Predictive and Interactive Management of Potential Inconsistencies in Business Rules

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


Predecessor project

HIPBM - Handling Inconsistencies in Business Process Modelling

Last updated 16.08.2023, Matthias Thimm | Terms