Uncertain Process Data with Probabilistic Knowledge: Problem
  Characterization and Challenges
  Motivated by the abundance of uncertain event data from multiple sources
including physical devices and sensors, this paper presents the task of
relating a stochastic process observation to a process model that can be
rendered from a dataset. In contrast to previous research that suggested to
transform a stochastically known event log into a less informative uncertain
log with upper and lower bounds on activity frequencies, we consider the
challenge of accommodating the probabilistic knowledge into conformance
checking techniques. Based on a taxonomy that captures the spectrum of
conformance checking cases under stochastic process observations, we present
three types of challenging cases. The first includes conformance checking of a
stochastically known log with respect to a given process model. The second case
extends the first to classify a stochastically known log into one of several
process models. The third case extends the two previous ones into settings in
which process models are only stochastically known. The suggested problem
captures the increasingly growing number of applications in which sensors
provide probabilistic process information.
