Topic Analysis of LIS Big Data Research with Overlay Mapping
Chen Shiji,Qiu Junping(),Yu Bo
Chinese Academy of Science and Education Evaluation (CASEE), Hangzhou Dianzi University,Hangzhou 310018, China Academy of Data Science and Informetrics (ADSI), Hangzhou Dianzi University, Hangzhou 310018, China
[Objective] This paper explores the topics of big data research in Library and Information Science (LIS), aiming to reveal their developing trends. [Methods] We used “big data” as keyword to search the Web of Science and then constructed a test collection with the retrieved documents. Based on the citation analysis, we removed those irrelevant documents. Then, we used the Leiden algorithm and the VOSviewer to construct the science mapping on LIS big data research. Finally, we created the overlay mapping of research topics. [Results] According to the citation analysis, LIS big data research focuses on big data and social media analysis, followed by cloud computing, machine learning, big data technologies (such as Hadoop and MapReduce), health information, precision medicine, industry 4.0 and Internet of Things. [Limitations] We only analyzed the themes and development trends of LIS big data research from the macro-perspective. [Conclusions] Big data is an important LIS research topic. Popular studies focuses on big data and social media analysis. Machine learning, health information, precision medicine, Industry 4.0 and the Internet of Things are the important directions for Library and Information Science.
陈仕吉, 邱均平, 余波. 基于Overlay图谱的图情领域大数据主题分析*[J]. 数据分析与知识发现, 2021, 5(10): 51-59.
Chen Shiji, Qiu Junping, Yu Bo. Topic Analysis of LIS Big Data Research with Overlay Mapping. Data Analysis and Knowledge Discovery, 2021, 5(10): 51-59.
internet of things, cloud computing, edge computing, fog computing, smart city
C5
8
121
social media, deep learning, machine learning, human mobility, mobile phone data
C10
4
109
machine learning, data science, climate change, digital agriculture, smart farming
C4
1
102
machine learning, deep learning, artificial intelligence, electronic health records, precision medicine
C7
1
50
machine learning, deep learning, internet of things, big data analytics, security
C8
1
63
industry 4.0, internet of things, cloud computing, smart manufacturing, cloud manufacturing
Table 2 图情领域关注的类团及引用
[1]
Hey T, Tansley S, Tolle K. 第四范式:数据密集型科学发现[M]. 潘教峰, 张晓林, 等译. 北京: 科学出版社, 2012.
[1]
(Hey T, Tansley S, Tolle K. The Fourth Paradigm: Data-Intensive Scientific Discovery[M]. Translated by Pan Jiaofeng, Zhang Xiaolin, et al. Beijing: Science Press, 2012.)
(Ma Feicheng, Zhang Rui, Li Zhiyuan. Impacts of Big Data on the Research of Information Science[J]. Documentation, Information & Knowledge, 2018(5): 4-9.)
(Zeng Jianxun, Wei Lai. The Changes of Information Science in Big Data Era[J]. Journal of the China Society for Scientific and Technical Information, 2015, 34(1): 37-44.)
(Hu Changping, Lv Meijiao. Development of Information Science Theory in Big Data and Intelligent Environment[J]. Information Studies: Theory & Application, 2020, 43(10): 1-6.)
(Tang Mingwei, Jiang Xun, Xu Zhenyuan, et al. System Construction of Intelligence Science Methods and Technologies in Big Data Environment[J]. Information Science, 2020, 38(5): 106-111.)
(Li Pin, Yang Jianlin. Research on the Development Path of Intelligence Studies Based on Big Data Thinking[J]. Journal of the China Society for Scientific and Technical Information, 2019, 38(3): 239-248.)
(Dong Ke, Qiu Junping. Research on the Influence of Big Data Environment on the Development of Informatics[J]. Journal of the China Society for Scientific and Technical Information, 2017, 36(9): 886-893.)
