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Erschienen in:
2017 | OriginalPaper | Buchkapitel
Finding Process Variants in Event Logs
(Short Paper)
verfasst von : Alfredo Bolt, Wil M. P. van der Aalst, Massimiliano de Leoni
Erschienen in: On the Move to Meaningful Internet Systems. OTM 2017 Conferences
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Abstract
The analysis of event data is particularly challenging when there is a lot of variability. Existing approaches can detect variants in very specific settings (e.g., changes of control-flow over time), or do not use statistical testing to decide whether a variant is relevant or not. In this paper, we introduce an unsupervised and generic technique to detect significant variants in event logs by applying existing, well-proven data mining techniques for recursive partitioning driven by conditional inference over event attributes. The approach has been fully implemented and is freely available as a ProM plugin. Finally, we validated our approach by applying it to a real-life event log obtained from a multinational Spanish telecommunications and broadband company, obtaining valuable insights directly from the event data.