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Published in:
2015 | OriginalPaper | Chapter
Process Discovery
Authors : Eric Badouel, Luca Bernardinello, Philippe Darondeau
Published in: Petri Net Synthesis
Publisher: Springer Berlin Heidelberg
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One of the purposes of process mining [1] is model discovery, i.e. the ability to construct or reconstruct from an event log a business process model that can generate this event log. The game is to dig out of event logs sufficient information on the structure of their generating model. As a technique for model discovery or model identification, process mining has some connections with machine learning. For instance, after collecting over a long period of time information on the health history of many patients, including diagnosis and treatment steps, one may want to extract from this record an accurate model of the workflow system of a hospital. Another type of application is to try to reconstruct from representative use cases an existing but partially unknown system.