三元空间大数据跨域可视化分析展望

陈为; 张天野; 朱海洋; 王叙萌; 汪云海

doi:10.1631/FITEE.2100553

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三元空间大数据跨域可视化分析展望

常规文章 | Updated：2022-05-20

- 三元空间大数据跨域可视化分析展望
  Enhanced Publication
- Perspectives on cross-domain visual analysis of cyber-physical-social big data
- 信息与电子工程前沿（英文） 2021年22卷第12期页码：1559-1564
- Affiliations：
  
  1.The State Key Lab of CAD & CG, Zhejiang University, Hangzhou310058, China
  2.School of Computer Science and Technology, Shandong University, Jinan250100, China
- Author bio：
  
  ‡Corresponding author
- Funds：
  
  National Natural Science Foundation of China(62132017)
- DOI：10.1631/FITEE.2100553
  中图分类号：
- 收稿：2021-11-29，
  
  修回：2021-12-10，
  
  录用：2021-12-10，
  
  纸质出版：2021-12-0
- Accepted：
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陈为, 张天野, 朱海洋, 等. 三元空间大数据跨域可视化分析展望[J]. 信息与电子工程前沿（英文）, 2021,22(12):1559-1564.

Wei CHEN, Tianye ZHANG, Haiyang ZHU, et al. Perspectives on cross-domain visual analysis of cyber-physical-social big data[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(12): 1559-1564.
陈为, 张天野, 朱海洋, 等. 三元空间大数据跨域可视化分析展望[J]. 信息与电子工程前沿（英文）, 2021,22(12):1559-1564. DOI： 10.1631/FITEE.2100553.

Wei CHEN, Tianye ZHANG, Haiyang ZHU, et al. Perspectives on cross-domain visual analysis of cyber-physical-social big data[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(12): 1559-1564. DOI： 10.1631/FITEE.2100553.

摘要

三元空间大数据一般定义为由其定义领域（包括数据、对象、任务、应用场景、主体等）所有元素组成的集合。可视分析是一种新兴的人在回路大数据分析范式，可利用人类感知提高人类认知效率。本文探讨三元空间大数据跨域可视化分析，强调三元空间大数据跨域性带来的新挑战——数据、主题和任务域，并提出一个新的可视分析模型和一套方法来应对这些挑战。

Abstract

The domain of cyber-physical-social (CPS) big data is generally defined as the set consisting of all the elements in its defined domain

including domains of data

objects

tasks

application scenarios

and subjects. Visual analytics is an emerging human-in-the-loop big data analytics paradigm that can exploit human perception to enhance human cognitive efficiency. In this paper

we explore the perspectives on cross-domain visual analysis of CPS big data. We also highlight new challenges brought by the cross-domain nature of CPS big data—data

subject

and task domains—and propose a novel visual analytics model and a suite of approaches to address these challenges.

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references

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