Machine Learning applications in Process Mining



Marco Pegoraro

PhD Student


+49 241 80 21948



Course Details

Language: The language of the course is English; therefore, all meetings and the written reports will be in English.


The topic of Process Mining concerns the analysis of event data in the form of event logs; namely, databases containing the execution data of a process detailing events that occurred, linking them to specific cases (specific instantiations of a process), and related features. The case identifier attribute and the timestamp of events define the structure of process execution data: a sequence of activities performed in a specific order, called a trace. Traces are not easily representable in a tabular format, thus needing the development of process-focused data analysis techniques; this allow extract from event data precious insights through data science techniques like prediction, classification and clustering. This seminar illustrates different methods and techniques to train learning algorithm from the execution logs of a process, effectively linking the topics of Process Mining and Machine Learning.


Prior knowledge of the fields of Process Mining and Machine Learning is required. Ideal prerequisite include:

- Participation in the Introduction to Data Science (IDS) course

- Participation in the Business Process Intelligence (BPI) course

- Participation in the "Process Mining: Data Science in Action" MOOC on Coursera

- Having read the book "Process Mining: Data Science in Action" by Wil van der Aalst

This teaching will be held in English.


The registration is carried out by the central registration process (Supra) in July 2020

Please provide a list of the relevant courses, seminars, and practical labs you successfully attended (with grades). Add relevant experience in industry or open source projects. Explain your interest in this field in detail and your motivation to partake this seminar. State if you have already completed a seminar successfully (if yes, give details), and if already resigned a seminar. Please note that only the students who will participate in the seminar will be contacted later on.