{"id":{"repo_id":"bradford","oai_identifier":"oai:bradscholars.brad.ac.uk:10454/20046"},"canonical_url":"https://search.dev.ndltd.org/etd/bradford/oai:bradscholars.brad.ac.uk:10454/20046","repository":{"repo_id":"bradford","name":"University of Bradford","base_url":"https://bradscholars.brad.ac.uk/oai/request"},"display":{"title":"A Semantic Complex Event Processing Framework for Internet of Things Applications. Towards Detecting Complex Events in Stream Processing","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Yemson, Rose A."],"institution":"University of Bradford","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Kabir, Sohag","Konur, Savas","Thakker, Dhaval"],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023","date_published":"2023","updated_at":"2026-07-24T01:14:55Z","subjects":["Semantic web technologies","Traditional complex event processing (CEP)","Internet of Things (IoT)","Ontology","Complex event detection","Integration"],"languages":["en"],"rights":["\"Creative
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