Model Independent Error Bound Estimation for Conformance Checking Approximation

TL;DR

This paper introduces a method to estimate error bounds for conformance checking approximations that are independent of the process model, enabling more efficient and accurate process conformance analysis.

Contribution

It presents a novel approach to derive a priori error bounds for conformance approximation without relying on the specific process model.

Findings

Error bounds improve approximation accuracy

Subset selection guided by error bounds enhances efficiency

Method is effective for complex, large-scale event data

Abstract

Conformance checking techniques allow us to quantify the correspondence of a process's execution, captured in event data, w.r.t., a reference process model. In this context, alignments have proven to be useful for calculating conformance statistics. However, for extensive event data and complex process models, the computation time of alignments is considerably high, hampering their practical use. Simultaneously, it suffices to approximate either alignments or their corresponding conformance value(s) for many applications. Recent work has shown that using subsets of the process model behavior leads to accurate conformance approximations. The accuracy of such an approximation heavily depends on the selected subset of model behavior. Thus, in this paper, we show that we can derive a priori error bounds for conformance checking approximation based on arbitrary activity sequences, independently of the given process model. Such error bounds subsequently let us select the most relevant subset of process model behavior for the alignment approximation. Experiments confirm that conformance approximation accuracy improves when using the proposed error bound approximation to guide the selection of relevant subsets of process model behavior.