  Process mining methods often analyze processes in terms of the individual
end-to-end process runs. Process behavior, however, may materialize as a
general state of many involved process components, which can not be captured by
looking at the individual process instances. A more holistic state of the
process can be determined by looking at the events that occur close in time and
share common process capacities. In this work, we conceptualize such behavior
using high-level events and propose a new framework for detecting and logging
such high-level events. The output of our method is a new high-level event log,
which collects all generated high-level events together with the newly assigned
event attributes: activity, case, and timestamp. Existing process mining
techniques can then be applied on the produced high-level event log to obtain
further insights. Experiments on both simulated and real-life event data show
that our method is able to automatically discover how system-level patterns
such as high traffic and workload emerge, propagate and dissolve throughout the
process.
