Research Principles

We aim to develop novel techniques, methods and tools in the process mining domain. The tight integration of the Process Mining group at Fraunhofer with the Process and Data Science chair of the RWTH Aachen University, allows for a unique knowledge exchange between top researchers in the field. Furthermore, the close cooperation of our group with industry allows for the identification of problems and corresponding solutions that are of actual business value.

As such, our research portfolio is characterized by the following guiding principles:

Business / Societal Impact

The tools, algorithms, methods and techniques developed by us are of great value for business and / or society. Driven by the goal to bridge the gap between academia and business / society, the solutions proposed by us have a strong emphasis on applicability. The techniques developed are typically applied on real data sets, and are preferably applied in practice

Tool Support

We strive to provide open source software corresponding to the algorithms developed. As such, the work performed by us is publicly available and therefore inspires and helps accelerate other research as well as industry adoption.

Ongoing Research Projects

Automated Operational Process Improvement (AOPI)

The main aim of the AOPI project is to develop technology making it possible to support automated process improvement in a data-driven manner. We envision an interactive tool that supports and integrates multiple process mining disciplines such as process discovery, conformance checking and enhancement. For example, by highlighting deviations on the currently discovered mode, the user is able to (possibly manually) change the model to better describe reality.

Process Mining for Python (pm4py)

The Process Mining group of Fraunhofer FIT plays an active and leading role of the process mining for python (pm4py) python library. The pm4py library comprises of a vast array of process mining algorithms supporting all the aspects of process mining, i.e. process discovery, conformance checking and process enhancement. The library allows both researchers and industry professionals to more efficiently adopt process mining techniques in their analyses, and, allows for quick adoption with other data science algorithms and tools.