Decision mining enables discovery of decision rules guiding the control flow in processes. Existing decision mining techniques deal with different kinds of decision rules, e.g., overlapping rules, or includ- ing data elements, for example, time series data. Though online process mining and monitoring are gaining traction, online decision mining al- gorithms are still missing. Decision rules can be, similarly to process models, subject to change during runtime due to, for example, changing regulations or customer requirements. In order to address these runtime challenges, this paper proposes an approach that i) discovers decision rules during runtime and ii) continuously monitors and adapts discov- ered rules to reflect changes. Furthermore, the concept of a decision rule history is proposed, enabling (manual) identification of change patterns. The feasibility and the applicability of the approach is evaluated based on three synthetic datasets, BPIC12, BPIC20 and sepsis data set.

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