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Decisions and the underlying rules are indispensable for driving processexecution during runtime, i.e., for routing process instances at alternativebranches based on the values of process data. Decision rules can comprise unarydata conditions, e.g., age > 40, binary data conditions where the relationbetween two or more variables is relevant, e.g. temperature1 < temperature2,and more complex conditions that refer to, for example, parts of a medicalimage. Decision discovery aims at automatically deriving decision rules fromprocess event logs. Existing approaches focus on the discovery of unary, or insome instances binary data conditions. The discovered decision rules areusually evaluated using accuracy, but not with regards to their semantics andmeaningfulness, although this is crucial for validation and the subsequentimplementation/adaptation of the decision rules. Hence, this paper comparesthree decision mining approaches, i.e., two existing ones and one newlydescribed approach, with respect to the meaningfulness of their results. Forcomparison, we use one synthetic data set for a realistic manufacturing caseand the two real-world BPIC 2017/2020 logs. The discovered rules are discussedwith regards to their semantics and meaningfulness.

Comparing decision mining approaches with regard to the meaningfulness of their results