Time-Aware Conformance Checking
The subject of this internship project 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 to 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. From alignments, one can derive measures of fitness, precision, and generalisation of a process model, which are crucial to analysing its performance. We develop three metrics by which to compare timed processes, and seek to solve the alignment problem, that is, the problem of calculating the closest trace in the language of a timed model to a particular observed event trace, where each such metric devised provides a new notion of closeness. Here we have the paper(s) we are working on currently, and some implementations of the algorithms we have developed.
Thomas Chatain, Neha Rino
On-going