Process model discovery : a method based on transition system decomposition
| Autor: | Kalenkova, A.A., Lomazova, I.A., Aalst, van der, W.M.P., Ciardo, G., Kindler, E. |
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| Přispěvatelé: | Process Science |
| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: | |
| Zdroj: | Application and Theory of Petri Nets and Concurrency ISBN: 9783319077338 Petri Nets Application and Theory of Petri Nets and Concurrency (35th International Conference, Petri Nets 2014, Tunis, Tunesia, June 23-27, 2014. Proceedings), 71-90 STARTPAGE=71;ENDPAGE=90;TITLE=Application and Theory of Petri Nets and Concurrency (35th International Conference, Petri Nets 2014, Tunis, Tunesia, June 23-27, 2014. Proceedings) |
| ISSN: | 0302-9743 |
| DOI: | 10.1007/978-3-319-07734-5_5 |
| Popis: | Process mining aims to discover and analyze processes by extracting information from event logs. Process mining discovery algorithms deal with large data sets to learn automatically process models. As more event data become available there is the desire to learn larger and more complex process models. To tackle problems related to the readability of the resulting model and to ensure tractability, various decomposition methods have been proposed. This paper presents a novel decomposition approach for discovering more readable models from event logs on the basis of a priori knowledge about the event log structure: regular and special cases of the process execution are treated separately. The transition system, corresponding to a given event log, is decomposed into a regular part and a specific part. Then one of the known discovery algorithms is applied to both parts, and finally these models are combined into a single process model. It is proven, that the structural and behavioral properties of submodels are inherited by the unified process model. The proposed discovery algorithm is illustrated using a running example. |
| Databáze: | OpenAIRE |
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