A Novel Token-Based Replay Technique to Speed Up Conformance Checking and Process Enhancement
This work addresses scalability issues in process mining for users dealing with long traces and complex models, though it is incremental as it revives an abandoned technique with enhancements.
The paper tackles the scalability and diagnostic limitations of advanced conformance checking techniques by introducing an improved token-based replay approach, which is shown to be faster and more accurate, outperforming state-of-the-art methods in speed and diagnostics.
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.