  Decision mining enables the discovery of decision rules from event logs or
streams, and constitutes an important part of in-depth analysis and
optimisation of business processes. So far, decision mining has been merely
applied in an ex-post way resulting in a snapshot of decision rules for the
given chunk of log data. Online decision mining, by contrast, enables
continuous monitoring of decision rule evolution and decision drift. Hence this
paper presents an end-to-end approach for the discovery as well as monitoring
of decision points and the corresponding decision rules during runtime,
bridging the gap between online control flow discovery and decision mining. The
approach provides automatic decision support for process-aware information
systems with efficient decision drift discovery and monitoring. For monitoring,
not only the performance, in terms of accuracy, of decision rules is taken into
account, but also the occurrence of data elements and changes in branching
frequency. The paper provides two algorithms, which are evaluated on four
synthetic and one real-life data set, showing feasibility and applicability of
the approach. Overall, the approach fosters the understanding of decisions in
business processes and hence contributes to an improved human-process
interaction.
