- Trainer/in: Daniel Schuster
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.
- Trainer/in: Daniel Schuster
- Trainer/in: Daniel Diefenthaler
- Trainer/in: Fabian Dreer
- Trainer/in: Markus Baumann
- Trainer/in: Jonas Stein
- Trainer/in: Florian Krötz
- Trainer/in: Korbinian Staudacher
- Trainer/in: Xiao-Ting To
- Trainer/in: Nina Freise
- Trainer/in: Alexander Klingebiel
- Trainer/in: Sebastian Wölckert