In this lab, students will work in small teams to implement algorithms and techniques from the field of Process Mining. The goal is to gain hands-on experience with modern process analysis methods and to deepen understanding by translating theoretical concepts into working software.
Possible topics (to be finalized at the beginning of the course):
- LLM-assisted Process Mining Techniques
- Event Abstraction and Transformation
- Clustering in Process Mining
- Partially-Ordered Event Data
- Predictive Process Mining
Technical Requirements:
- Solid programming skills in Python are mandatory (the lab will primarily use the PM4Py library)
- For visualization tasks, knowledge of web technologies (HTML, CSS, JavaScript, TypeScript) is helpful
Language:
The lab will be held in English or German, depending on the composition of the participants.
Publication Opportunity:
Depending on student interest, motivation, and result quality, project results may be
further developed and submitted as a tool/workshop/full paper to an international
conference upon completion of the lab.
- Teacher: Daniel Schuster
- Teacher: Daniel Diefenthaler
- Teacher: Fabian Dreer
- Teacher: Florian Krötz
- Teacher: Korbinian Staudacher
- Teacher: Xiao-Ting To
- Teacher: Nina Freise
- Teacher: Alexander Klingebiel
- Teacher: Sebastian Wölckert