  Developments in machine learning together with the increasing usage of sensor
data challenge the reliance on deterministic logs, requiring new process mining
solutions for uncertain, and in particular stochastically known, logs. In this
work we formulate {trace recovery}, the task of generating a deterministic log
from stochastically known logs that is as faithful to reality as possible. An
effective trace recovery algorithm would be a powerful aid for maintaining
credible process mining tools for uncertain settings. We propose an algorithmic
framework for this task that recovers the best alignment between a
stochastically known log and a process model, with three innovative features.
Our algorithm, SKTR, 1) handles both Markovian and non-Markovian processes; 2)
offers a quality-based balance between a process model and a log, depending on
the available process information, sensor quality, and machine learning
predictiveness power; and 3) offers a novel use of a synchronous product
multigraph to create the log. An empirical analysis using five publicly
available datasets, three of which use predictive models over standard video
capturing benchmarks, shows an average relative accuracy improvement of more
than 10 over a common baseline.
