  Event data, often stored in the form of event logs, serve as the starting
point for process mining and other evidence-based process improvements.
However, event data in logs are often tainted by noise, errors, and missing
data. Recently, a novel body of research has emerged, with the aim to address
and analyze a class of anomalies known as uncertainty-imprecisions quantified
with meta-information in the event log. This paper illustrates an extension of
the XES data standard capable of representing uncertain event data. Such an
extension enables input, output, and manipulation of uncertain data, as well as
analysis through the process discovery and conformance checking approaches
available in literature.
