Capturing Process Behavior with Log-Based Process Metrics

Abstract

Currently, process mining literature is primarily focused on the dis-covery of comprehensible process models that best capture the underlying behav-ior 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, dif-ferent 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 log-based 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 unbi-ased, 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.