  Encoding methods are employed across several process mining tasks, including
predictive process monitoring, anomalous case detection, trace clustering, etc.
These methods are usually performed as preprocessing steps and are responsible
for transforming complex information into a numerical feature space. Most
papers choose existing encoding methods arbitrarily or employ a strategy based
on a specific expert knowledge domain. Moreover, existing methods are employed
by using their default hyperparameters without evaluating other options. This
practice can lead to several drawbacks, such as suboptimal performance and
unfair comparisons with the state-of-the-art. Therefore, this work aims at
providing a comprehensive survey on event log encoding by comparing 27 methods,
from different natures, in terms of expressivity, scalability, correlation, and
domain agnosticism. To the best of our knowledge, this is the most
comprehensive study so far focusing on trace encoding in process mining. It
contributes to maturing awareness about the role of trace encoding in process
mining pipelines and sheds light on issues, concerns, and future research
directions regarding the use of encoding methods to bridge the gap between
machine learning models and process mining.
