Complex data analysis inherently seeks unexpected insights through
exploratory \re{visual analysis} methods, transcending logical, step-by-step
processing. However, \re{existing interfaces such as notebooks and dashboards
have limitations in exploration and comparison for visual data analysis}.
Addressing these limitations, we introduce a "design-like" intelligent canvas
environment integrating generative AI into data analysis, offering rapid
prototyping, iteration, and comparative visualization management. Our dual
contributions include the integration of generative AI components into a canvas
interface, and empirical findings from a user study (N=10) evaluating the
effectiveness of the canvas interface.

我们介绍了一种将生成性人工智能组件集成到画布界面的智能画布环境，通过提供快速原型设计、迭代和比较可视化管理，解决了现有界面在可视数据分析中探索和比较方面的限制。我们的研究通过一个用户研究（N=10）从实践中获得的经验结果，实证了画布界面的有效性。

智能画布：通过快速原型设计、迭代和展示，实现类似设计的探索性视觉数据分析

Intelligent Canvas: Enabling Design-Like Exploratory Visual Data  Analysis through Rapid Prototyping, Iteration and Curation

In the analysis of large/big data sets, aggregation (replacing values of a
variable over a group by a single value) is a standard way of reducing the size
(complexity) of the data. Data analysis programs provide different aggregation
functions.
Recently some books dealing with the theoretical and algorithmic background
of traditional aggregation functions were published. A problem with traditional
aggregation is that often too much information is discarded thus reducing the
precision of the obtained results. A much better, preserving more information,
summarization of original data can be achieved by representing aggregated data
using selected types of complex data.
In complex data analysis the measured values over a selected group $A$ are
aggregated into a complex object $\Sigma(A)$ and not into a single value. Most
of the aggregation functions theory does not apply directly. In our
contribution, we present an attempt to start building a theoretical background
of complex aggregation.
We introduce and discuss exactly mergeable summaries for which it holds for
merging of disjoint sets of units
\[ \Sigma(A \cup B) = F( \Sigma(A),\Sigma(B)),\qquad \mbox{ for } \quad A\cap
B = \emptyset .\]

该论文介绍了复杂数据分析中的聚合技术，提出了可确保信息完整性的可合并汇总方法，并探讨了构建理论背景的尝试。