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Decision mining enables the discovery of decision rules from event logs orstreams, and constitutes an important part of in-depth analysis andoptimisation of business processes. So far, decision mining has been merelyapplied in an ex-post way resulting in a snapshot of decision rules for thegiven chunk of log data. Online decision mining, by contrast, enablescontinuous monitoring of decision rule evolution and decision drift. Hence thispaper presents an end-to-end approach for the discovery as well as monitoringof decision points and the corresponding decision rules during runtime,bridging the gap between online control flow discovery and decision mining. Theapproach provides automatic decision support for process-aware informationsystems with efficient decision drift discovery and monitoring. For monitoring,not only the performance, in terms of accuracy, of decision rules is taken intoaccount, but also the occurrence of data elements and changes in branchingfrequency. The paper provides two algorithms, which are evaluated on foursynthetic and one real-life data set, showing feasibility and applicability ofthe approach. Overall, the approach fosters the understanding of decisions inbusiness processes and hence contributes to an improved human-processinteraction.

An End-to-End Approach for Online Decision Mining and Decision Drift Analysis in Process-Aware Information Systems: Extended Version