Analysis and Implementation of Multidimensional Data Visualization Methods in Large-Scale Power Internet of Things

  • Zhoubin Liu
  • Zixiang Wang
  • Boyang Wei
  • Xiaolu YuanEmail author
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 303)


In the large-scale power Internet of things, a large amount of data is generated due to its diversity. Data visualization technology is very important for people to capture the mathematical characteristics, rules and knowledge of data. People tend to get limited and less valuable information directly form large data when rely only on human-being’s cognition. Therefore, people need new means and technologies to help display these data more intuitively and effectively. Data visualization mainly aims at conveying and communicating information clearly and effectively in term of graphical display, which can make data more human-readable and intuitive. Multidimensional data visualization refers to the methods to project multidimensional data to two-dimensional plane. It has important applications in exploratory data analysis, and verification of clustering or classification problems. This paper mainly studies the data visualization algorithm and technology in large-scale power Internet of things. Specifically, the traditional Radviz algorithm is selected and improved. The improved radviz-t algorithm is designed and implemented, and the unknown information of data transmission is obtained by analyzing its visualization effect. Finally, the methods used to study fault detection ability of radviz-t algorithm are discussed in detail.


Data visualization Radviz algorithm Power Internet of Things 



This work was supported by State Grid Zhejiang Electric Power Corporation Technology Project (Grant No. 5211DS16001R).


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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  • Zhoubin Liu
    • 1
  • Zixiang Wang
    • 1
  • Boyang Wei
    • 2
  • Xiaolu Yuan
    • 3
    Email author
  1. 1.State Grid Zhejiang Electric Power Research InstituteHangzhouChina
  2. 2.Georgetown UniversityWashington DCUSA
  3. 3.RUN CorporationWuxiChina

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