دانلود مقاله انگلیسی رایگان:سیستم عامل اطلاعات داده کاوی انرژی مبتنی بر ابر مبتنی بر فناوری تجزیه و تحلیل داده های بزرگ - 2019
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  • A cloud-based energy data mining information agent system based on big data analysis technology A cloud-based energy data mining information agent system based on big data analysis technology
    A cloud-based energy data mining information agent system based on big data analysis technology

    سال انتشار:

    2019


    عنوان انگلیسی مقاله:

    A cloud-based energy data mining information agent system based on big data analysis technology


    ترجمه فارسی عنوان مقاله:

    سیستم عامل اطلاعات داده کاوی انرژی مبتنی بر ابر مبتنی بر فناوری تجزیه و تحلیل داده های بزرگ


    منبع:

    Sciencedirect - Elsevier - Microelectronics Reliability, 97 (2019) 66-78: doi:10:1016/j:microrel:2019:03:010


    نویسنده:

    Hsueh-Yuan Lina, Sheng-Yuan Yangb,⁎


    چکیده انگلیسی:

    2019 is the first year of 5G and the information flow is growing even more; therefore, data mining technology is one of the key technologies regarding how to find useful information from the vast information flow. This paper aims to develop the cloud-based energy data mining information agent system OntoDMA, as based on the WIAS cloud environment and Big Data analysis technology, which is embedded in a cloud-based active multi-agent system to proactively provide appropriate, real-time, and fast domain information prediction. On one hand, the related technologies for constructing web service platforms are shared; on the other hand, how to widely and seamlessly integrate and support the cloud interaction paradigm handled by the data mining agent system through these technologies is explored. In order to outline the feasibility of the proposed system architecture, a case study is conducted on the energy saving information system, and the relevant R&D results are presented in detail. Then, both the preliminary system R&D interface and experimental verification are illustrated. Finally, the cache performance of the Solutions Pool is increased by 19.82%, the query workload of the Prediction Rules is reduced by 66.51%, and the overall operating time is decreased by 5.21%, which effectively and efficiently relieves the workload on the back-end servo system.
    Keywords: Data mining agent systems | Big Data analysis | Web services | Energy saving agent systems


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 13
    حجم فایل: 1426 کیلوبایت

    قیمت: رایگان


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