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Process Discovery is concerned with the automatic generation of a processmodel that describes a business process from execution data of that businessprocess. Real life event logs can contain chaotic activities. These activitiesare independent of the state of the process and can, therefore, happen atrather arbitrary points in time. We show that the presence of such chaoticactivities in an event log heavily impacts the quality of the process modelsthat can be discovered with process discovery techniques. The current modusoperandi for filtering activities from event logs is to simply filter outinfrequent activities. We show that frequency-based filtering of activitiesdoes not solve the problems that are caused by chaotic activities. Moreover, wepropose a novel technique to filter out chaotic activities from event logs. Weevaluate this technique on a collection of seventeen real-life event logs thatoriginate from both the business process management domain and the smart homeenvironment domain. As demonstrated, the developed activity filtering methodsenable the discovery of process models that are more behaviorally specificcompared to process models that are discovered using standard frequency-basedfiltering.

Discovering More Precise Process Models from Event Logs by Filtering Out Chaotic Activities