Discovering Redundant Activities in Event Logs for the Simplification of Process Models
This work addresses the issue of complex, real-life event logs for organizations using process mining, offering a preprocessing step to enhance model quality, though it is incremental as it builds on existing filtering techniques.
The paper tackles the problem of redundant activities in event logs, which degrade the quality of discovered process models, and proposes a method to identify and remove these redundancies, resulting in simplified models that improve process mining outcomes.
Process mining acts as a valuable tool to analyse the behaviour of an organisation by offering techniques to discover, monitor and enhance real processes. The key to process mining is to discovery understandable process models. However, real-life logs can be complex with redundant activities, which share similar behaviour but have different syntax. We show that the existence of such redundant activities heavily affects the quality of discovered process models. Existing approaches filter activities by frequency, which cannot solve problems caused by redundant activities. In this paper, we propose first to discover redundant activities in the log level and, then, use the discovery results to simplify event logs. Two publicly available data sets are used to evaluate the usability of our approach in real-life processes. Our approach can be adopted as a preprocessing step before applying any discovery algorithms to produce simplify models.