Discovering simulation models

By Anne Rozinat, Ronny Mans, Minseok Song, Wil M.P. van der Aalst. Information Systems, 2009

305 citations recorded by the source corpus.

Process mining is a tool to extract non-trivial and useful information from process execution logs. These so-called event logs (also called audit trails, or transaction logs) are the starting point for various discovery and analysis techniques that help to gain insight into certain characteristics of the process. In this paper we use a combination of process mining techniques to discover multiple perspectives (namely, the control-flow, data, performance, and resource perspective) of the process from historic data, and we integrate them into a comprehensive simulation model. This simulation model is represented as a colored Petri net (CPN) and can be used to analyze the process, e.g., evaluate the performance of different alternative designs. The discovery of simulation models is explained using a running example. Moreover, the approach has been applied in two case studies; the workflows in two different municipalities in the Netherlands have been analyzed using a combination of process mining and simulation. Furthermore, the quality of the CPN models generated for the running example and the two case studies has been evaluated by comparing the original logs with the logs of the generated models.

Subfields: Computational Theory and Mathematics, Information Systems, Management Information Systems

View Paper: https://www.semanticscholar.org/paper/a06ecee15eaa3cca83789023373884de96dec605

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Citations in the corpus

Business Process Management: A Comprehensive Survey (2013, unknown): “The connection can be used to repair models [94] or to extend them [190, 194, 192, 193].”
Time prediction based on process mining (2011, methodology): “The idea to apply process mining in the context of workflow management systems was introduced in [7].”