Abstract
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 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 is evaluated for feasibility and applicability on four synthetic and one real-life data set.
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Acknowledgements
This work has been partly supported and funded by the Austrian Research Promotion Agency (FFG) via the Austrian Competence Center for Digital Production (CDP) under the contract number 881843 and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – project number 277991500.
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Scheibel, B., Rinderle-Ma, S. (2023). An End-to-End Approach for Online Decision Mining and Decision Drift Analysis in Process-Aware Information Systems. In: Cabanillas, C., Pérez, F. (eds) Intelligent Information Systems. CAiSE 2023. Lecture Notes in Business Information Processing, vol 477. Springer, Cham. https://doi.org/10.1007/978-3-031-34674-3_3
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