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Song, Minseok
Business Process Intelligence(BPI) Lab
Research Interests
  • Process mining

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Discovering colored Petri nets from event logs

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Title
Discovering colored Petri nets from event logs
Author
Rozinat, A.Mans, R.S.Song, Minseokvan der Aalst, W.M.P.
Keywords
Algorithms; Computer simulation; Mathematical models; Petri nets
Issue Date
200801
Publisher
Springer Verlag
Citation
INTERNATIONAL JOURNAL ON SOFTWARE TOOLS FOR TECHNOLOGY TRANSFER, v.10, no.1, pp.57 - 74
Abstract
Process-aware information systems typically log events (e.g., in transaction logs or audit trails) related to the actual execution of business processes. Analysis of these execution logs may reveal important knowledge that can help organizations to improve the quality of their services. Starting from a process model, which can be discovered by conventional process mining algorithms, we analyze how data attributes influence the choices made in the process based on past process executions using decision mining, also referred to as decision point analysis. In this paper we describe how the resulting model (including the discovered data dependencies) can be represented as a Colored Petri Net (CPN), and how further perspectives, such as the performance and organizational perspective, can be incorporated. We also present a CPN Tools Export plug-in implemented within the ProM framework. Using this plug-in, simulation models in ProM obtained via a combination of various process mining techniques can be exported to CPN Tools. We believe that the combination of automatic discovery of process models using ProM and the simulation capabilities of CPN Tools offers an innovative way to improve business processes. The discovered process model describes reality better than most hand-crafted simulation models. Moreover, the simulation models are constructed in such a way that it is easy to explore various redesigns.
URI
http://scholarworks.unist.ac.kr/handle/201301/4582
DOI
http://dx.doi.org/10.1007/s10009-007-0051-0
ISSN
1433-2779
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