Process Science

Head of the research group Process Science

Our research focuses on the technological aspects of business process management. We develop methods, algorithms, models, and tools that simplify and streamline business processes. Current topics are (Social) Process Mining, Predictive Process Monitoring, Business Rules Management, Business Process Modeling Recommender Systems, Business Process Compliance, (Quantum) Machine Learning in BPM, and Ontology-based Process Engineering. The developed artifacts always aim at simplifying and streamlining everyday business operations to create added value for companies and their customers and employees.

Contact

Loading

Research

Our research focuses on the technological aspects of business process management. We develop methods, algorithms, models, and tools that simplify and streamline business processes. Current topics are (Social) Process Mining, Predictive Process Monitoring, Business Rules Management, Business Process Modeling Recommender Systems, Business Process Compliance, (Quantum) Machine Learning in BPM, and Ontology-based Process Engineering. The developed artifacts always aim at simplifying and streamlining everyday business operations to create added value for companies and their customers and employees. As methodological foundations, we use automatable procedures from algorithmic graph theory, computational linguistics, ontologies, formal logic, (quantum) machine learning, software engineering, and primarily quantitative empirical analysis methods (including eye-tracking) for benefit evaluation. Newly developed methods are applied with practice partners to ensure their dissemination into practice and practical applicability and usefulness in the company.

Process modeling

Process mining

Software

During the research processes of the research group, multiple tools and software have been created. Those artifacts can be downloaded here:

[εm]

Often standard software is only partly suitable for performing modeling projects, while the development of individual software is very expensive. But with [εm] you have found the ideal tool for all types of modeling companies, because the modeling does not start with the development of the to be created models, but already when the modeling language itself is defined. In this way the modeling language can be adapted to any context. The models are adaptable to individual needs through configuration mechanisms, without having redundancies or inconsistencies. Therefore [εm] is a tool which you can completely adjust to fit your personal needs.

This software is aimed at both experienced developers as well as at beginners in the domain of modeling. Through the intuitive interaction it is possible to create and edit languages and models and finally to compare existing patterns.

Publications

Riehle, D. M., Höhenberger, S., Brunk, J., Delfmann, P., & Becker, J. (2017). [εm] — Process Analysis using a Meta Modeling Tool. In Proceedings of the Conceptual Modeling Demos (ER Demos 2017), Valencia, Spain, 334–337.

Downloads

current version

Download

All versions

RegPFA

The name RegPFA is a combination of "regularization" and "Probabilistic Finite Automaton". The RegPFA algorithm creates a probabilistic model from event logs, which is used to predict the next event. The algorithm was implemented as a plugin for the ProM Framework.

Publications