  Process mining analyzes business processes based on events stored in event
logs. However, some recorded events may correspond to activities on a very low
level of abstraction. When events are recorded on a too low level of
granularity, process discovery methods tend to generate overgeneralizing
process models. Grouping low-level events to higher level activities, i.e.,
event abstraction, can be used to discover better process models. Existing
event abstraction methods are mainly based on common sub-sequences and
clustering techniques. In this paper, we propose to first discover local
process models and then use those models to lift the event log to a higher
level of abstraction. Our conjecture is that process models discovered on the
obtained high-level event log return process models of higher quality: their
fitness and precision scores are more balanced. We show this with preliminary
results on several real-life event logs.
