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Humboldt-Universität zu Berlin - Faculty of Mathematics and Natural Sciences - 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 seminar-style presentations on state-of-the-art developments in the field. Each participant will be asked to read a recent research paper on process mining (selection from a given list) and give a critical assessment of the approach presented in the paper in the form of a 45min presentation.

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 exam, each student will be required to give a presentation (45min) on a research paper in the second part of the course. 

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".



VL Fr 9-13 RUD 26, Raum 1'306
UE Tu 11-13 RUD 26, Raum 1'307


No meeting on Tuesday, 22.10.2019.


See AGNES for further details: