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纸质出版日期： 2023-06-16 ，
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陶钧， 张宇， 陈晴， 刘灿， 陈思明， 袁晓如. 2023. 智能可视化与可视分析. 中国图象图形学报， 28(06):1909-1926
Tao Jun， Zhang Yu， Chen Qing， Liu Can， Chen Siming， Yuan Xiaoru. 2023. Intelligent visualization and visual analytics. Journal of Image and Graphics， 28(06):1909-1926
可视化与可视分析已成为众多领域中结合人类智能与机器智能协同理解、分析数据的常见手段。人工智能可以通过对大数据的学习分析提高数据质量，捕捉关键信息，并选取最有效的视觉呈现方式，从而使用户更快、更准确、更全面地从可视化中理解数据。利用人工智能方法，交互式可视化系统也能更好地学习用户习惯及用户意图，推荐符合用户需求的可视化形式、交互操作和数据特征，从而降低用户探索的学习及时间成本，提高交互分析的效率。人工智能方法在可视化中的应用受到了极大关注，产生了大量学术成果。本文从最新工作出发，探讨人工智能在可视化流程的关键步骤中的作用。包括如何智能地表示和管理数据、如何辅助用户快速创建和定制可视化、如何通过人工智能扩展交互手段及提高交互效率、如何借助人工智能辅助数据的交互分析等。具体而言，本文详细梳理每个步骤中需要完成的任务及解决思路，介绍相应的人工智能方法（如深度网络结构），并以图表数据为例介绍智能可视化与可视分析的应用，最后讨论智能可视化方法的发展趋势，展望未来的研究方向及应用场景。
Visualization and visual analysis have become an key instrument in analyzing and understanding data in the era of big data. Visualization maps data to the visual channel through visual encoding， allowing users to quickly access multi-dimensional information from large amounts of data through the human perception， while visual analysis builds an interaction loop between data and users based on the visual representation， and promotes the user’s reasoning on complex data analysis through interactive visual interfaces. However， as the current data scale continues to grow and its structure becomes increasingly complex， the rich information exceeds the expression ability of the screen space and the processing ability of human visual perception. In this context， straightforward visual encoding cannot effectively convey all the information in data any longer. In addition， interactive exploration is challenged of large-scale complex data as well. It is unfeasible for users to determine the exploration direction based on experience or simple observation of data. Users may fall into time-consuming trial-and-error attempts without uncovering the insights hidden in the data. Therefore， it becomes natural to shift the effort from users to artificial intelligence. The most recent visualization and visual analytic approaches often rely on artificial intelligence （AI） methods to analyze， understand， and summarize data， extract key structures and relationships from data， simplify visualization content， optimize information transmission in visual performance forms， and provide guidance and direction for interactive exploration. With the advancement of AI methods （such as deep learning）， machine intelligence is constantly increasing its ability to fit， analyze， and reason about complex data and transformations. This empowers artificial intelligence to overcome the heterogeneity across multi-modal data， such as the data elements， user intention， and visual representation. It further enables AI to establish complex connections among these heterogeneous factors involved in visual exploration. Accordingly， leveraging the power of AI to enhance visualization and visual analysis systems has attracted significant research attention from the visualization community in the past several years. This research direction inspires a wide diversity of research works. Some might focus on improving the performance of traditional computation tasks in visualization with AI methods， while other works even expands the boundaries of visualization methods and explores new research opportunities. For example， in visualization creation， by learning to accurately match data features and user intentions， the AI-powered approaches can automatically create visualizations showing the key information of users’ interest. These approaches effectively reduce the requirements of professional visualization skills and relieve the burden on users in manual operations. For scientific visualization， by observing a large number of simulation members， the AI-enabled techniques can quickly generate renderings of different simulation parameters and visualization parameters in interaction without the need for time-consuming simulations or complex rendering. For interactive exploration， by incorporating various kinds of interaction means， machine learning-based methods can reduce the learning and usage cost for users to use interactive systems， and， therefore attract a broader range of users to experience visualization systems. For visual analysis， by learning user behaviors and analyzing data， the intelligent approaches can suggest interaction operations during exploration， reducing trial-and-error costs and improving analysis efficiency. The literature review briefly introduces the AI-driven visualization researches， summarizes these approaches by categories， and discuss their applications and development. The survey covers four key tasks in visualization： data-oriented visualization management， visualization creation， interactive exploration， and visual analysis. Data management focuses on how to represent and manage large-scale integrated data to support subsequent high-precision rendering. Visualization creation focuses on how to map data to informative visual representations. Interactive exploration discusses how to enrich the interaction means during visual explorations. Visual analysis emphasizes how to combine visualization and interaction to facilitate intuitive and efficient data analysis. These four key tasks cover the entire visualization process from data to visual presentation and then to the human cognition. The survey further discusses the application of intelligent visualization and visual analysis using chart data as an example. Finally， the paper will discuss the development trend of intelligent visualization and visual analysis and point out the potential research directions in future. In terms of artificial intelligence methods， this survey focuses on the application of the new-generation of intelligence methods， with deep learning being one of the most prominent representative， in the field of visualization. It does not elaborate on the use traditional learning methods， such as linear optimization and cluster analysis， etc. Intelligent visualization becomes central to the entire discipline of visualization with many potential applications. Several key trends can be observed in its development. For data representation， the trend is to move from structured data on regular grids towards a more flexible and effective space using deep representation， commonly through networks based on multi-layer perceptrons. The improved computation space can open up possibilities of further researches， such as 1） generalizing the neural network to handle multiple variables， 2） multiple simulations （ensembles）， and 3） multiple tasks. For intelligent creation of visualization， applications of AI include using natural language generation algorithms to enhance the connection between users and intelligent tools， using data-driven models to strengthen these tools， and making these tools more effective at recognizing and predicting the user’s design intent. For intelligent interaction， the research focus shifts to developing machine learning methods that enhance visualization expression and information transformation， moving away from rule-based processes with limited scalability and extensibility. For visual analysis， the trend moves towards using deep learning for content and interaction recommendation， interactive update to visual analysis models， and interpretation of user interactions. Overall， these trends demonstrate the potential for intelligent visualization to significantly improve the efficiency and accuracy of data analysis and communication.
可视化可视分析机器学习深度学习前沿报告
visualizationvisual analyticsmachine learningdeep learningfrontier report
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