Dominique Sommers

Position: PhD
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

  • Foundations of Process Mining (2AMI10) 2024-2025 - Objectives After taking this course students should: have a good understanding of process mining, understand the role of data science in today’s society, be able to relate process mining techniques to other analysis techniques such as simulation, business intelligence, data mining, machine learning, and verification, be able to apply basic process discovery techniques to learn Read More ...

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

  • 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!

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