Handling Inconsistencies in Business Process Modelling

Project description

Business rules and business process models are widely acknowledged as a useful means for the organization of businesses. Both these artifacts are usually created and maintained by human modelers. Here, a potential problem is that of inconsistency, which can result from an incremental and collaborative modelling process. For example, Batoulis and Weske (2017) report on a recent case-study with a large insurance company, where they found that 27% of business rules were erroneous. In particular, in the case of digitization, with processes being accessed across socio-technical systems, multi-cultural or multi-jurisdictional boundaries, business process management (BPM) and business rules management (BRM) becomes even more challenging. As business rules are the de facto object of study for compliance management, where rules are utilized to govern compliant processes, such inconsistencies are also a problem for compliance management, as inconsistent rule bases cannot be used to verify compliance correctly, which thus impedes company efforts in compliance management. Here, methods are needed to detect inconsistencies and present companies with a careful analysis to support inconsistency resolution. Also, different users may model aspects of a scenario differently and joining their partial models may yield unsatisfiable processes, meaning an adaptation of the model is called for. In these scenarios, methods are needed that analyse the contradiction and present the users with a analysis so they may be able to re-design the model in an appropriate manner or trigger (semi)-automatic methods to repair the model. This calls for both, a qualitative analysis of the reasons of contradictions (such as pin-pointing the exact cause of problems) and a quantitative analysis of the severity of problems. We therefore aim to investigate the detection, analysis, and resolution of inconsistencies in business rules and processes. In the envisioned project, we aim to address the challenge of dealing with erroneous process models and business rules by employing methods from inconsistency measurement, a research topic within the area of knowledge representation and reasoning. Our aim is to develop measures that can assess the severity of inconsistencies occurring within business rules or between process models and business rules, and present respective results in a way comprehensible for stakeholders in BPM and BRM. These quantitative insights can be used for inconsistency detection and ranking and support subsequent inconsistency resolution in the context of business process improvement. Also, we will investigate means for (semi-) automated inconsistency resolution, as well as guiding modellers in resolving errors in an incremental-manner.


Project leader

People

Project duration

February 2020 - November 2022

Follow-up project

MIB - Predictive and Interactive Management of Potential Inconsistencies in Business Rules

Publications

  • Carl Corea, Isabelle Kuhlmann, Matthias Thimm, John Grant. Measuring and Resolving Inconsistency in Declarative Process Specifications (Extended Abstract). In AAAI 2023 Bridge Program on Artificial Intelligence and Business Process Management. February 2023. bibtex pdf
  • Carl Corea, John Grant, Matthias Thimm. Measuring Inconsistency in Declarative Process Specifications. In Proceedings of the 20th International Conference on Business Process Management (BPM'22). September 2022. bibtex pdf
  • Isabelle Kuhlmann, Matthias Thimm. Algorithms for Inconsistency Measurement using Answer Set Programming. In Proceedings of the 19th International Workshop on Non-Monotonic Reasoning (NMR'21). November 2021. bibtex pdf
  • Carl Corea, Matthias Thimm, Patrick Delfmann. Measuring Inconsistency over Sequences of Business Rule Cases. In Proceedings of the 18th International Conference on Principles of Knowledge Representation and Reasoning (KR'21). November 2021. bibtex pdf
  • Jandson Santos Ribeiro Santos, Matthias Thimm. Consolidation via Tacit Culpability Measures: Between Explicit and Implicit Degrees of Culpability. In Proceedings of the 18th International Conference on Principles of Knowledge Representation and Reasoning (KR'21). November 2021. bibtex pdf
  • Elina Unruh, Patrick Delfmann, Matthias Thimm. Quantitative Deadlock Analysis in Petri Nets using Inconsistency Measures. In Proceedings of the 23rd IEEE International Conference on Business Informatics (IEEE CBI 2021). September 2021. bibtex pdf
  • Carl Corea, Sabine Nagel, Jan Mendling and Patrick Delfmann. Interactive and Minimal Repair of Declarative Process Models. In Proceedings of the BPM Forum 2021 co-located with the 19th International Conference on Business Process Management (BPM 2021). Rome, 2021. pdf
  • Jandson Santos Ribeiro Santos, Viorica Sofronie-Stokkermans, Matthias Thimm. Measuring Disagreement with Interpolants. In Proceedings of the 14th International Conference on Scalable Uncertainty Management (SUM'20). September 2020. bibtex pdf
  • Markus Ulbricht, Matthias Thimm, Gerhard Brewka. Handling and measuring inconsistency in non-monotonic logics. In Artificial Intelligence, 286:103344. June 2020. bibtex pdf
  • Carl Corea, Matthias Thimm. On Quasi-Inconsistency and its Complexity. In Artificial Intelligence. May 2020. bibtex pdf
  • Carl Corea, Matthias Thimm. Towards Inconsistency Measurement in Business Rule Bases. In Proceedings of the 24th European Conference on Artificial Intelligence (ECAI'20). June 2020. bibtex pdf
  • Isabelle Kuhlmann, Matthias Thimm. An Algorithm for the Contension Inconsistency Measure using Reductions to Answer Set Programming. In Jesse Davis, Karim Tabia (Eds.), Proceedings of the 14th International Conference on Scalable Uncertainty Management (SUM'20), pages 289-296, Springer International Publishing, volume 12322 of Lecture Notes in Artificial Intelligence. September 2020. Winner of best student paper award bibtex pdf
  • Matthias Thimm. Inconsistency Measurement. In Proceedings of the 13th International Conference on Scalable Uncertainty Management (SUM'19). December 2019. bibtex pdf
  • Matthias Thimm. An Experimental Study on the Behaviour of Inconsistency Measures. In Proceedings of the 13th International Conference on Scalable Uncertainty Management (SUM'19). December 2019. bibtex pdf
  • Carl Corea, Sabine Nagel, Patrick Delfmann. Effects of Visualization Techniques on Understanding Inconsistencies in Automated Decision-Making. In Proceedings of the 53rd Hawaii International Conference on System Sciences. Hawaii, 2020.
  • Carl Corea, Jonas Blatt, Patrick Delfmann. A Tool for Decision-Logic Level Verification in DMN Decision Tables. In Proceedings of the Demonstration Track at BPM 2019 co-located with 17th International Conference on Business Process Management (BPM 2019). Wien, 2019.
  • Carl Corea, Patrick Delfmann. Quasi-Inconsistency in Declarative Process Models. In Proceedings des Business Process Management Forum (BPM2019). Wien, 2019.
  • Carl Corea, Matthias Deisen, Patrick Delfmann. Resolving Inconsistencies in Declarative Process Models based on Culpability Measurement. In Proceedings der 14. Internationalen Tagung der Wirtschaftsinformatik (WI2019). Siegburg, 2019. Winner of best paper award
  • Sabine Nagel, Carl Corea, Patrick Delfmann. Effects of Quantitative Measures on Understanding Inconsistencies in Business Rules. In Proceedings of the 52nd Hawaii International Conference on System Sciences (HICSS52). Hawaii, 2019
  • Carl Corea, Patrick Delfmann. Supporting Business Rule Management with Inconsistency Analysis. In Proceedings of the Industrial Track at BPM 2018 co-located with 16th International Conference on Business Process Management (BPM 2018). Sydney, 2018
  • Carl Corea, Patrick Delfmann. A Tool to Monitor Consistent Decision-Making in Business Process Execution. In Proceedings of the Demonstration Track at BPM 2018 co-located with 16th International Conference on Business Process Management (BPM2018). Sydney, 2018



Last updated 02.07.2019, Matthias Thimm | Terms