  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.
