Knowledge storage and acquisition for industrial cyber-physical systems based on non-relational database

Abstract

Industrial cyber-physical systems (iCPS) provide horizontal and vertical integration between various devices and systems. With the amount of information in iCPS increasing rapidly, data storage and processing mechanism must be scalable and flexible enough to suit requirements of controllers, sensors, and actuators. In addition, collected data should be further organized as local knowledge fragments to support device-level intelligence. Distributed knowledge fragments can be linked together to provide support for decision making by adopting semantic web technologies. This paper presents a data management approach for devices that utilize the non-relational database to store and query ontological knowledge bases. The goal of this work is to provide efficient processing for enabling intelligence on machines during real-time operations.