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Course module: 2IMI20
Advanced process mining
Course info
Course module2IMI20
Credits (ECTS)5
Course typeGraduate School
Language of instructionEnglish
Offered byEindhoven University of Technology; Mathematics and Computer Science; Computer Science;
Is part of
Business Information Systems
Computer Science and Engineering
Data Science in Engineering (CSE)
Contact W.M.P. van der Aalst
Contactperson for the course W.M.P. van der Aalst
Other course modules lecturer
Academic year2016
2  (14/11/2016 to 05/02/2017)
Starting block
TimeslotD1: D1 - We 5-6, Fr 1-2
Course mode
Registration openfrom 15/06/2016 up to and including 23/10/2016
Application procedureYou apply via OSIRIS Student
Registration using OSIRISYes
Registration open for students from other department(s)Yes
Waiting listNo
Number of insufficient tests-
Number of groups of preference1
Learning objectives
After taking this course students should:

  • have a detailed understanding of the entire process mining spectrum and be able to relate process mining techniques to other analysis techniques for business processes,
  • master various representational biases for process mining (subclasses of Petri nets, structured process models, C-nets, etc.),
  • understand and apply advanced process discovery techniques based on regions and genetic algorithms,
  • be able to discuss all four conformance dimensions (replay fitness, precision, generalization, and simplicity), provide metrics for these dimensions, and apply conformance checking using models and logs,
  • be able to reason about the strengths and weaknesses of existing process mining algorithms and critically evaluate new ones.
  • Content
    Process mining provides a new means to improve processes in a variety of application domains. There are two main drivers for this new technology. On the one hand, more and more events are being recorded thus providing detailed information about the history of processes. On the other hand, in most organizations there is a need to improve process performance (e.g., reduce costs and flow time) and compliance (e.g., avoid deviations or risks). This advanced course on process mining teaches students the theoretical foundations of process mining and exposes students to real-life data sets to understand challenges related to process discovery, conformance checking, and model extension.

    The course will cover various advanced process discovery techniques, i.e., techniques based on region theory and genetic algorithms. One needs to be able to understand such techniques, apply them, and know their strengths and weaknesses.

    The course will also cover conformance checking techniques covering all four conformance dimensions: replay fitness, precision, generalization, and simplicity. A key element is the notion of alignments linking observed to modeled behavior.

    Process mining techniques will not be limited to control-flow and will also include other perspectives such as time (bottleneck analysis), resources (social network analysis), and data (decision mining).

    Besides learning theoretical concepts, students will be exposed to event data from a variety of domains, including hospitals, insurance companies, governments, high-tech systems, etc. The assignment will either focus on the analysis of such data sets or on focusing on a particular process mining problem.

    Note that the bachelor course Business Process Intelligence (2IIE0) introduces process mining at an introductory level. This course is not required as prior knowledge, i.e., 2IMI20 does not depend on 2IIE0. However, students can benefit from 2IIE0 to already have an initial understanding of process mining and being familiar with some of the simpler process mining algorithms.
    Entrance requirements
    Entrance requirements tests
    Assumed previous knowledge
    2ID50 - Datamodelling and databases
    2IIC0 - Business information systems
    Previous knowledge can be gained by
    Resources for self study
    Bachelor College or Graduate School
    Graduate School
    URL study guide
    URL study guide
    Required materials
    Recommended materials
    Papers, slides, event logs, and exercises are provided via OASE and
    The textbook W. van der Aalst. Process Mining: Data Science in Action. Springer-Verlag, Berlin, 2016 ( serves as background information.
    Instructional modes
    College / course


    Test weight100
    Minimum grade6
    Test typeWritten
    Number of opportunities3
    OpportunitiesBlock 2, Block 3, Block I
    Test duration in minutes180


    The mark will be based on an assignment (4 points) and a written exam (6 points, 3 hours). The assignment expires after

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