Information Visualization from the Perspective of Big Data Analysis and Fusion | Zendy

Xiang Lin | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Information Visualization from the Perspective of Big Data Analysis and Fusion

Author(s) -

Xiang Lin

Publication year - 2021

Publication title -

scientific programming

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.269

H-Index - 36

eISSN - 1875-919X

pISSN - 1058-9244

DOI - 10.1155/2021/8934632

Subject(s) - visualization , computer science , big data , construct (python library) , perspective (graphical) , data mining , data visualization , network topology , graph drawing , information visualization , complex network , theoretical computer science , data science , world wide web , artificial intelligence , computer network

In the big data environment, the visualization technique has been increasingly adopted to mine the data on library and information (L&I), with the diversification of data sources and the growth of data volume. The previous research into the information association of L&I visualization network rarely tries to construct such a network or explore the information association of the network. To overcome these defects, this paper explores the visualization of L&I from the perspective of big data analysis and fusion. Firstly, the authors analyzed the topology of the L&I visualization network and calculated the metrics for the construction of L&I visualization topology map. Next, the importance of meta-paths of the L&I visualization network was calculated. Finally, a complex big data L&I visualization network was established, and the associations between information nodes were analyzed in detail. Experimental results verify the effectiveness of the proposed algorithm.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research