期刊文献+

基于知识图谱国际视野下大数据研究可视化分析 被引量:8

Visualization Analysis of Big Data Research Based on Knowledge Map from an International Perspective
下载PDF
导出
摘要 在云计算等技术的支撑下,大数据时代充满机遇与挑战,为全面了解大数据环境,利用可视化分析工具Bibexecl、Cite Space III对近十年来大数据研究知识图谱进行可视化分析。在了解大数据知识背景的基础上,展现文献分布特点和知识基础,及时把握国际大数据研究动态、学术前沿及发展脉络。最后提出结论和建议,大数据时代需要跨领域、跨区域研究力量的协作,同时隐私保护问题也应引起关注。大数据促进教育改革,催生数据共享联盟的成立。 With the support of cloud computing technology, the age of big data is full of opportunities and challenges. In order to fully understand the big data environment, we use visual analysis tools Bibexecl and CiteSpacellI to conduct visual analysis of knowledge map on big data research in the past decade. Based on background knowledge of big data, we demonstrate the distribution and knowledge base of literature, and identify the international dynamic, academic frontiers and development path of big data. In the end, we put forward conclusions and recommendations. The age of big data requires collaboration of interdisciplinary and cross-regional forces. Meanwhile, the issue of privacy should arouse due concern. Big data promotes education reforms and the establishment of data sharing alliance.
出处 《图书馆杂志》 CSSCI 北大核心 2016年第5期13-19,31,共8页 Library Journal
基金 河南省科技厅软科学研究计划项目"全民信息素养教育体系构建研究"(项目编号:112300450013)的成果之一 新乡医学院研究生科研创新支持计划项目"移动互联网用户信息利用行为研究"(项目编号:YJSCX20451Y)的研究成果之一
关键词 大数据 知识图谱 云计算 数据挖掘 数据共享联盟 隐私保护 Big data, Knowledge map, Cloud computing, Data mining, Data sharing alliance, Privacy protection
  • 相关文献

参考文献12

  • 1Toffler A. The third wave[M]. New York: Banlam books, 1981.
  • 2Manyika J, Chui M, Brown B, etal. Big data: The next frontier for innovation, competition, and productivity [EB/OL]. [2014-11-04]. http://www. mckinsev, tom/insights/business_technology/big_ data the next fron-lier for innovation.
  • 3Sehroeck M, Shockley R, Smart J, et al. Analytics: The real-world use of big da-ta [EB/OL]. [2014- 11-04]. H-tip: //www-03. ibm. com/systems/hu/ resources/the real wc, rd use of_bigdata, pdf.
  • 4Chen C. CiteSpace lI: Detecting and visualizing emerging trends anti transient patterns in scientific literature[J]. Journal of the American Society for information Science and Technology, 2006, 57(3): 359-377.
  • 5杨绎.基于文献计量的“大数据”研究[J].图书馆杂志,2012,31(9):29-32. 被引量:63
  • 6Dean J, Ghemawat S. MapReduce: simplified data processing on large clusters[J]. Communicalions of the ACM, 2008, 51(1): 107-113.
  • 7The fourlh paradigm: dala-inlensive scientific discovery[M]. 2009.
  • 8Schadl E E, Linderman M D, Sorenson J, el al. Computational sdutions Io large-scale dala management' and analysis[J]. Nature Reviews Genetics, 2010, 11(9): 647-657.
  • 9Bates D W, Saria S, Ohno-Maehado L, el al. Big data in health care: using analytics to idenlify and manage high-risk and high-cost patients[J]. Health Affairs, 2014, 33(7): 1123-1131.
  • 10Well A R. Big data in health: a new era for research and patient care[J}. Health Affairs, 2014, 33(7): 1 110-1110.

二级参考文献2

共引文献62

同被引文献62

引证文献8

二级引证文献59

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部