Towards Multi-perspective conformance checking with fuzzy sets

TL;DR

This paper introduces a fuzzy set-based approach to conformance checking that incorporates actors' tolerance levels for deviations, improving diagnostic quality and process flexibility.

Contribution

It presents a novel method for conformance checking that accounts for severity and tolerance of deviations using fuzzy sets, enhancing diagnostic accuracy and process resilience.

Findings

Improves diagnostics by considering deviation severity.

Enhances process flexibility through tolerance modeling.

Potentially increases process resilience.

Abstract

Conformance checking techniques are widely adopted to pinpoint possible discrepancies between process models and the execution of the process in reality. However, state of the art approaches adopt a crisp evaluation of deviations, with the result that small violations are considered at the same level of significant ones. This affects the quality of the provided diagnostics, especially when there exists some tolerance with respect to reasonably small violations, and hampers the flexibility of the process. In this work, we propose a novel approach which allows to represent actors' tolerance with respect to violations and to account for severity of deviations when assessing executions compliance. We argue that besides improving the quality of the provided diagnostics, allowing some tolerance in deviations assessment also enhances the flexibility of conformance checking techniques and, indirectly, paves the way for improving the resilience of the overall process management system.