Abstract
In process discovery, the goal is to find, for a given event log, the model describing the underlying process. While process models can be represented in a variety of ways, Petri nets form a theoretically well-explored description language. In this paper, we present an extension of the process discovery algorithm eST-Miner. This approach computes the maximal set of non-redundant places, that are considered to be fitting with respect to a user-definable fraction of the behavior described by the given event log, by evaluating all possible candidate places using token-based replay. The number of candidate places is exponential in the number of activities, and thus evaluating all of them by replay is very time-consuming. To increase efficiency, the eST-miner organizes these candidates in a special search structure, that allows to skip large chunks of the search space, while still returning all the fitting places. While this greatly increases its efficiency compared to the brute force approach evaluating all the candidates, the miner is still very slow compared to other approaches. In this paper, we explore two approaches to increase the fraction of skipped candidates and thus the efficiency of the eST-Miner. The impact of the presented concepts is evaluated by various experiments using both real and artificial event logs.
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Notes
- 1.
Note that \(\sum _{\sigma \in L_{}} f(\sigma )\) and \([ \sigma \in L_{} | f(\sigma ) ]\) operate on multisets, i.e., if the same trace \(\sigma \) appears multiple times in \(L_{}\), this is taken into account.
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We thank the Alexander von Humboldt (AvH) Stiftung for supporting our research.
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Mannel, L.L., Epstein, Y., van der Aalst, W.M.P. (2020). Improving the State-Space Traversal of the eST-Miner by Exploiting Underlying Log Structures. In: Del Río Ortega, A., Leopold, H., Santoro, F.M. (eds) Business Process Management Workshops. BPM 2020. Lecture Notes in Business Information Processing, vol 397. Springer, Cham. https://doi.org/10.1007/978-3-030-66498-5_25
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