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一个并行的社会网络分析对学生使用虚拟化?年代学术进步

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“大数据的下一个前沿创新,竞争和生产力”[1]。大数据是巨大的和混乱的生成速度非常快。这些特点构成了数据存储和处理的问题,但关注这些因素导致了很多纸上谈兵。社交网络产生的数据包括结构化(10%)和非结构化(90%),巨大的体积和越来越大的挑战来处理和分析。大数据技术在技术发展提供了重大贡献。此外,现在一天也有更高的对学生的学业成绩的影响。LinkedIn等社交网站,推特和脸书在年轻一代中有这样的影响。本文是关于学生的参与社会媒体和他们的交互与他人(学生,学生和学生导师)通过这个网络。通过这个分析,他们对社交网络的互动和沟通将有助于找到他们的兴趣和需求。这种分析也列出一些web服务的使用和对语言的影响和学术行为的年轻的学习者。 In social network analysis the structured data are capable of storing and analyze using traditional analysis techniques. Here the unstructured data are from social media are considered as useless. But it is also needed to analyze and extract meaning full information from them. The unstructured huge volume of data could be processed under special environment with additional techniques. It is not feasible to process under traditional environment. Since the social network could be represented by socio graph, it is will be feasible by partitioning the big socio graph into computable size. Then it is taken as input into virtualized memory to analyze. Here the Mapper() and Reducer() programs are executed under virtual environment in distributed manner. The virtualization of process has been used to reduce the space complexity and time complexity. There are tools created to address the problem of big data. Hadoop is one of the best examples for dealing big data in a distributed environment. It involves breaking the huge socio graph into cliques of similar interest and analyzed to identify the students need for their development.

K。Geetha Dr.A。Vijaya Kathiravan

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