The main goal of this course is to introduce process mining as one of the disciplines of data science. In this course, students will learn how to analyze business processes and discover knowledge from process event logs.

Learning objectives

• To be able to relate process mining techniques to the other analysis techniques such as data mining and machine learning;
• Be able to apply basic process discovery techniques to learn a process model from the event log;
• Be able to apply basic conformance checking to compare process models and event logs;
• Be able to extend a process model with information extracted from the event log;
• Have a good understanding of the data needed to start a process mining project;
• To be able to conduct process mining projects in a structured manner.

• Introduction to Course

• Part 1: Introduction (3 Sessions)

  • Business process and BPM

  • Process mining: Definition and tasks

  • Methodologies of process mining projects

• Part 2: Data mining (4 sessions)

  • Data mining: definition and tasks, pre-processing

  • Discovering frequent patterns and association rules
    

  • Classification

  • Clustering

  • Introduction to process mining tools (1 session)

• Part 3: Event logs and process modeling (3 sessions)

  • Event log and data quality

  • Process modeling

  • Process modeling in practice

• Part 4: Process Discovery (4 sessions)

  • Discover the process model

  • Alpha algorithm

  • Process discovery in practice

• Part 5: Conformance Checking (4 sessions)

  • Conformance checking

  • Conformance checking in practice

• Discuss on a real-world scenario (1 session)

• Student seminars (2 sessions)

• Process Mining: Data science in action,  Wil van der Aalst, Springer, 2016
• Data Mining - Concepts and Techniques,   3rd edition,J. Han, M. Kamber, J. Pei, Elsevier, 2012
• Data Mining and Analytics - Fundamental Concepts and Algorithms,  Mohammed J. Zaki, Wagner Meira, JR., Cambridge University, 2014

• 2 Group assignments (2 points in total)
  • 4-5 hours per homework
  • ≈ 10 hours in total
• Project (8 points)
  • ≈ 40 hours
• Seminar (2 points)
  • ≈ 15 hours
• Online quizzes (3 points in total)
• Final exam (5 points)
• Active participation in the class (up to 2 extra points)