Model-Independent Error Bound Estimation for Conformance Checking Approximation

Conformance checking techniques quantify correspondence between a process's execution and a reference process model using event data. Alignments, used for conformance statistics, are computationally expensive for complex models and large datasets. Recent studies show accurate approximations can be achieved by selecting subsets of model behavior. This paper presents a novel approach deriving error bounds for conformance checking approximation based on arbitrary activity sequences. The proposed approach allows for the selection of relevant subsets for improved accuracy. Experimental evaluations validate its effectiveness, demonstrating enhanced accuracy compared to traditional alignment methods.