An End-to-End Approach for Online Decision Mining and Decision Drift Analysis in Process-Aware Information Systems: Extended Version
This work addresses the need for automated decision support and drift monitoring in process-aware information systems, representing an incremental advancement by extending decision mining to online contexts.
The paper tackles the problem of discovering and monitoring decision rules in business processes during runtime, presenting an end-to-end approach that enables continuous tracking of decision drift and rule evolution, with evaluation on synthetic and real-life datasets showing feasibility and applicability.
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.