Process Mining

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This course focuses on the missing link between model-based process analysis and data-oriented analysis techniques
The course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains
The course explains various process discovery algorithms
The course introduces conformance techniques to compare processes and event data
The course provides students the opportunity to experiment with tools

At the end of the course, the students will gain familiarity with process mining terminology, methodology and technologies
Be able to apply basic process discovery techniques to learn a process model from an event log
Be able to apply basic conformance checking techniques to compare event logs and process models
Have a good understanding of the data needed to start a process mining project
Be able to conduct process mining projects

Course Contents

Introduction
- From Internet of Events to Process Mining
- Logs: Play-in, Play Out and Reply
- Positioning Process Mining

From Event Logs to Process Models
- Getting Data
- Process Discovery
- Advance Process Discovery Techniques
- Tools support

Putting Process Mining to Work
- Business Activity Monitoring, KPI and Improvement
- Blockchain Technologies
- Sensors, Internet-of-Things (IoT) and wearable devices

Course Slides Classroom with Slide

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Reference book:

Process Mining: Data Science in Action by W.M.P. van der Aalst, Springer Verlag, 2016 (ISBN 978-3-662-49850-7).
Fundamentals of Business Process Management. Marlon Dumas, Marcello La Rosa, Jan Mendling, Hajo Reijers. Springer. II Edition.

Tools:

Exams Rules:

On the exam date, a written examination takes place; it has a mixed structure: solution of exercises and open/close answer questionnaire.
To be admitted to the written text, the students have to run a project with the APROMORE software tool and deliver a report describing the project objective, findings, and lesson learned.