Direkt zum InhaltDirekt zur SucheDirekt zur Navigation
▼ Zielgruppen ▼

Humboldt-Universität zu Berlin - Mathematisch-Naturwissenschaftliche Fakultät - Process-Driven Architectures

Process Mining (VL/UE)

Prof. Dr. Matthias Weidlich



One emerging branch of data science is process mining. In the field of process automation, process mining aims at deriving qualitative and quantitative insights on the execution of a process based on recorded events logs.


The course features lectures and recitations that focus on the formal foundations and basic techniques of process mining. Specifically, this includes algorithms for process discovery that construct models from event data. Also, essential conformance checking techniques to identify deviations between models and event data, e.g., by replay or alignment construction will be discussed. Finally, advanced techniques for model extension, process simulation, and performance prediction will be reviewed. As part of excercises, course participants will be exposed to real-world data and prototype process mining techniques.

The lectures and recitations are complemented by a practical project. Students work in groups and are asked to read recent research papers on process mining. They should then conduct one of the announced tasks and report on the obtained results in a project report.

The course will be given in English.


There will be an oral exam at the end of the semester. To be eligible to take the final exam and earn the LP, each student will be required to successfully complete a project task during the semester.

The course can only be taken by students that have not completed the old module Q5-5 of the same name.

Credit Points

The course counts for 9 LP and is open for: Informatik, Master of Science (M.Sc.) Informatik, Master of Education (M.Ed.) Wirtschaftsinformatik, Master of Science (M.Sc.). The related area of specialisation is "Daten- und Wissensmanagement".


Lectures start on Friday, November 6. Exercises start only in the second week.

VL Fr 9-13 Online
UE We 15-17 Online


See AGNES for further details: