Abstract
There is a growing trend towards integrating physical data into the Internet which is supported by sensor devices, smartphones, GPS and many other sources that capture and communicate real world data. Cyber-Physical Data describes the type of data that represents observations and measurements gathered by sensor devices. These sensor devices are capable of transforming physical information (e. g. light, temperature, coordinates) into digitised data. With tremendous volumes of Cyber-Physical Data that are created, novel methods have to be developed that facilitate processing and provisioning of the data. Automated techniques are required to extract and infer meaningful abstractions for the end-user and/or higher-level knowledge. Investigation of the related work leads to the conclusion that there has been significant work on communication and processing aspects of Cyber-Physical Data, however, there is a need for integrated solutions that contemplate the workflow from data acquisition to extraction and knowledge representation. We propose a set of novel solutions for Cyber-Physical Data communication and information processing by providing a middleware component that contains management and communication processing capabilities to deliver actionable knowledge to the end-user and services. We have developed a novel data abstraction method for Cyber-Physical Data. The abstraction method is based on a probabilistic graph model and machine-learning techniques to extract relevant information and infer knowledge from patterns that are represented by the abstracted data. The proposed approach is able to create human-readable/machine-interpretable abstractions from numerical sensor data with precision rate of 79% and recall of 94%. The automated ontology construction algorithm has a success rate of 84% of representing occurred events in the ontology. Finally, an integrated software system is introduced that uses the middleware and the information processing techniques to provide a complete workflow from data acquisition to knowledge acquisition and representation.