  The strong impulse to digitize processes and operations in companies and
enterprises have resulted in the creation and automatic recording of an
increasingly large amount of process data in information systems. These are
made available in the form of event logs. Process mining techniques enable the
process-centric analysis of data, including automatically discovering process
models and checking if event data conform to a given model. In this paper, we
analyze the previously unexplored setting of uncertain event logs. In such
event logs uncertainty is recorded explicitly, i.e., the time, activity and
case of an event may be unclear or imprecise. In this work, we define a
taxonomy of uncertain event logs and models, and we examine the challenges that
uncertainty poses on process discovery and conformance checking. Finally, we
show how upper and lower bounds for conformance can be obtained by aligning an
uncertain trace onto a regular process model.
