Abstract
Process discovery is one of the most challenging tasks in process mining. Based on event data, a process discovery approach generates a process model that captures the behavior recorded in the data. The hybrid miner is a two-step process discovery approach that creates a balance between the advantages of formal modeling and the necessity of remaining informal for vague structures. In the first discovery step, an informal causal graph is constructed based on direct succession dependencies between activities. In the second discovery step, the hybrid miner tries to convert the discovered dependencies into formal constraints. For vague structures where formal constraints cannot be justified, dependencies are depicted informally. In this paper, we reduce the representational bias of the hybrid miner by exploiting causal graph metrics to mine for long-term dependencies. Our evaluation shows that the proposed approach leads to the discovery of more precise models.
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Notes
- 1.
In all examples in this paper, we assume that all traces in any event log start with the activity “start" and end with the activity “end" without explicitly mentioning them.
- 2.
For all examples and experiments in this paper, we use \({t_{ R_{W} } = 1}\) to deactivate the detection of uncertain edges because these edges are out of the scope of this paper.
- 3.
A new plugin “Extended Causal Graph Miner" has been implemented in ProM [13] to support the approach introduced in this paper.
- 4.
In order to apply conformance checking techniques, hybrid Petri nets are transformed into standard Petri nets by simply removing all informal arcs.
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Kourani, H., Di Francescomarino, C., Ghidini, C., van der Aalst, W., van Zelst, S. (2023). Mining for Long-Term Dependencies in Causal Graphs. In: Cabanillas, C., Garmann-Johnsen, N.F., Koschmider, A. (eds) Business Process Management Workshops. BPM 2022. Lecture Notes in Business Information Processing, vol 460. Springer, Cham. https://doi.org/10.1007/978-3-031-25383-6_10
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