Abstract
Executing operational processes generates valuable event data in organizations’ information systems. Process discovery describes the learning of process models from such event data. Incremental process discovery algorithms allow learning a process model from event data gradually. In this context, process behavior recorded in event data is incrementally fed into the discovery algorithm that integrates the added behavior to a process model under construction. In this paper, we investigate the open research question of the impact of the ordering of incrementally selected process behavior on the quality, i.e., recall and precision, of the learned process models. We propose a framework for defining ordering strategies for traces, i.e., observed process behavior, for incremental process discovery. Further, we provide concrete instantiations of this framework. We evaluate different trace-ordering strategies on real-life event data. The results show that trace-ordering strategies can significantly improve the quality of the learned process models.
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Note that per trace that is incrementally added, various LCAs might be changed. However, without fully executing the incremental process discovery approach for a trace, we only can compute the first LCA that must be changed. Therefore, there is a risk that the first LCA will be rated as good based on the strategy, but that further LCAs will have to be changed, which the strategy would rate as bad.
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Schuster, D., Domnitsch, E., van Zelst, S.J., van der Aalst, W.M.P. (2022). A Generic Trace Ordering Framework for Incremental Process Discovery. In: Bouadi, T., Fromont, E., Hüllermeier, E. (eds) Advances in Intelligent Data Analysis XX. IDA 2022. Lecture Notes in Computer Science, vol 13205. Springer, Cham. https://doi.org/10.1007/978-3-031-01333-1_21
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