Project description

Business rules (BR) are commonly used to model company regulations or rules that should be adhered to by executed processes. The actual set of BRs can be manually crafted, mined from process observations, or derived from natural language specifications. As these are error-prone mechanisms, the resulting set of BRs may however contain inconsistencies and other issues, which may lead to unexpected behaviour and costly debugging when processes are validated against these rules during process execution. A challenging type of issues are potential inconsistencies, i.e., contradictory conclusions of certain rules that will however only occur when certain fact combinations are observed during execution. For example, if we consider the two rules {a-> b; c-> !b}, these rules could be used for meaningful reasoning individually; however, they become inconsistent if the fact input {a,c} is observed simultaneously.

In MIB we address the predictive and interactive management of such types of potential inconsistencies in BRs. For that, we will make use of approaches of inconsistency measurement (IM) and formal argumentation, both being research topics within the area of knowledge representation (KR) and reasoning. In particular, we will first set up a formal framework for investigating the notion of a potential inconsistency, investigate it in state-of-the-art rule formalisms such as DMN, Declare, and FCL, and analyze properties such as computational complexity. Further, we will develop approaches to analyze, visualize, and, in particular, measure potential inconsistencies to provide the modeler with an assessment on the severity of potential issues. Moreover, using methods from formal argumentation, we will develop a dialog system that allows the modeler to interactively address and solve the issues in the rule base. These methods will be embedded in the authoring phase of BR bases to support modelers early in the development cycle of BRs. This also includes the integration of our methods into existing rule mining algorithms and the development of novel inconsistency-aware ones.

Project leader

• Matthias Thimm, University of Hagen
• Patrick Delfmann, Universität Koblenz

People

• Yehia Hatab, University of Hagen
• Isabelle Kuhlmann, University of Hagen
• Carl Corea, Universität Koblenz
• Sabine Nagel, Universität Koblenz

Predecessor project

Publications

• Yehia Hatab, Kai Sauerwald, Matthias Thimm. A Hitting Set Approach to Inconsistent-Tolerant Reasoning. In Nina Gierasimczuk, Jesse Heyninck (Eds.), Proceedings of the 22nd International Workshop on Nonmonotonic Reasoning (NMR'24). November 2024. bibtex pdf
• Sabine Nagel, Anna Wolters, Dennis M. Riehle, Patrick Delfmann. EduClare - An Intelligent Tutoring Chatbot for Teaching Declarative Process Modeling. In Proceedings of the ICPM Doctoral Consortium and Demo Track 2024, co-located with 6th International Conference on Process Mining (ICPM 2024). October 2024. bibtex
• Sabine Nagel, Patrick Delfmann. Interactive Resolution of Inconsistencies in Declarative Process Models. In Proceedings der 19. Internationalen Tagung der Wirtschaftsinformatik (WI 2024). September 2024. bibtex
• Sabine Nagel, Patrick Delfmann. Towards an E-Learning Approach for Declarative Process Modeling. In Proceedings of the BPM 2024 Blockchain/RPA/CEE/Educators/Industry Forum. September 2024. bibtex
• Carl Corea, Matthias Thimm. Towards Handling Potential Issues in Business Rule Bases. In Journal of Applied Logics, 11(4):565-592. August 2024. bibtex pdf
• Carl Corea, Isabelle Kuhlmann, Matthias Thimm, John Grant. Paraconsistent reasoning for inconsistency measurement in declarative process specifications. In Information Systems, 122:102347. February 2024. bibtex pdf
• Sabine Nagel, Eric Amann, Patrick Delfmann. Exploring Cognitive Effects of Inconsistency Characteristics on Understanding Inconsistencies in Declarative Process Models. In Proceedings of the 57th Hawaii International Conference on System Sciences (HICSS), pages 6012-6021. January 2024. bibtex
• Sabine Nagel, Eric Amann, Patrick Delfmann. Declare-js: A Web-Based Viewer and Editor for Declarative Process Models. In Proceedings of the ICPM Doctoral Consortium and Demo Track 2023, co-located with 5th International Conference on Process Mining (ICPM 2023). October 2023. bibtex
• Sabine Nagel, Patrick Delfmann. Identification, Abstraction and Classification of Inconsistency Structures in Declarative Process Models. In ECIS 2023 Research Papers. June 2023. bibtex