A Scalable and Near-Optimal Conformance Checking Approach for Long Traces

TL;DR

This paper presents a scalable sliding window approach for conformance checking in large event logs, improving efficiency and accuracy by breaking down long traces and leveraging structural information.

Contribution

Introduces a novel sliding window method that reduces search space and maintains interpretability for conformance checking of long traces.

Findings

Consistently finds optimal alignments in most cases

Demonstrates significant scalability improvements

Provides theoretical complexity analysis supporting efficiency

Abstract

Long traces and large event logs that originate from sensors and prediction models are becoming more common in our data-rich world. In such circumstances, conformance checking, a key task in process mining, can become computationally infeasible due to the exponential complexity of finding an optimal alignment. This paper introduces a novel sliding window approach to address these scalability challenges while preserving the interpretability of alignment-based methods. By breaking down traces into manageable subtraces and iteratively aligning each with the process model, our method significantly reduces the search space. The approach uses global information that captures structural properties of the trace and the process model to make informed alignment decisions, discarding unpromising alignments even if they are optimal for a local subtrace. This improves the overall accuracy of the results. Experimental evaluations demonstrate that the proposed method consistently finds optimal alignments in most cases and highlight its scalability. This is further supported by a theoretical complexity analysis, which shows the reduced growth of the search space compared to other common conformance checking methods. This work provides a valuable contribution towards efficient conformance checking for large-scale process mining applications.