  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.
