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A Semantic Complex Event Processing Framework for Internet of Things Applications. Towards Detecting Complex Events in Stream Processing
Yemson, Rose A.
Yemson, Rose A.
Publication Date
2023
End of Embargo
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The University of Bradford theses are licenced under a Creative Commons Licence.
Peer-Reviewed
Open Access status
Accepted for publication
Institution
University of Bradford
Department
School of Computer Science, AI and Electronics. Faculty of Engineering and Digital Technologies
Awarded
2023
Embargo end date
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Abstract
The rapid growth of the internet of things (IoT) has led to an overwhelming
volume of data generated by interconnected devices. Effectively extracting
valuable insights from this data in real-time is crucial for informed
decision-making and optimizing IoT applications. This research explores
the integration of traditional complex event processing (CEP) with semantic
web technologies to detect complex events in real-time streaming data
analysis within the IoT domain.
The research develops a semantic complex event processing framework tailored
specifically for IoT applications. By leveraging the strengths of traditional
CEP in detecting complex event patterns and semantic web technologies
in providing standardised data representation and reasoning capabilities,
the integrated approach proves to be a powerful solution for event
detection. The framework demonstrates enhanced accuracy, real-time analysis
capabilities, and the ability to handle heterogeneous data sources.
The proposed traditional CEP with semantic web technologies framework is
thoroughly evaluated and experimented with to assess its performance and
effectiveness in real-time event detection. Performance metrics, including event detection efficiency, scalability, and accuracy of generated insights,
are used to compare the framework against traditional CEP.
The research findings emphasize the significance of integrating traditional
CEP with semantic web technologies in real-time IoT analytics. The proposed
framework improves event detection efficiency, scalability, and accuracy,
empowering IoT applications with intelligent event processing capabilities.
These results provide valuable insights into IoT data analytics
and have the potential to revolutionise the way we analyse and leverage IoT
data for informed decision-making and optimised system performance.
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Type
Thesis
Qualification name
PhD