  Conformance checking techniques allow us to evaluate how well some exhibited
behaviour, represented by a trace of monitored events, conforms to a specified
process model. Modern monitoring and activity recognition technologies, such as
those relying on sensors, the IoT, statistics and AI, can produce a wealth of
relevant event data. However, this data is typically characterised by noise and
uncertainty, in contrast to the assumption of a deterministic event log
required by conformance checking algorithms. In this paper, we extend
alignment-based conformance checking to function under a probabilistic event
log. We introduce a weighted trace model and weighted alignment cost function,
and a custom threshold parameter that controls the level of confidence on the
event data vs. the process model. The resulting algorithm considers activities
of lower but sufficiently high probability that better align with the process
model. We explain the algorithm and its motivation both from formal and
intuitive perspectives, and demonstrate its functionality in comparison with
deterministic alignment using real-life datasets.
