Timed Alignments with Mixed Moves

TL;DR

This paper develops linear time algorithms for timed conformance checking with mixed move metrics, enabling efficient alignment of time-aware process models considering timestamp errors and their propagation.

Contribution

It introduces a novel approach to timed alignment that handles mixed move metrics and provides linear time algorithms for distance computation and alignment.

Findings

Linear time algorithms for timed alignment with mixed moves

Effective handling of timestamp errors and propagation in process models

Enhanced conformance checking for time-aware process mining

Abstract

The subject of this paper is to study conformance checking for timed models, that is, process models that consider both the sequence of events in a process as well as the timestamps at which each event is recorded. Time-aware process mining is a growing subfield of research, and as tools that seek to discover timing related properties in processes develop, so does the need for conformance checking techniques that can tackle time constraints and provide insightful quality measures for time-aware process models. In particular, one of the most useful conformance artefacts is the alignment, that is, finding the minimal changes necessary to correct a new observation to conform to a process model. This paper follows a previous one, where we have set our problem of timed alignment. In the present paper, we solve the case where the metrics used to compare timed processes allows mixed moves, i.e. an error on the timestamp of an event may or may not have propagated to its successors, and provide linear time algorithms for distance computation and alignment on models with sequential causal processes.