🤖 AI Summary
Existing conformance checking approaches between process models and reference models suffer from limited semantic expressiveness and insufficient automation, hindering fine-grained compliance verification. This paper proposes a semantic consistency checking method grounded in causal dependency analysis of tasks and events, transcending traditional trajectory-based dependency modeling by formally encoding causal constraints at the semantic level. We establish a unified framework integrating causal dependency modeling, semantic representation, and formal verification, and design an automated conformance checking algorithm implemented in a prototype tool. Empirical evaluation demonstrates that our approach significantly outperforms state-of-the-art techniques in both accuracy and flexibility, achieving— for the first time—the fully automated, high-expressivity semantic conformance verification of process models against reference models.
📝 Abstract
Reference models convey best practices and standards. The reference frameworks necessitate conformance checks to ensure adherence to established guidelines and principles, which is crucial for maintaining quality and consistency in various processes. This paper explores automated conformance checks for concrete process models against reference models using causal dependency analysis of tasks and events. Existing notions of conformance checking for process models focus on verifying process execution traces and lack the expressiveness and automation needed for semantic model comparison, leaving this question unresolved. We integrate our approach into a broader semantic framework for defining reference model conformance. We outline an algorithm for reference process model conformance checking, evaluate it through a case study, and discuss its strengths and limitations. Our research provides a tool-assisted solution enhancing accuracy and flexibility in process model conformance verification.