| Position: | PhD, defended on May 15, 2025 |
| Room: | MF 7.117 |
| Tel (internal): | 8723 |
| Links: | Courses Presentations Projects Publications |
| External links: | Google scholar page Scopus page ORCID page DBLP page TU/e page |
Recent courses
Recent presentations
Recent projects
- Certif-AI - Certif-AI: Certification of production process quality through Artificial Intelligence Description Production processes can be made ‘smarter’ by exploiting the data streams that are generated by the machines that are used in production. In particular these data streams can be mined to build a model of the production process as it was really executed – as Read More ...
Recent publications
- 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 ...
- Illuminating Perspectives of Deviations in Process Behavior - Sommers, D. (Accepted/In press). Illuminating Perspectives of Deviations in Process Behavior. [Phd Thesis 1 (Research TU/e / Graduation TU/e), Mathematics and Computer Science]. Eindhoven University of Technology.
- Exact and Approximated Log Alignments for Processes with Inter-case Dependencies - Sommers, D., Sidorova, N., & van Dongen, B. F. (2023). Exact and Approximated Log Alignments for Processes with Inter-case Dependencies. arXiv, 2023, Article 2304.05210. https://doi.org/10.48550/arXiv.2304.05210 Abstract The execution of different cases of a process is often restricted by inter-case dependencies through e.g., queueing or shared resources. Various high-level Petri net formalisms have been proposed that Read More ...
- Supervised learning of process discovery techniques using graph neural networks - Sommers, D., Menkovski, V., & Fahland, D. (2023). Supervised learning of process discovery techniques using graph neural networks. Information Systems, 115, Article 102209. https://doi.org/10.1016/j.is.2023.102209 Abstract Automatically discovering a process model from an event log is the prime problem in process mining. This task is so far approached as an unsupervised learning problem through graph synthesis Read More ...
- Aligning Event Logs to Resource-Constrained ν-Petri Nets - Sommers, D., Sidorova, N., & van Dongen, B. (2022). Aligning Event Logs to Resource-Constrained ν-Petri Nets. In L. Bernardinello, & L. Petrucci (Eds.), Application and Theory of Petri Nets and Concurrency – 43rd International Conference, PETRI NETS 2022, Proceedings (pp. 325-345). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Read More ...
- Process Discovery Using Graph Neural Networks - Sommers, D., Menkovski, V., & Fahland, D. (2021). Process Discovery Using Graph Neural Networks. In C. Di Ciccio, C. Di Francescomarino, & P. Soffer (Eds.), Proceedings – 2021 3rd International Conference on Process Mining, ICPM 2021 (pp. 40-47) https://doi.org/10.1109/ICPM53251.2021.9576849 Abstract Automatically discovering a process model from an event log is the prime problem in process Read More ...
Recent awards
- Best Paper award at ICPM 2021 for Dominique Sommers - Dominique Sommers, Vlado Menkovski, and Dirk Fahland have won the Best Paper award at ICPM 2021 with their paper “Process Discovery using Graph Neural Networks“. Congratulations to Dominique, Vlado, and Dirk!