A Principled Approach to Mining From Noisy Logs Using Heuristics Miner

Noise is a challenge for process mining algorithms, but there is no
standard definition of noise nor accepted way to quantify it. This
means it is not possible to mine with confidence from event logs
which may not record the underlying process correctly. We discuss
one way of thinking about noise in process mining. We consider mining
from a `noisy log' as learning a probability distribution over traces,
representing the true process, from a log which is a sample from
multiple distributions: the `true' process model and one or more
`noise' models. We apply this using a probabilistic analysis of the
Heuristics Miner algorithm, and demonstrate on a simple example.
We show that for a given model it is possible to predict how much
data is needed to mine the underlying model without the noise, and
identify differences in the the robustness of Heuristics Miner to
different types of noise.