VIS+AI: integrating visualization with artificial intelligence for efficient data analysis

被引:0
作者
WANG Xumeng [1 ]
WU Ziliang [2 ]
HUANG Wenqi [3 ]
WEI Yating [2 ]
HUANG Zhaosong [4 ]
XU Mingliang [5 ,6 ,7 ]
CHEN Wei [2 ,8 ]
机构
[1] College of Computer Science, Nankai University, Tianjin , China
[2] State Key Lab of CAD&CG, Zhejiang University, Hangzhou , China
[3] Digital Grid Research Institute, China Southern Power Grid, Guangzhou , China
[4] Huawei Technologies Co, Ltd, Hangzhou , China
[5] School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou , China
[6] Engineering Research Center of Ministry of Education on Intelligent Swarm Systems, Zhengzhou University, Zhengzhou , China
[7] National Supercomputing Center in Zhengzhou, Zhengzhou , China
[8] Laboratory of Art and Archaeology Image, Zhejiang University, Hangzhou ,
关键词
visualization; artificial intelligence; data analysis; knowledge generation;
D O I
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中图分类号
学科分类号
摘要
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.
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