农业图书情报学刊 ›› 2017, Vol. 29 ›› Issue (4): 76-80.doi: 10.13998/j.cnki.issn1002-1248.2017.04.016

• 文献研究 • 上一篇    下一篇

大数据下数字图书馆热点研究——基于关键词的因子降维分析方式

刘佳美, 程结晶   

  1. 曲阜师范大学,山东 日照 276800
  • 收稿日期:2016-10-19 出版日期:2017-04-05 发布日期:2017-04-11
  • 作者简介:刘佳美(1992-),女,硕士,研究方向:大数据分析,数据挖掘。程结晶(1967-),男,教授,研究方向:图书馆服务模式创新,信息资源开发与管理,大数据分析。

Research on the Hotspots of Digital Library under the Big Data by Applying Factor Dimension Reduction Analysis Based on Keywords

LIU Jiamei, CHENG Jiejing   

  1. Qufu Normal University, Shandong Rizhao 276800, China
  • Received:2016-10-19 Online:2017-04-05 Published:2017-04-11

摘要: 随着大数据时代的到来,数字图书馆作为时代新兴学习空间,其热点趋势分析有着不可估量的价值。 选取2010年至今的70篇论文作为研究样本,运用bicomb书目分析系统提取关键词,形成关键词共现矩阵。借助SPSS软件,通过因子降维方式分析出六大主成分热点,即大数据下数字图书馆资源合理化建设、数据结构的创新、云计算和大数据技术、知识服务模型、知识资源整合服务、未来发展规划和创新策略。通过相关热点的聚类分析,继续挖掘大数据时代数字图书馆新兴的研究价值点,促进未来大数据环境下数字图书馆的可持续发展。

关键词: 大数据, 数字图书馆, , 因子分析

Abstract: With the arrival of the new era of big data, digital library has become a new learning space. The analysis on its hotspots and trend has immeasurable value. This paper selected 70 papers from 2010 to now as the research sample, and used BICOMB bibliographic analysis system to extract keywords and form co-occurrence matrix of keywords. With the help of SPSS software, the six main components of hot spot were analyzed by means of factor dimension reduction method, including the rational construction of digital library resource under the big data, the data structure of innovation, cloud computing and big data technology, knowledge service model, knowledge resource integration service, future development of the planning and innovation strategy. Through the analysis of relevant hotspots, the emerging research value points were continued to dig to promote the sustainable development of digital library in the future environment of big data.

Key words: big data

中图分类号: 

  • G250

引用本文

刘佳美, 程结晶. 大数据下数字图书馆热点研究——基于关键词的因子降维分析方式[J]. 农业图书情报学刊, 2017, 29(4): 76-80.

LIU Jiamei, CHENG Jiejing. Research on the Hotspots of Digital Library under the Big Data by Applying Factor Dimension Reduction Analysis Based on Keywords[J]. , 2017, 29(4): 76-80.