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 from the Perspective of Bibliometrics

作　　者：高继平[1] 马峥[1] 潘云涛[1] 张玉华[1] 

机构地区：[1]中国科学技术信息研究所,北京100038

出　　处：《科技管理研究》2016年第16期177-182,共6页Science and Technology Management Research

基　　金：国家科技支撑计划项目"向科技情报分析的信息服务系统研发与应用示范"(2015BAH25F00)

摘　　要：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,识别不同研究方向下的代表性专家,并对其主要研究成果进行分析。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。With the tide of big data rising all over the world, the paper took the papers of big data in the Scopus as the a- nalysis target, and adopted cluster analysis on the co -keywords network to form 10 research subjects, including Hadoop a- nalysis, MapReduce optimization, data mining, network seeurity and privacy, distributed eomputer system, data - inten- sive computing, data visualization, internet of things, mobile eloud computing and cloud manufaeturing. And then based on the indicators of paper number, I-I index and academic excellence, we distinguished the representative experts in the differ- ent research subject and analyzed their core researches. At last, there were more top scientists in the field of network secur- ity and privacy.

关 键 词：大数据 研究方向 代表性专家 论文数 H指数 

分 类 号：G353.1[文化科学—情报学] G201