[8]
Virkus S, Garoufallou E. Data Science from a Library and Information Science Perspective[J]. Data Technologies and Applications, 2019, 53(4): 422-441.
doi: 10.1108/DTA-05-2019-0076
[9]
Virkus S, Garoufallou E. Data Science and Its Relationship to Library and Information Science: A Content Analysis[J]. Data Technologies and Applications, 2020, 54(5): 643-663.
doi: 10.1108/DTA-07-2020-0167
[10]
Ajibade P, Mutula S M. Big Data Research Outputs in the Library and Information Science: South African’s Contribution Using Bibliometric Study of Knowledge Production[J]. African Journal of Library Archives and Information Science, 2020, 30(1): 49-60.
[11]
Ahmed W, Ameen K. Defining Big Data and Measuring Its Associated Trends in the Field of Information and Library Management[J]. Library Hi Tech News, 2017, 34(9): 21-24.
(Zhao Dongxiang, Zhang Rui. Hot Topic Detection and Analysis of Big Data Research in the Field of International Library and Information Science[J]. Research on Library Science, 2018(14): 10-19.)
[13]
Zhan M, Widén G. Understanding Big Data in Librarianship[J]. Journal of Librarianship and Information Science, 2019, 51(2): 561-576.
doi: 10.1177/0961000617742451
(Wang Chunhua, Li Wei, Wen Tingxiao. The Research Hotspot of Big Data in Library and Information Science of China[J]. Documentation,Information & Knowledge, 2015(4): 82-89.)
(Zhao Rongying, Wei Xuqiu. Analysis of Evolutional Characteristics of Big Data in Library and Information Science in China[J]. Information Science, 2017, 35(5): 3-7,14.)
(Yu Qiuyu, Xu Yuequan. The Research Hotspots of Big Data in China’s Library and Information Science in Recent Five Years[J]. Research on Library Science, 2020(8): 10-18.)
(Chen Junying, Bai Rujiang, Wang Xiaoyue, et al. Research Status and Development Trend Analysis of Big Data in the Field of Chinese and Foreign Library and Information Science in Recent 10 Years (2007—2016)[J]. Information Science, 2018, 36(7): 104-110.)
[18]
Leydesdorff L, Bornmann L, Zhou P. Construction of a Pragmatic Base Line for Journal Classifications and Maps Based on Aggregated Journal-Journal Citation Relations[J]. Journal of Informetrics, 2016, 10(4): 902-918.
doi: 10.1016/j.joi.2016.07.008
[19]
Rafols I, Porter A L, Leydesdorff L. Science Overlay Maps: A New Tool for Research Policy and Library Management[J]. Journal of the American Society for Information Science and Technology, 2010, 61(9): 1871-1887.
doi: 10.1002/asi.v61:9
[20]
Traag V A, Waltman L, van Eck N J. From Louvain to Leiden: Guaranteeing Well-Connected Communities[J]. Scientific Reports, 2019, 9: 5233.
doi: 10.1038/s41598-019-41695-z
pmid: 30914743
[21]
Waltman L, van Eck N J. A Smart Local Moving Algorithm for Large-Scale Modularity-Based Community Detection[J]. The European Physical Journal B, 2013, 86(11): 471.
doi: 10.1140/epjb/e2013-40829-0
[22]
Yaqoob I, Hashem I A T, Gani A, et al. Big Data: From Beginning to Future[J]. International Journal of Information Management, 2016, 36(6): 1231-1247.
doi: 10.1016/j.ijinfomgt.2016.07.009
[23]
Yang C W, Huang Q Y, Li Z L, et al. Big Data and Cloud Computing: Innovation Opportunities and Challenges[J]. International Journal of Digital Earth, 2017, 10(1): 13-53.
doi: 10.1080/17538947.2016.1239771
[24]
Gandomi A, Haider M. Beyond the Hype: Big Data Concepts, Methods, and Analytics[J]. International Journal of Information Management, 2015, 35(2): 137-144.
doi: 10.1016/j.ijinfomgt.2014.10.007
[25]
Khan N, Yaqoob I, Hashem I A T, et al. Big Data: Survey, Technologies, Opportunities, and Challenges[J]. The Scientific World Journal, 2014: Article ID 712826.
[26]
Chen M, Mao S W, Liu Y H. Big Data: A Survey[J]. Mobile Networks and Applications, 2014, 19(2): 171-209.
doi: 10.1007/s11036-013-0489-0