  The insights revealed from process mining heavily rely on the quality of
event logs. Activities extracted from different data sources or the free-text
nature within the same system may lead to inconsistent labels. Such
inconsistency would then lead to redundancy of activity labels, which refer to
labels that have different syntax but share the same behaviours. The
identifications of these labels from data-driven process discovery are
difficult and would rely heavily on human intervention. In this paper, we
propose an approach to detect redundant activity labels using control-flow
relations and data values from event logs. We have evaluated our approach using
two publicly available logs and also a case study using the MIMIC-III data set.
The results demonstrate that our approach can detect redundant activity labels
even with low occurrence frequencies. This approach can value-add to the
preprocessing step to generate more representative event logs for process
mining tasks.
