  Process Discovery is concerned with the automatic generation of a process
model that describes a business process from execution data of that business
process. Real life event logs can contain chaotic activities. These activities
are independent of the state of the process and can, therefore, happen at
rather arbitrary points in time. We show that the presence of such chaotic
activities in an event log heavily impacts the quality of the process models
that can be discovered with process discovery techniques. The current modus
operandi for filtering activities from event logs is to simply filter out
infrequent activities. We show that frequency-based filtering of activities
does not solve the problems that are caused by chaotic activities. Moreover, we
propose a novel technique to filter out chaotic activities from event logs. We
evaluate this technique on a collection of seventeen real-life event logs that
originate from both the business process management domain and the smart home
environment domain. As demonstrated, the developed activity filtering methods
enable the discovery of process models that are more behaviorally specific
compared to process models that are discovered using standard frequency-based
filtering.
