Efficient conditional compliance checking of business process models

When checking compliance of business processes against a set of business rules or regulations, the ability to handle and verify conditions in both the model and the rules is essential. Existing design-time verification approaches, however, either completely lack support for the verification of conditions or propose costly verification methods that also consider the full data perspective. This paper proposes a novel light-weight verification method, which is preferable over expensive approaches that include the data perspective when considering structural properties of a business process model. This novel approach generates partial models that capture only relevant execution states to the conditions under investigation. The resulting model can be verified using existing model checking techniques. The computation of such partial models fully abstracts conditions from the full models and specifications, thus avoiding the analysis of the full data perspective. The proposed method is complete with respect to the analyzed execution paths, while significantly reducing the state space complexity by pruning unreachable states given the conditions under investigation. This approach offers the ability to check if a process is compliant with rules and regulations on a much more fine-grained level, and it enables a more precise formulation of the conditions that should and should not hold in the processes. The approach is particularly useful in dynamic environments where processes are constantly changing and efficient conditional compliance checking is a necessity. The approach – implemented in Java and publicly available – is evaluated in terms of performance and practicability, and tested over both synthetic datasets and a real-life case from the Australian telecommunications sector.