Abstract
Process discovery aims to discover models to explain the behaviors of information systems. The Inductive Miner (IM) discovery algorithm is able to discover process models with desirable properties: free-choiceness and soundness. Moreover, a family of variations makes IM practical for real-life applications. Due to the advantages, IM is regarded as the state of the art and has been implemented in commercial process mining software. However, IM can only discover block-structured process models that tend to have high fitness but low precision. To improve the quality of process models discovered by IM while preserving desirable properties, we propose an approach that applies property-preserving (free-choiceness and soundness) reduction/synthesis rules to iteratively modify the process model. The experimental results show that the models discovered by our approach have a more flexible representation while preserving desirable properties. Moreover, the model quality, as measured by the F1-score, is improved compared to the original models.
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Notes
- 1.
We use the following formula for the F1-score: \(2\cdot \frac{precision \cdot fitness}{precision + fitness}\).
- 2.
Note that none of the directly-follows relations are filtered out using the default noise threshold for our running example \(L_s\) as most of the relations are frequent.
- 3.
There can be multiple activities with the same lowest similarity score. In such a case, we randomly choose one from the set.
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- 8.
The cells in the matrix represent the relations between the corresponding two activities. For two activities \(x,y\in \mathcal {B}(\mathcal {U}_{A}^{*})\), \(x>y\) means that x is directly followed by y but not the other way round. \(x\#y\) represents that the two activities never follow each other while x||y means x and y both directly follows each other. For more details and a formal definition of the footprint matrix, we refer to [23].
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We thank the Alexander von Humboldt (AvH) Stiftung for supporting our research.
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Huang, TH., van der Aalst, W.M.P. (2023). Unblocking Inductive Miner. In: van der Aa, H., Bork, D., Proper, H.A., Schmidt, R. (eds) Enterprise, Business-Process and Information Systems Modeling. BPMDS EMMSAD 2023 2023. Lecture Notes in Business Information Processing, vol 479. Springer, Cham. https://doi.org/10.1007/978-3-031-34241-7_23
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