  Process-Mining techniques aim to use event data about past executions to gain
insight into how processes are executed. While these techniques are proven to
be very valuable, they are less successful to reach their goal if the process
is flexible and, hence, events can potentially occur in any order. Furthermore,
information systems can record events at very low level, which do not match the
high-level concepts known at business level. Without abstracting sequences of
events to high-level concepts, the results of applying process mining (e.g.,
discovered models) easily become very complex and difficult to interpret, which
ultimately means that they are of little use. A large body of research exists
on event abstraction but typically a large amount of domain knowledge is
required to be fed in, which is often not readily available. Other abstraction
techniques are unsupervised, which give lower accuracy. This paper puts forward
a technique that requires limited domain knowledge that can be easily provided.
Traces are divided in sessions, and each session is abstracted as one single
high-level activity execution. The abstraction is based on a combination of
automatic clustering and visualization methods. The technique was assessed on
two case studies that evidently exhibits a large amount of behavior. The
results clearly illustrate the benefits of the abstraction to convey knowledge
to stakeholders.
