  Decisions and the underlying rules are indispensable for driving process
execution during runtime, i.e., for routing process instances at alternative
branches based on the values of process data. Decision rules can comprise unary
data conditions, e.g., age > 40, binary data conditions where the relation
between two or more variables is relevant, e.g. temperature1 < temperature2,
and more complex conditions that refer to, for example, parts of a medical
image. Decision discovery aims at automatically deriving decision rules from
process event logs. Existing approaches focus on the discovery of unary, or in
some instances binary data conditions. The discovered decision rules are
usually evaluated using accuracy, but not with regards to their semantics and
meaningfulness, although this is crucial for validation and the subsequent
implementation/adaptation of the decision rules. Hence, this paper compares
three decision mining approaches, i.e., two existing ones and one newly
described approach, with respect to the meaningfulness of their results. For
comparison, we use one synthetic data set for a realistic manufacturing case
and the two real-world BPIC 2017/2020 logs. The discovered rules are discussed
with regards to their semantics and meaningfulness.
