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.