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Information Systems and Applied Computer Sciences

Cognitive Systems

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  9. BayVFP-Project KIGA

BayVFP-Project KIGA

Project description

The aim of the project is to analyse complex holistic business processes with semantic methods of process mining using a hybrid AI approach of machine learning. Explainable artificial intelligence (AI) methods in the form of interactive dashboards are to be used to improve the comprehensibility and transparency of the process models and to realise their refinement and correction through a human-in-the-loop approach. In detail, domain-specific background knowledge from ERP systems and from subject matter experts is to be used and explicitly represented as a knowledge graph. The underlying data model of the event log is to be enriched with semantic meta-information (semantic event log) in order to explicitly represent causal and hierarchical relationships between events and processes and to use them in process reconstruction. Based on machine learning approaches, existing process discovery methods will be adapted and developed as specialised declarative learning methods on the basis of the semantic event log. Finally, the results of the methods will be validated on the basis of business processes of partner companies with real data.

Exemplary process model
Exemplary process model as Workflow Net: Represents all possible process flows in the traffic ticket processing of the Italian police [RTFMP-2015].

Research Focus:

  • Investigation of semantic methods for knowledge representation and inference methods for process discovery approaches.
  • Semantic representation of causality relations and process properties in knowledge graphs, especially semantic event log.
  • Development of machine learning methods for process discovery (white box approach) with inductive logic programming (ILP) methods.
  • Explained interactive machine learning methods for intelligent dashboards with a human-in-the-loop approach.
  • Conducting studies and experiments as part of a usability study to evaluate the research results.

Publications and Talks

Publications

  • Christian Dormagen, Jonas Amling, Stephan Scheele, and Ute Schmid. Explaining Process Behavior: A Declarative Framework for Interpretable Event Data. Accepted for publication in: Proceedings of the 3rd World Conference on eXplainable Artificial Intelligence (XAI 2025), Late-Breaking Work Track.
  • Jonas Amling, Emanuel Slany, Christian Dormagen, Marco Kretschmann, and Stephan Scheele. Bridging the Interpretability Gap in Process Mining: A Comprehensive Approach Combining Explainable Clustering and Generative AI. Accepted for publication in: Proceedings of the 3rd World Conference on eXplainable Artificial Intelligence (XAI 2025), Main Track.
  • Christopher Lorenz Werner, Jonas Amling, Christian Dormagen, and Stephan Scheele. ClustXRAI: Interactive Cluster Exploration and Explanation for Process Mining with Generative AI. Accepted for publication in: Proceedings of the 3rd World Conference on eXplainable Artificial Intelligence (XAI 2025), Demonstrator Track.
  • Christian Dormagen. Towards Semantic-driven, Declarative and Interactive Process Mining. In: Proceedings of the ICPM 2023 Doctoral Consortium and Demo Track. CEUR Workshop Proceedings, vol. 3648, CEUR-WS.org, 2023. Available at: https://ceur-ws.org/Vol-3648/paper_6521.pdf.
  • Zineddine Bettouche. Transforming Process Mining: A Transformer-Based Approach to Semantic Clustering in Event Log Analysis. In: Proceedings of the ICPM 2023 Doctoral Consortium and Demo Track. CEUR Workshop Proceedings, vol. 3648, CEUR-WS.org, 2023. Available at: https://ceur-ws.org/Vol-3648/paper_7661.pdf.

Student Projects:

We are always looking for students who would like to contribute to the KIGA project in the form of a thesis, a project or as a student assistant. If you are interested, please feel free to contact us.