  Nowadays, more and more process data are automatically recorded by
information systems, and made available in the form of event logs. Process
mining techniques enable process-centric analysis of data, including
automatically discovering process models and checking if event data conform to
a certain model. In this paper we analyze the previously unexplored setting of
uncertain event logs: logs where quantified uncertainty is recorded together
with the corresponding data. 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 aligning an uncertain trace onto a regular
process model.
