  Executing operational processes generates event data, which contain
information on the executed process activities. Process mining techniques allow
to systematically analyze event data to gain insights that are then used to
optimize processes. Visual analytics for event data are essential for the
application of process mining. Visualizing unique process executions -- also
called trace variants, i.e., unique sequences of executed process activities --
is a common technique implemented in many scientific and industrial process
mining applications. Most existing visualizations assume a total order on the
executed process activities, i.e., these techniques assume that process
activities are atomic and were executed at a specific point in time. In
reality, however, the executions of activities are not atomic. Multiple
timestamps are recorded for an executed process activity, e.g., a
start-timestamp and a complete-timestamp. Therefore, the execution of process
activities may overlap and, thus, cannot be represented as a total order if
more than one timestamp is to be considered. In this paper, we present a
visualization approach for trace variants that incorporates start- and
complete-timestamps of activities.
