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Course module: JM0210-M-6
Real-Time Process Mining
Course info
Course moduleJM0210-M-6
Credits (ECTS)6
CategoryMA (Master)
Course typeCourse
Language of instructionEnglish
Offered byTilburg University; Tilburg School of Economics and Management; TiSEM: Management; TiSEM: Management;
Is part of
M Data Science and Entrepreneurship (joint degree)
Academic year2020
Starting block
SM 1
Course mode
RemarksCaution: this information is subject to change
Registration openfrom 25/08/2020 up to and including 20/08/2021
After taking this course students should:
  • be able to relate process mining techniques to other analysis techniques (data mining, model checking, simulation, etc.),
  • understand the positioning of process mining in the context of data science,
  • be able to apply a range of process mining techniques and use tools such as RapidMiner, ProM, and Disco,
  • be able to design analysis workflows and execute them using process discovery and conformance checking techniques on concrete practical datasets (e.g., using RapidMiner and ProM),
  • be able to reason about the strengths and weaknesses of existing process mining algorithms and critically evaluate new ones,
  • be able to use process mining for comparing subgroups of cases and process variants (comparative process mining),
  • compresence the concept of real-time (stream) data mining, and be able to execute available stream clustering and stream classification algorithms
  • describe the dimensionality reduction concept and differentiate between dimensionality reduction techniques
  • be able to identify the business value of real-time process mining,
  • be able to conduct real-world process mining projects (e.g. customer journey optimization) using real data and imprecise questions from stakeholders.


Highly recommended literature:
  • Selected parts of the textbook Process Mining: Discovery, Conformance and Enhancement of Business Processes by W. van der Aalst. Springer-Verlag, Berlin, 2011 (
  • W.M.P. van der Aalst, A. Adriansyah, and B. van Dongen. Replaying History on Process Models for Conformance Checking and Performance Analysis. WIREs Data Mining and Knowledge Discovery, 2(2):182-192, 2012.Slides, event logs, exercises, and additional papers are provided via OASE and
  • Aggarwal, Charu C. (Ed.) Data Streams Models and Algorithms. Springer-Verlag 2007 ISBN 978-0-387-47534-9
  • W.M.P. van der Aalst. Process Mining Data Science in Action. Springer-Verlag 2016 Online ISBN 978-3-662-49851-4 (
The course starts with an overview of the BPM domain using a set of twenty BPM use cases. These cover four key BPM activities: model (creating a process model to be used for analysis or enactment), enact (using a process model to control and support concrete cases), analyze (analyzing a process using a process model and/or event logs), and manage (all other activities, e.g., adjusting the process, reallocating resources, or managing large collections of related process models).

Then the focus shifts to process mining. Process mining bridges the gap between model-based process analyses (e.g., simulation, model checking, and classical BPM techniques) and data-oriented techniques (e.g., data mining techniques like classification, clustering, and regression). Process mining techniques can be applied in a variety of domains ranging. Some examples:

  • Discovering the root causes for delays in treatment processes in a hospital. Which groups of patients are not treated according to the guidelines?
  • Diagnosing the behavior of an X-ray machine that malfunctions and suggesting preventative maintenance. What component should be replaced?
  • Analyzing the "customer journey" of customers that have purchased a product and are using related services. How to seduce customers to purchase more services and additional products?
  • Checking the conformance of processes in local governments to find potential cases of fraud. Why was the formal approval step bypassed frequently?
  • Analyzing the study behavior of students following a Massive Open Online Course (MOOC). What are the differences in study behavior between students that pass and students that fail the course?
  • Analyzing a baggage handling system in an airport to understand where luggage gets delayed or misplaced. When and why is the baggage handling system not meeting the service level agreements?
  • Discovering the actual processes supported by a service desk of a large bank. Why does it take such a long time before a person is found that can assist in solving the problem?

Particular emphasis will be on the performance/management side of process mining and the creation of repeatable analysis workflows using RapidProM.

The course consists of two tracks.

Track 1: Business Process Management and Process Mining Techniques (based on selected papers). Track 1 is assessed by means of a final written test (40%). The track focuses on topics such as the BPM Use Cases, process modeling, simulation, process discovery, conformance checking, performance analysis, process cubes, and prediction

Track 2: Practical hands-on experience with process mining with a particular focus on analysis workflows, scientific process mining experiments, and real-world process mining. This track exposes students to real-life data sets to understand challenges related to process discovery, conformance checking, and model extension. Track 2 is examined by means of an assignment that consists of three parts (60%).

Type of instructions

Lectures and instructions

Type of exams

Track 1: written test (40%)
Track 2: three-part assignment (60%)

Compulsory Reading
Contact person M. Hassani
Timetable information
Real-Time Process Mining
Written test opportunities
Written test opportunities (HIST)
Schriftelijk (60%) / Written (60%)EXAM_01SM 1109-12-2020
Schriftelijk (60%) / Written (60%)EXAM_01SM 1213-01-2021
Required materials
Recommended materials
Written (60%)

Final grade

2 Presentations (40%)

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