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Frontiers of Computer Science

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Front. Comput. Sci.    2023, Vol. 17 Issue (6) : 176709    https://doi.org/10.1007/s11704-023-2691-y
Excellent Young Computer Scientists Forum
VIS+AI: integrating visualization with artificial intelligence for efficient data analysis
Xumeng WANG1, Ziliang WU2, Wenqi HUANG3, Yating WEI2, Zhaosong HUANG4, Mingliang XU5,6,7, Wei CHEN2,8()
1. College of Computer Science, Nankai University, Tianjin 300381, China
2. State Key Lab of CAD&CG, Zhejiang University, Hangzhou 310058, China
3. Digital Grid Research Institute, China Southern Power Grid, Guangzhou 510670, China
4. Huawei Technologies Co., Ltd., Hangzhou 310052, China
5. School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China
6. Engineering Research Center of Ministry of Education on Intelligent Swarm Systems, Zhengzhou University, Zhengzhou 450001, China
7. National Supercomputing Center in Zhengzhou, Zhengzhou 450001, China
8. Laboratory of Art and Archaeology Image, Zhejiang University, Hangzhou 310058, China
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Abstract

Visualization and artificial intelligence (AI) are well-applied approaches to data analysis. On one hand, visualization can facilitate humans in data understanding through intuitive visual representation and interactive exploration. On the other hand, AI is able to learn from data and implement bulky tasks for humans. In complex data analysis scenarios, like epidemic traceability and city planning, humans need to understand large-scale data and make decisions, which requires complementing the strengths of both visualization and AI. Existing studies have introduced AI-assisted visualization as AI4VIS and visualization-assisted AI as VIS4AI. However, how can AI and visualization complement each other and be integrated into data analysis processes are still missing. In this paper, we define three integration levels of visualization and AI. The highest integration level is described as the framework of VIS+AI, which allows AI to learn human intelligence from interactions and communicate with humans through visual interfaces. We also summarize future directions of VIS+AI to inspire related studies.
Keywords visualization      artificial intelligence      data analysis      knowledge generation     
Corresponding Author(s): Wei CHEN   
Just Accepted Date: 04 April 2023   Issue Date: 06 June 2023
 Cite this article:   
Xumeng WANG,Ziliang WU,Wenqi HUANG, et al. VIS+AI: integrating visualization with artificial intelligence for efficient data analysis[J]. Front. Comput. Sci., 2023, 17(6): 176709.
 URL:  
https://academic.hep.com.cn/fcs/EN/10.1007/s11704-023-2691-y
https://academic.hep.com.cn/fcs/EN/Y2023/V17/I6/176709
Fig.1  Three levels of integration of VIS and AI in data analysis
Fig.2  The knowledge generation model proposed by Sacha et al. [17]
Fig.3  The framework of VIS+AI consists of three loops: an interaction loop that supports direct communication between AI and humans using visual analysis systems, an execution loop running models/algorithms to perform tasks, and an intelligence optimization loop restoring what is learned previously
Category Tool Assistance provision (× or √) User accessibility (×, ○ or √)
Finding Action Insight Instruction Feedback Learning
AI4VIS Power BI √ √ √ ○ ○ ×
Qlik √ √ √ ○ ○ ×
IBM Cognos Analytics √ √ √ ○ ○ ×
Tableau √ √ √ ○ ○ ×
Outlier AI √ √ √ ○ ○ ×
Sisense √ √ √ ○ ○ ×
Domo √ × √ ○ ○ ×
SAP Business Technology Platform √ √ √ ○ ○ ×
Datapine √ √ √ ○ ○ ×
SAS Visual Analytics √ √ √ ○ ○ ×
TIBCO Spotfire √ × √ ○ ○ ×
Voyager2 √ √ × ○ ○ ×
VIS4AI AnyLogic √ × × ○ ○ ○
neptune.ai × × × ○ ○ ○
TensorBoard × × × ○ ○ ○
MindSpore × × × ○ ○ ○
Tab.1  Existing tools attempting to combine VIS and AI. √ = fully supporting the function, ○ = partially supporting the function, × = missing the function
  
  
  
  
  
  
  
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