The Experts below are selected from a list of 4476 Experts worldwide ranked by ideXlab platform
Payam Barnaghi - One of the best experts on this subject based on the ideXlab platform.
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A knowledge-based approach for real-time IoT data stream annotation and processing
Proceedings - 2014 IEEE International Conference on Internet of Things iThings 2014 2014 IEEE International Conference on Green Computing and Communic, 2014Co-Authors: Sefki Kolozali, Maria Bermudez-edo, Daniel Puschmann, Frieder Ganz, Payam BarnaghiAbstract:Internet of Things is a generic term that refers to interconnection of real-world services which are provided by smart objects and sensors that enable interaction with the physical world. Cities are also evolving into large interconnected ecosystems in an effort to improve sustainability and operational efficiency of the city services and infrastructure. However, it is often difficult to perform real-time analysis of large amount of heterogeneous data and sensory information that are provided by various sources. This paper describes a framework for real-time semantic annotation of streaming IoT data to support dynamic integration into the Web using the Advanced Message Queuing Protocol (AMPQ). This will enable delivery of large volume of data that can influence the performance of the smart city systems that use IoT data. We present an information model to represent summarisation and reliability of stream data. The framework is evaluated with the data size and average Exchanged Message time using summarised and raw sensor data. Based on a statistical analysis, a detailed comparison between various sensor points is made to investigate the memory and computational cost for the stream annotation framework.
Sefki Kolozali - One of the best experts on this subject based on the ideXlab platform.
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A knowledge-based approach for real-time IoT data stream annotation and processing
Proceedings - 2014 IEEE International Conference on Internet of Things iThings 2014 2014 IEEE International Conference on Green Computing and Communic, 2014Co-Authors: Sefki Kolozali, Maria Bermudez-edo, Daniel Puschmann, Frieder Ganz, Payam BarnaghiAbstract:Internet of Things is a generic term that refers to interconnection of real-world services which are provided by smart objects and sensors that enable interaction with the physical world. Cities are also evolving into large interconnected ecosystems in an effort to improve sustainability and operational efficiency of the city services and infrastructure. However, it is often difficult to perform real-time analysis of large amount of heterogeneous data and sensory information that are provided by various sources. This paper describes a framework for real-time semantic annotation of streaming IoT data to support dynamic integration into the Web using the Advanced Message Queuing Protocol (AMPQ). This will enable delivery of large volume of data that can influence the performance of the smart city systems that use IoT data. We present an information model to represent summarisation and reliability of stream data. The framework is evaluated with the data size and average Exchanged Message time using summarised and raw sensor data. Based on a statistical analysis, a detailed comparison between various sensor points is made to investigate the memory and computational cost for the stream annotation framework.
Maria Bermudez-edo - One of the best experts on this subject based on the ideXlab platform.
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A knowledge-based approach for real-time IoT data stream annotation and processing
Proceedings - 2014 IEEE International Conference on Internet of Things iThings 2014 2014 IEEE International Conference on Green Computing and Communic, 2014Co-Authors: Sefki Kolozali, Maria Bermudez-edo, Daniel Puschmann, Frieder Ganz, Payam BarnaghiAbstract:Internet of Things is a generic term that refers to interconnection of real-world services which are provided by smart objects and sensors that enable interaction with the physical world. Cities are also evolving into large interconnected ecosystems in an effort to improve sustainability and operational efficiency of the city services and infrastructure. However, it is often difficult to perform real-time analysis of large amount of heterogeneous data and sensory information that are provided by various sources. This paper describes a framework for real-time semantic annotation of streaming IoT data to support dynamic integration into the Web using the Advanced Message Queuing Protocol (AMPQ). This will enable delivery of large volume of data that can influence the performance of the smart city systems that use IoT data. We present an information model to represent summarisation and reliability of stream data. The framework is evaluated with the data size and average Exchanged Message time using summarised and raw sensor data. Based on a statistical analysis, a detailed comparison between various sensor points is made to investigate the memory and computational cost for the stream annotation framework.
Frieder Ganz - One of the best experts on this subject based on the ideXlab platform.
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A knowledge-based approach for real-time IoT data stream annotation and processing
Proceedings - 2014 IEEE International Conference on Internet of Things iThings 2014 2014 IEEE International Conference on Green Computing and Communic, 2014Co-Authors: Sefki Kolozali, Maria Bermudez-edo, Daniel Puschmann, Frieder Ganz, Payam BarnaghiAbstract:Internet of Things is a generic term that refers to interconnection of real-world services which are provided by smart objects and sensors that enable interaction with the physical world. Cities are also evolving into large interconnected ecosystems in an effort to improve sustainability and operational efficiency of the city services and infrastructure. However, it is often difficult to perform real-time analysis of large amount of heterogeneous data and sensory information that are provided by various sources. This paper describes a framework for real-time semantic annotation of streaming IoT data to support dynamic integration into the Web using the Advanced Message Queuing Protocol (AMPQ). This will enable delivery of large volume of data that can influence the performance of the smart city systems that use IoT data. We present an information model to represent summarisation and reliability of stream data. The framework is evaluated with the data size and average Exchanged Message time using summarised and raw sensor data. Based on a statistical analysis, a detailed comparison between various sensor points is made to investigate the memory and computational cost for the stream annotation framework.
Daniel Puschmann - One of the best experts on this subject based on the ideXlab platform.
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A knowledge-based approach for real-time IoT data stream annotation and processing
Proceedings - 2014 IEEE International Conference on Internet of Things iThings 2014 2014 IEEE International Conference on Green Computing and Communic, 2014Co-Authors: Sefki Kolozali, Maria Bermudez-edo, Daniel Puschmann, Frieder Ganz, Payam BarnaghiAbstract:Internet of Things is a generic term that refers to interconnection of real-world services which are provided by smart objects and sensors that enable interaction with the physical world. Cities are also evolving into large interconnected ecosystems in an effort to improve sustainability and operational efficiency of the city services and infrastructure. However, it is often difficult to perform real-time analysis of large amount of heterogeneous data and sensory information that are provided by various sources. This paper describes a framework for real-time semantic annotation of streaming IoT data to support dynamic integration into the Web using the Advanced Message Queuing Protocol (AMPQ). This will enable delivery of large volume of data that can influence the performance of the smart city systems that use IoT data. We present an information model to represent summarisation and reliability of stream data. The framework is evaluated with the data size and average Exchanged Message time using summarised and raw sensor data. Based on a statistical analysis, a detailed comparison between various sensor points is made to investigate the memory and computational cost for the stream annotation framework.
