Exploratory data analysis (EDA) is often hindered by cold-start friction; when users lack specific analytic goals, they struggle to configure complex visualization parameters. While existing visualization tools mostly rely on explicit user input to frame data, we propose leveraging the physical environment as an implicit framing mechanism. We introduce a conceptual framework that uses the geometric and spatial properties of physical containers in Augmented Reality (AR) to guide data interpretation. We characterize how container attributes, such as number of faces, size, proportion, and shape, give rise to distinct perceptual tendencies. For example, a circular container may encourage cyclic interpretation, while juxtaposed planar faces may facilitate comparative analysis. By treating physical forms as environmental framing conditions, we show how AR can orient a user's attention and structure their exploration without requiring manual encoding or prescribing fixed conclusions. We demonstrate this framework through a series of AR design examples illustrating how container morphology foregrounds cyclic, comparative, and sequential analytic patterns.

翻译：探索性数据分析通常受冷启动摩擦的阻碍：当用户缺乏具体的分析目标时，他们难以配置复杂的可视化参数。虽然现有可视化工具大多依赖显式用户输入来框定数据，我们提出利用物理环境作为隐式框定机制。我们引入了一个概念框架，利用增强现实中物理容器的几何与空间属性来引导数据解读。我们描述了容器属性（如面数、尺寸、比例与形状）如何激发不同的感知倾向。例如，圆形容器可能促进循环式解读，而并置的平面面有助于比较分析。通过将物理形态视为环境框定条件，我们展示了增强现实如何无需手动编码或预设固定结论即可引导用户注意力并结构化其探索过程。我们通过一系列增强现实设计示例演示该框架，阐明容器形态如何凸显循环、比较与序列分析模式。