Prof. Dr. Koschmider: Business Informatics and Process Analytics

Chair Description

Our research interest broadly explores

How to design a framework for the efficient processing of low-level data allowing to extract process knowledge?
How can machine learning be used to increase data quality (e.g. noise) and thus accelerate data and process analysis?
How can synthetic data be efficiently generated that enable privacy-awareness or distributed analysis?
How can machine learning be used to reduce the involvement of users, but to increase the quality of the data-driven, discovered processes?

Chair holder: Prof. Dr. Agnes Koschmider