Photo credit: Anna Logue

IE 677: Advanced Process Mining (FSS 2021)

Process mining is an emerging branch of data science that aims at deriving qualitative and quantitative insights on the execution of organizational processes, based on the analysis of recorded event sequences.

The course features lectures and exercises that focus on the formal foundations, algorithms, and techniques of process mining. Specifically, this course covers aspects such as:

  • Process discovery, which aims to derive a process model from recorded events
  • Conformance checking, which aims to identify deviations between event data and process models
  • Process enhancement, which aims to augment process models with information on the temporal, organizational, and data perspectives of a process
  • Predictive monitoring, which aims to make predictions about ongoing process instances
  • Techniques to preprocess, abstract, cluster event data for improved analyses

For the above subjects, the course will cover fundamental algorithms as well as advanced, state-of-the-art techniques.

During the exercises that follow each lecture, you will practice through pen-and-paper exercises, as well as implementation and evaluation using open source process mining tools and libraries.

The lectures and exercises are complemented by a practical assignment in which students will work in groups on a project that involves implementation and/or evaluation of a process mining technique.


After completing this course, you will be able to:

  • Understand the importance and potential of process mining
  • Know and apply both fundamental and advanced techniques for core process mining tasks
  • Be able to analyze real-world data using open-source process mining tools



Basic programming knowledge, preferably in python, is strongly recommended. Experience with Petri nets, other process model notations, or basic process mining is helpful but not necessary. IS 514 is NOT a prerequisite.