  Machine learning models are routinely integrated into process mining
pipelines to carry out tasks like data transformation, noise reduction, anomaly
detection, classification, and prediction. Often, the design of such models is
based on some ad-hoc assumptions about the corresponding data distributions,
which are not necessarily in accordance with the non-parametric distributions
typically observed with process data. Moreover, the learning procedure they
follow ignores the constraints concurrency imposes to process data. Data
encoding is a key element to smooth the mismatch between these assumptions but
its potential is poorly exploited. In this paper, we argue that a deeper
insight into the issues raised by training machine learning models with process
data is crucial to ground a sound integration of process mining and machine
learning. Our analysis of such issues is aimed at laying the foundation for a
methodology aimed at correctly aligning machine learning with process mining
requirements and stimulating the research to elaborate in this direction.
