Visualization and artificial intelligence (AI) are well-applied approaches to data analysis. 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. However, how can AI and visualization complement each other and be integrated into data analysis processes are still missing.
To solve the problems, a research team led by Prof. Wei Ch en published their new research in Frontiers of Computer Science co-published by Higher Education Press and Springer Nature.
The team define three integration levels of visualization and AI. Visualization and AI are first used separately, which are data analysis approaches at level 0: independent process. As the technology matures, visualization and AI have been applied to assist each other. Related approaches are known as VIS4AI and AI4VIS, which correspond to level 1: one-way assistance. One-way assistance cannot support feedback. Approaches at level 1 have no chance to assess or optimize the effect of the provided assistance. To further improve data analysis approaches, the next level requires dual-way assistance, which is level 2: deep integration.
VIS+AI aims at barrier-free communication between human intelligence and artificial intelligence in the scenario of visual analysis. The framework of VIS+AI can completely open up the channel between AI and visualization, which further links human intelligence. As shown on the left of the framework, the knowledge generation model is inherited from the previous level to inject human intelligence. As shown on the right of the framework, the channel between AI and visualization consists of three iterative loops: an interaction loop, an execution loop, and an intelligence optimization loop. Through the three loops, AI can adapt to dynamic data analysis processes, and therefore be deeply involved into the data analysis processes guided by humans.
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Research Article, Published: 06 June 2023
Xumeng WANG, Ziliang WU, Wenqi HUANG, Yating WEI, Zhaosong HUANG, Mingliang XU, Wei CHEN. VIS+AI: integrating visualization with artificial intelligence for efficient data analysis. Front. Comput. Sci., 2023, 17(6): 176709, https://doi.org/10.1007/s11704-023-2691-y
About Frontiers of Computer Science (FCS)
FCS was launched in 2007. It is published bimonthly both online and in print by HEP and Springer. Prof. Zhi-Hua Zhou from Nanjing University serves as the Editor-in-Chief. It aims to provide a forum for the publication of peer-reviewed papers to promote rapid communication and exchange between computer scientists. FCS covers all major branches of computer science, including: architecture, software, artificial intelligence, theoretical computer science, networks and communication, information systems, multimedia and graphics, information security, interdisciplinary, etc. The readers may be interested in the special columns "Perspective" and "Excellent Young Scholars Forum".
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VIS+AI: integrating visualization with artificial intelligence for efficient data analysis
6-Jun-2023