Online event. November 3-12, 2021.
Copyright © 2021 International Joint Conferences on Artificial Intelligence Organization
Trace Alignment is a prominent problem in Declarative Process Mining, which consists in identifying a minimal set of modifications that a log trace (produced by a system under execution) requires in order to be made compliant with a temporal specification. In its simplest form, log traces are sequences of events from a finite alphabet and specifications are written in DECLARE, a strict sublanguage of linear-time temporal logic over finite traces (LTLf ). The best approach for trace alignment has been developed in AI, using cost-optimal planning, and handles the whole LTLf . In this paper, we study the timed version of trace alignment, where events are paired with timestamps and specifications are provided in metric temporal logic over finite traces (MTLf ), essentially a superlanguage of LTLf . Due to the infiniteness of timestamps, this variant is substantially more challenging than the basic version, as the structures involved in the search are (uncountably) infinite-state, and calls for a more sophisticated machinery based on alternating (timed) automata, as opposed to the standard finite-state automata sufficient for the untimed version. The main contribution of the paper is a provably correct, effective technique for Timed Trace Alignment that takes advantage of results on MTLf decidability as well as on reachability for well-structured transition systems.