دانلود مقاله انگلیسی رایگان:به سوی مدل سازی و بهینه سازی انتخاب ویژگی ها در داده های بزرگ مبتنی بر اینترنت اشیا اجتماعی - 2018
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  • Toward modeling and optimization of features selection in Big Data based social Internet of Things Toward modeling and optimization of features selection in Big Data based social Internet of Things
    Toward modeling and optimization of features selection in Big Data based social Internet of Things

    سال انتشار:

    2018


    عنوان انگلیسی مقاله:

    Toward modeling and optimization of features selection in Big Data based social Internet of Things


    ترجمه فارسی عنوان مقاله:

    به سوی مدل سازی و بهینه سازی انتخاب ویژگی ها در داده های بزرگ مبتنی بر اینترنت اشیا اجتماعی


    منبع:

    Sciencedirect - Elsevier - Future Generation Computer Systems, 82 (2018) 715-726: doi:10:1016/j:future:2017:09:028


    نویسنده:

    Awais Ahmad a,*, Murad Khan b, Anand Paul c, Sadia Din c, M. Mazhar Rathore c, Gwanggil Jeon d, Gyu Sang Choi a,*


    چکیده انگلیسی:

    The growing gap between users and the Big Data analytics requires innovative tools that address the challenges faced by big data volume, variety, and velocity. Therefore, it becomes computationally inefficient to analyze and select features from such massive volume of data. Moreover, advancements in the field of Big Data application and data science poses additional challenges, where a selection of appropriate features and High-Performance Computing (HPC) solution has become a key issue and has attracted attention in recent years. Therefore, keeping in view the needs above, there is a requirement for a system that can efficiently select features and analyze a stream of Big Data within their requirements. Hence, this paper presents a system architecture that selects features by using Artificial Bee Colony (ABC). Moreover, a Kalman filter is used in Hadoop ecosystem that is used for removal of noise. Furthermore, traditional MapReduce with ABC is used that enhance the processing efficiency. Moreover, a complete four-tier architecture is also proposed that efficiently aggregate the data, eliminate unnecessary data, and analyze the data by the proposed Hadoop-based ABC algorithm. To check the efficiency of the proposed algorithms exploited in the proposed system architecture, we have implemented our proposed system using Hadoop and MapReduce with the ABC algorithm. ABC algorithm is used to select features, whereas, MapReduce is supported by a parallel algorithm that efficiently processes a huge volume of data sets. The system is implemented using MapReduce tool at the top of the Hadoop parallel nodes with near real time. Moreover, the proposed system is compared with Swarm approaches and is evaluated regarding efficiency, accuracy and throughput by using ten different data sets. The results show that the proposed system is more scalable and efficient in selecting features.
    Keywords: SIoT ، Big Data ، ABC algorithm، Feature selection


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 12
    حجم فایل: 1789 کیلوبایت

    قیمت: رایگان


    توضیحات اضافی:




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