Real-time Data Analytics and Event Detection for IoT-enabled Communication Systems

Muhammad Intizar Ali, Naomi Ono, Mahedi Kaysar, Zia Ush Shamszaman, Thu-Le Pham, Feng Gao, Keith Griffin, Alessandra Mileo


Enterprise Communication Systems are designed in such a way to maximise the eciency of communication and collaboration within the enterprise. With users becoming mobile, the Internet of Things (IoT) can play a crucial role in this process, but is far from being seamlessly integrated into modern online communications. In this paper, we present a semantic infrastructure for gathering, integrating and reasoning upon heterogeneous, distributed and continuously changing data streams by means of semantic technologies and rule-based inference. Our solution exploits semantics to go beyond today's ad-hoc integration and processing of heterogeneous data sources for static and streaming data. It provides
exible and ecient processing techniques that can transform low-level data into high-level abstractions and actionable knowledge, bridging the gap between IoT and online Enterprise Communication Systems. We document the technologies used for acquisition and semantic enrichment of sensor data, continuous semantic query processing for integration and ltering, as well as stream reasoning for decision support. Our main contributions are the following, i) we dene and deploy a semantic processing pipeline for IoT-enabled Communication Systems, which builds upon existing systems for semantic data acquisition, continuous query processing and stream reasoning, detailing the implementation of each component of our framework; ii) we present a rich semantic information model for representing and linking IoT data, social data and personal data in the Enterprise Communication scenario, by reusing and extending existing standard semantic models; iii) we dene and develop an expressive stream reasoning component as part of our framework, based on continuous query processing and non-monotonic reasoning for semantic streams, iv) we conduct experiments to comparatively evaluate the performance of our data acquisition and semantic annotation layer based on OpenIoT, and the performance of our expressive reasoning layer in the scenario of Enterprise Communication.

Full Text: Untitled
Type of Paper: Research paper
Keywords: RDF Stream Processing, Stream Federation, Communication Systems, OpenIoT, Linked Data, Stream Reasoning, IoT
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