大数据领域代表性专家识别与分析——文献计量学视角

Identification and Analysis of Representative Experts in the Big Data——In the Perspective of Bibliometrics

摘要:

大数据浪潮席卷全球,本文以Scopus数据库中“大数据”相关研究作为分析对象,采用关键词共现网络聚类的方法,确定了相关的10个研究方向,分别是Hadoop研究、MapReduce优化、大数据下的数据挖掘研究、大数据下的网络安全与隐私、分布式计算机系统、数据密集型计算、可视化技术、物联网、移动云计算和云制造等研究方向。结合论文数、H指数等指标,本文识别了不同研究方向下的代表性专家,并对其主要研究成果进行了分析。结果发现：网络安全与隐私中顶尖专家较多,同时在云制造方向下,我国大陆地区的研究人员较多。

Abstract:

With the tide of big data rising all over the world, the paper took the papers of big data in the Scopus as the analysis target, and adopt cluster analysis on the co-keywords network to form 10 research subjects, including Hadoop analysis, MapReduce optimization, data mining, network security and privacy, distributed computer system, data-intensive computing, data visualization, internet of things, mobile cloud computing and cloud manufacturing. And then based on the indicators paper number, H index and academic excellence, we distinguished the representative experts in the different research subject and analyzed their core researches. At last, there were more top scientists in the field of network security and privacy.