The process mining field is exploring a new data model for event data called “object-centric event data” (OCED). In this data model, events are not partitioned under a unique case identifier, but each event is related to a number of data objects that can also be related to each other. The resulting data model essentially Read More …
Event Knowledge Graphs using RDF and SPARQL
The process mining field recently has adopted a graph-based approach for modeling and reasoning over event data using labeled property graphs. From a process mining perspective, events are related to the various objects and entities involved in a process, providing a more realistic description of actual process dynamics in relation to domain concepts. From knowledge Read More …
ProM 6.15 released
Today, September 26th, ProM 6.15 has been released. This is the second release of ProM for which GitHub was used during development.
Neural Combinatorial Optimization for Stochastic Flexible Job Shop Scheduling Problems
Smit, I. G., Wu, Y., Troubil, P., Zhang, Y., & Nuijten, W. P. M. (2025). Neural Combinatorial Optimization for Stochastic Flexible Job Shop Scheduling Problems. In T. Walsh, J. Shah, & Z. Kolter (Eds.), Proceedings of the 39th Annual AAAI Conference on Artificial Intelligence: AAAI-25 Technical Tracks 25 (pp. 26678-26687). (Proceedings of the AAAI Conference Read More …
Graph Neural Networks for Job Shop Scheduling Problems: A Survey
Smit, I. G., Zhou, J., Reijnen, R., Wu, Y., Chen, J., Zhang, C., Bukhsh, Z., Zhang, Y. & Nuijten, W. P. M. (2025). Graph Neural Networks for Job Shop Scheduling Problems: A Survey. Computers and Operations Research, 176, Article 106914. https://doi.org/10.1016/j.cor.2024.106914 Abstract Job shop scheduling problems (JSSPs) represent a critical and challenging class of combinatorial Read More …
Neuro-Symbolic Learning for Responsible Process Analytics
Highlights Background Business process management has evolved from manual workflow documentation to sophisticated data-driven systems that leverage machine learning for process optimization, anomaly detection, and predictive monitoring. Organizations across healthcare, finance, manufacturing, and logistics generate massive amounts of event logs that capture detailed execution traces of their operational processes. While deep learning approaches have shown remarkable success Read More …
Best Student Paper Award at BPM 2025 for Maike Basmer, Hannes Ueck, Dirk Fahland and Matthias Weidlich
Maike Basmer, Hannes Ueck, Dirk Fahland and Matthias Weidlich have received the Best Student Paper Award at the BPM 2025 conference in Seville with the paper titled “MANTA: Materializing Views on Event Data for Context Exploration in Process Analysis“. Congratulations to Maike, Hannes, Dirk and Matthias!
Can Users Trust Process Mining?
Tentina, I., Mannhardt, F., Zerbato, F., & van Dongen, B. (2025). Can Users Trust Process Mining? In K. Wecel (Ed.), Business Information Systems: 25th International Conference, BIS 2025, Poznań, Poland, June 25–27, 2025, Proceedings (pp. 137-151). (Lecture Notes in Business Information Processing (LNBIP); Vol. 554). Springer. https://doi.org/10.1007/978-3-031-94193-1_11 Abstract Process mining enables organizations to gain insights Read More …