期刊文献+

大数据背景下的数据分析与可视化研究 被引量:18

Research on Data Analysis and Visualizationin Big Data Background
下载PDF
导出
摘要 大数据背景下,随着非结构化海量数据的产生,大数据的重要性就不仅仅体现在数据量巨大上,更重要的是如何对海量大数据进行数据分析和可视化,获取更多智能的、深入的和有价值的信息.笔者从大数据关键技术、特征等方面的综合视角出发,分析了大数据分析的领域以及分析数据的提取和大数据分析的总体框架,在此基础之上,对数据可视化的方法、技术和工具进行了对比和分析,提出了当前数据分析与可视化方面的应用前景以及遇到的问题与挑战. Under the big data background,along with the magnanimous non-structurized data production,the big data importance manifests not merely in the data quantity on greatly,how more importantly carries on the data analysis and the visualization to the magnanimous big data,gain more intelligences,thorough and the valuable information. The author mainly from big aspect and so on data key technologies,characteristic comprehensive angles of view embarked,has analyzed the big data analysis domain as well as the analysis data extraction and the big data analysis overall frame,above this foundation,to the data visualization method,the technology and the tool has carried on the contrast and the analysis,and proposed the current data analysis and the visualization aspect application prospect as well as met question and challenge.
作者 龙虎 杨晖
机构地区 凯里学院
出处 《凯里学院学报》 2016年第3期98-102,共5页 Journal of Kaili University
基金 贵州省科技厅 黔东南州科技局 凯里学院科技联合基金项目(编号:黔科合LH字[2014]7229号) 凯里学院院级规划课题(编号:Z1513)
关键词 大数据 数据分析 数据可视化 算法 Big data data analysis data visualization algorithm
  • 相关文献

参考文献5

二级参考文献41

  • 1宋国杰,唐世渭,杨冬青,王腾蛟.数据流中异常模式的提取与趋势监测[J].计算机研究与发展,2004,41(10):1754-1759. 被引量:19
  • 2David M. Blei.Probabilistic topic models[J]. Communications of the ACM . 2012 (4)
  • 3Erik Meijer.The world according to LINQ[J]. Communications of the ACM . 2011 (10)
  • 4Daniel M. Dunlavy,Tamara G. Kolda,Evrim Acar.Temporal Link Prediction Using Matrix and Tensor Factorizations[J]. ACM Transactions on Knowledge Discovery from Data (TKDD) . 2011 (2)
  • 5Soumen Chakrabarti,Martin van den Berg,Byron Dom.Focused crawling: a new approach to topic-specific Web resource discovery[J]. Computer Networks . 1999 (11)
  • 6Abadi D, Agrawal R, Ailamaki A, et al. The beckman report on database research[R]. (2013-10-15)[2014-11- 30]. http://beckman. cs. wise, edu/.
  • 7International Data Corporation. EMC digital universe study with research and analysis by IDC[R]. 2014[2014-11-30]. http://www.emc.com/leadership/digital-universe/index.htm.
  • 8A thanassoulis M, Chen S, Ailamaki A, et al. MaSM: Efficient online updates in data warehouses[C]//Proc of the SIGMOD Int Conf on Management of Data. New York: ACM, 2011: 865-876.
  • 9Cao Z, Chen S, Li F, et al. LogKV: Exploiting Key-Value Stores for Event Log Processing[C/OL]//Proc of the 6th Biennial Conf on Innovative Data Systems Research. 2013[2014-11-30]. http://www. cidrdb. org.
  • 10Transaction Processing Council.[2014-11-30]. http://www. tpc. org.

共引文献838

同被引文献62

引证文献18

二级引证文献58

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