Process Mining

The students know the key use cases of process mining. They can conduct process mining projects in a structured manner and have a good understanding of process mining techniques: First, they can apply the various process discovery algorithms to derive a process model from an event log (both manually and using tools). Second, they are able to apply basic conformance checking techniques to compare event logs and process models (both manually and using tools). Third, they can extend a process model with information extracted from an event log (e.g., show bottlenecks). The students have a good understanding of the data needed to start a process mining project and they are able to characterize the questions that can be answered based on such event data. Finally, they can relate process mining techniques to other analysis techniques such as simulation, business intelligence, data mining, machine learning, and verification and they can explain how process mining can be used for operational support.

Module Manual


Course starts at Tuesday, 24th October, 10:00 in Room O27 / 545


All materials (e.g., slides, examples, exercises, ...) are distributes via Moodle. Please register in order to download the required materials.


Through concrete data sets and easy to use process mining tools, the course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains. Furthermore, the course presents many examples using real-life event logs to illustrate the basic concepts and algorithms of process mining. The course topics are as follows:

  • Process-aware information systems
  • Process representation formalisms
  • Process event logs
  • Process discovery algorithms
  • Conformance checking techniques
  • Process enhancement
  • Process mining tools

Contact Person

Johannes Schobel


4 semester hours, 6 ECTS


Every winter term