Research on Semantic++ Computing Based on Big Data Environment
Ye Liang, Guigang Zhang, Chunxiao Xing, Yong Zhang, Chao Li
Abstract
Ye Liang, Guigang Zhang, Chunxiao Xing, Yong Zhang, Chao Li
Abstract
With the development of cloud computing and IoT, more and more data-intensive applications have come into being. It is very import to process the big data for these data-intensive applications. In this paper, we do some analysis about the basic concepts of big data and semantic++ computing. It includes the definition of big data, semantic computing and semantic++ computing. In this paper, we make a detail analysis about the semantic++ understanding based on big data. It mainly includes semantic++ storage of big data resources, semantic++ information acquirement of big data resources, semantic++ resources management, semantic++ processing of big data, semantic++ service of big data, semantic++ security and privacy of big data, semantic++ interface and applications based on big data. Two kinds of semantic++ applications are proposed. In this paper, a semantic++ search and recommendation system framework is analyzed. Finally, the simulation experiment shows that the semantic++ search and recommendation method is more efficient than the traditional method.
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With the development of cloud computing and IoT, more and more data-intensive applications have come into being. It is very import to process the big data for these data-intensive applications. In this paper, we do some analysis about the basic concepts of big data and semantic++ computing. It includes the definition of big data, semantic computing and semantic++ computing. In this paper, we make a detail analysis about the semantic++ understanding based on big data. It mainly includes semantic++ storage of big data resources, semantic++ information acquirement of big data resources, semantic++ resources management, semantic++ processing of big data, semantic++ service of big data, semantic++ security and privacy of big data, semantic++ interface and applications based on big data. Two kinds of semantic++ applications are proposed. In this paper, a semantic++ search and recommendation system framework is analyzed. Finally, the simulation experiment shows that the semantic++ search and recommendation method is more efficient than the traditional method.
Key concepts: Computer science, Semantic computing, Big data, Semantic grid, Semantic technology, Semantic analytics, Semantic Web Stack, Semantic data model