Towards Semantics-based Monitoring of Large-Scale Industrial Systems

Monitoring today's industrial systems steadily grows in terms of complexity. Examples can be found in industry automation, building management, the medical and the transportation domain. Increasing numbers of components, increasing numbers of sensors producing monitoring data about these components, and increasing numbers of stakeholders managing this data lead to suboptimal exploitation of the actually available monitoring data. What is required, is a semantically meaningful way of representing, integrating and querying monitoring data from different sources. To this end, we propose to apply Semantic Web technologies to the monitoring of large-scale systems. This creates new challenges such as incorporating dynamic real-world data and supporting scalable reasoning over this data. As an approach to this problem, we present a generic architecture with different functional layers . We go into more detail about the distributed reasoning layer, where we present the idea of loosely coupled collaboration of several reasoners. Finally, we describe the current state of our prototype implementation, where we adopt a scenario from the railway monitoring domain, and present first evaluation results.