Publication
Hands-on Process Discovery with Python - Utilizing Jupyter Notebook for the Digital Assistance in Higher Education
Adrian Rebmann; Alexander Beuther; Steffen Schuhmann; Peter Fettke
In: Judith Michael; Dominik Bork (Hrsg.). Modellierung 2020 Short, Workshop and Tools & Demo Papers. Fachtagung "Modellierung" der Gesellschaft für Informatik (GI) (MoHoL-2020), Modellierung in der Hochschullehre, located at Modellierung 2020, February 19-21, Vienna, Austria, Pages 65-76, CEUR, 2020.
Abstract
The university course Process Mining aims at teaching contents as practically as possible. Students should therefore be able to apply the theoretical knowledge acquired in the course to real-world scenarios. In order to bridge the gap between these theoretical foundations and real-world applications, we propose Jupyter Notebook as an interactive learning environment for the introduction to process model discovery. To this end, we present an exercise, which consists to a large extent of the data preparation of process execution data, distributed in database tables. From this, a meaningful event log is to be generated and consequently, process model discovery techniques are to be applied. All descriptions and data that are necessary to complete the exercise, are provided through a single notebook. This guides the students throughout the process and also eases the grading for the instructors. The proposed exercise is meant as a pilot for evaluating the teaching approach in the course. We conducted initial tests with university staff, resulting in positive feedback. An in-depth evaluation is planned during this semester’s edition of the course.