Capturing Process Behavior with Log-Based Process Metrics

Currently, process mining literature is primarily focused on the discovery of comprehensible process models that best capture the underlying behavior in event logs. Consequently, the resulting models a) aggregate information, based on algorithm-specific assumptions, and b) transform information into a simplified representation. Both characteristics, which are valuable in certain, different contexts, suffer from the inability to describe objectively the behavior that is inherent to the event log at hand. In this paper, we present the need for logbased process metrics to capture the process behavior in an event log, without the need to first discover a model. The metrics provide a process owner with unbiased, algorithm-agnostic information of the event log, as a starting point of the process analysis. The constructed metrics also serve as a mean to objectively compare different event logs in terms of time-related and variance aspects.