Timed Alignments

The subject of this presentation 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 verification 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. In this presentation, we set the problem of timed alignment and solve three cases each corresponding to a different metric over time processes. For the first, we have an algorithm whose time complexity is linear both in the size of the observed trace and the process model, and for the second, we provide an encoding in simplex. For the second and third, we further restrict our attention to process models with linear causal processes which allows us to provide a quadratic time algorithm, and a linear time algorithm, respectively. Contributed talk given by Neha A Rino

Authors

• Neha A Rino

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