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.