Advanced Process Mining

IS 515 for Master's program (Business Informatics)

General Information

Spring 2024
Lecturer Prof. Dr. Jana-Rebecca Rehse
Course Format Lecture and Exercise
Prerequisites You are expected to be familiar with the use of Petri nets and BPMN for process modeling, and being able to do basic programming in Python. IS 515 or having experience with process mining are NOT prerequisites.
Credit Points 6 ECTS
Language English
Grading Written exam (80%) and group project (20%, mandatory)
Exam Date tbd
Information for Students Attention: This course is limited to 80 participants. Please register in time via Portal² and carefully follow the instructions provided in Portal². It is sufficient to register for the lecture only.
Prof. Dr. Jana-Rebecca Rehse

Prof. Dr. Jana-Rebecca Rehse

Junior Professorship for Management Analytics
University of Mannheim
Assistant Professor of Management Analytics
L 15, 1–6 – Room 413
68161 Mannheim

Course Information

  • Short Description

    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.

    • Objectives

      Upon successful completion of 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
      • Analyze real-world data using open-source process mining tools
    • Lecture

      Lecturer Prof. Dr. Jana-Rebecca Rehse
      Tutors Alexander Kraus, Adrian Rebmann

      Lectures take place in attendance.

    • Exercises

      The exercises take place on campus on Thursday and Wednesday, each session from 15:30 to 17:00. You can choose which exercise you attend. Both sessions will be taught by the same teaching assistant and have identical contents.

      For each exercise session, we expect that you have attended the corresponding lecture the week before or have otherwise familiarized yourself with the lecture's contents. You can also already familiarize yourself with the exercises, but it’s not necessary to have completed the exercises to attend the session.

      These sessions will be active. Each session will start with a Kahoot! quiz to recap what you learned in the lecture, followed by a Q&A regarding the week’s lecture materials. Afterwards, you have the option to work on the exercises (collaboratively or individually) and ask questions about them.

      Because of the active nature of the exercise sessions, they cannot be recorded. However, we will upload the quiz questions to ILIAS.

    • Case Study

      This course contains a (mandatory) case study. More information will be posted to ILIAS after the course has started.