Project Title:  "Building Semantic Models for the Process Mining Pipeline"

Motivation​:

​Process data analysis is a cornerstone of modern business process improvement efforts.A common analysis technique is process mining, where process data is queried from event logs. However, in process mining, substantial human effort is necessary to prepare the analysis that eventually leads to actionable insights. In order to scale the work that experts put into process mining-based analyses across organizations, the objective of the project is to define, represent, exploit, and update semantic models for developing a process mining recommender system. The underlying question is: Can we produce, exploit, and scale the expert knowledge that is used for analysis generation? A key challenge in this context is the integration of human and machine-driven efforts to make process mining analyses re-usable. 

Chair:

Prof. Dr. Stefanie Rinderle-Ma, Information Systems and Business Process Management