A Novel Token-Based Replay Technique to Speed Up Conformance Checking and Process Enhancement

TL;DR

This paper introduces an improved token-based replay method for conformance checking that is faster, more scalable, and provides better diagnostics, outperforming existing advanced techniques especially on complex traces.

Contribution

The paper presents a novel, scalable token-based replay approach that enhances speed and diagnostic accuracy, integrating it into the PM4Py library and improving existing conformance measures.

Findings

Outperforms state-of-the-art techniques in speed.

Provides more accurate diagnostics and avoids token flooding.

Scalable to longer traces and complex models.

Abstract

Token-based replay used to be the standard way to conduct conformance checking. With the uptake of more advanced techniques (e.g., alignment based), token-based replay got abandoned. However, despite decomposition approaches and heuristics to speed-up computation, the more advanced conformance checking techniques have limited scalability, especially when traces get longer and process models more complex. This paper presents an improved token-based replay approach that is much faster and scalable. Moreover, the approach provides more accurate diagnostics that avoid known problems (e.g., "token flooding") and help to pinpoint compliance problems. The novel token-based replay technique has been implemented in the PM4Py process mining library. We will show that the replay technique outperforms state-of-the-art techniques in terms of speed and/or diagnostics. %Moreover, a revision of an existing precision measure (ETConformance) will be proposed through integration with the token-based replayer.