Discovering and Navigating a Collection of Process Models using Multiple Quality Dimensions

Provided by: Eindhoven University of Technology
Topic: Big Data
Format: PDF
Process discovery algorithms typically aim at discovering a process model from an event log that best describes the recorded behavior. However, multiple quality dimensions can be used to evaluate a process model. In this paper, the authors showed that there often is not one single process model that describes the observed behavior best in all quality dimensions. Therefore, they present an extension to their flexible ETM algorithm that does not result in a single best process model but in a collection of mutually non-dominating process models.

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