Abstract
The full behavior of cyber-physical systems (CPS) emerges during operation only, when the systems interact with their environment. Runtime monitoring approaches are used to detect deviations from the expected behavior. While most monitoring approaches assume that engineers define the expected behavior as constraints, the deep domain knowledge required for this task is often not available. We describe an approach that automatically mines constraint candidates for runtime monitoring from event logs recorded from CPS. Our approach extracts different types of constraints on event occurrence, timing, data, and combinations of these. The approach further presents the mined constraint candidates to users and offers filtering and ranking strategies. We demonstrate the usefulness and scalability of our approach by applying it to event logs from two real-world CPS: a plant automation software system and a system controlling unmanned aerial vehicles. In our experiments, domain experts regarded 74% and 63%, respectively, of the constraints mined for these two systems as useful.
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The financial support by the Austrian Federal Ministry for Digital and Economic Affairs, the National Foundation for Research, Technology and Development, and Primetals Technologies is gratefully acknowledged.
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Krismayer, T., Rabiser, R., Grünbacher, P. (2019). A Constraint Mining Approach to Support Monitoring Cyber-Physical Systems. In: Giorgini, P., Weber, B. (eds) Advanced Information Systems Engineering. CAiSE 2019. Lecture Notes in Computer Science(), vol 11483. Springer, Cham. https://doi.org/10.1007/978-3-030-21290-2_41
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