  The discipline of process mining deals with analyzing execution data of
operational processes, extracting models from event data, checking the
conformance between event data and normative models, and enhancing all aspects
of processes. Recently, new techniques have been developed to analyze event
data containing uncertainty; these techniques strongly rely on representing
uncertain event data through graph-based models capturing uncertainty. In this
paper we present a novel approach to efficiently compute a graph representation
of the behavior contained in an uncertain process trace. We present our new
algorithm, analyze its time complexity, and report experimental results showing
order-of-magnitude performance improvements for behavior graph construction.
