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Erschienen in:
2009 | OriginalPaper | Buchkapitel
Discovering Process Models from Unlabelled Event Logs
verfasst von : Diogo R. Ferreira, Daniel Gillblad
Erschienen in: Business Process Management
Verlag: Springer Berlin Heidelberg
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Existing process mining techniques are able to discover process models from event logs where each event is known to have been produced by a given process instance. In this paper we remove this restriction and address the problem of discovering the process model when the event log is provided as an unlabelled stream of events. Using a probabilistic approach, it is possible to estimate the model by means of an iterative Expectaction–Maximization procedure. The same procedure can be used to find the
case id
in unlabelled event logs. A series of experiments show how the proposed technique performs under varying conditions and in the presence of certain workflow patterns. Results are presented for a running example based on a technical support process.