Process mining based on clustering: A quest for precision
| Autor: | Medeiros, Ana Karla Alves, Guzzo, Antonella, Greco, Gianluigi, Aalst, Wil M. P., Weijters, A. J. M. M., Dongen, Boudewijn F., Domenico Sacca', Terhofstede, A., Benatallah, B., Paik, Hy |
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| Přispěvatelé: | Information Systems IE&IS |
| Jazyk: | angličtina |
| Rok vydání: | 2008 |
| Předmět: | |
| Zdroj: | Business Process Management Workshops (BPM 2007 International Workshops, BPI, BPD, CBP, ProHealth, RefMod, semantics4ws, Brisbane, Australia, September 24, 2007, Revised Selected Papers), 17-29 STARTPAGE=17;ENDPAGE=29;TITLE=Business Process Management Workshops (BPM 2007 International Workshops, BPI, BPD, CBP, ProHealth, RefMod, semantics4ws, Brisbane, Australia, September 24, 2007, Revised Selected Papers) ResearcherID Proceedings of the International Workshop on Business Process Intelligence (BPI 07), 25-27 September 2007, Brisbane, Australia, 7-18 STARTPAGE=7;ENDPAGE=18;TITLE=Proceedings of the International Workshop on Business Process Intelligence (BPI 07), 25-27 September 2007, Brisbane, Australia Business Process Management Workshops ISBN: 9783540782377 Business Process Management Workshops |
| Popis: | Process mining techniques attempt to extract non-trivial and useful information from event logs recorded by information systems. For example, there are many process mining techniques to automatically discover a process model based on some event log. Most of these algorithms perform well on structured processes with little disturbances. However, in reality it is difficult to determine the scope of a process and typically there are all kinds of disturbances. As a result, process mining techniques produce spaghetti-like models that are difficult to read and that attempt to merge unrelated cases. To address these problems, we use an approach where the event log is clustered iteratively such that each of the resulting clusters corresponds to a coherent set of cases that can be adequately represented by a process model. The approach allows for different clustering and process discovery algorithms. In this paper, we provide a particular clustering algorithm that avoids over-generalization and a process discovery algorithm that is much more robust than the algorithms described in literature [1]. The whole approach has been implemented in ProM. |
| Databáze: | OpenAIRE |
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