  Modern information systems are able to collect event data in the form of
event logs. Process mining techniques allow to discover a model from event
data, to check the conformance of an event log against a reference model, and
to perform further process-centric analyses. In this paper, we consider
uncertain event logs, where data is recorded together with explicit uncertainty
information. We describe a technique to discover a directly-follows graph from
such event data which retains information about the uncertainty in the process.
We then present experimental results of performing inductive mining over the
directly-follows graph to obtain models representing the certain and uncertain
part of the process.
