AI 中文总结
Ptolemy通过将分析历史外部化为语义地图，帮助分析师跟踪已探索空间并定位空白，改善了全局定向和局部比较，同时揭示了定向与可操作性之间的权衡。
AI 中文摘要
探索性数据分析（EDA）中的一个核心挑战是跟踪已经检查过的内容，以便决定接下来分析什么。在实践中，分析师在建立对数据集的理解时，通常会运行数十次分析。然而，大多数工具很少支持维护这一演变过程的概览，而是仅展示分析步骤的线性历史。这些工具显示顺序，即之前发生了什么，但不显示位置，即当前分析与更广泛的可能分析空间的关系。因此，分析师必须在大脑中重建他们已经探索了空间的哪些部分以及哪些地方存在空白，这增加了冗余工作或遗漏模式的风险。我们提出了Ptolemy，一种导航界面，将分析历史外部化为语义地图。每个分析步骤被表示为一个点，其位置由从其有效数据视图（例如，列、过滤器、转换）的结构化描述中导出的嵌入来确定，从而使空间距离反映分析相似性。在一项比较地图、画布和树表示的混合方法研究中，我们发现地图改善了全局定向和局部比较，而有序布局降低了决策成本。这些发现揭示了定向与可操作性之间的权衡，并突出了在EDA中支持策略性探索的设计原则。
英文摘要
A central challenge in exploratory data analysis (EDA) is keeping track of what has already been examined in order to decide what to analyze next. In practice, analysts often run dozens of analyses while building an understanding of a dataset. However, most tools provide little support for maintaining an overview of this evolving process, instead exposing only a linear history of analysis steps. These tools show sequence, what came before, but not position, how a current analysis relates to the broader space of possible analyses. As a result, analysts must mentally reconstruct which parts of the space they have explored and where gaps remain, increasing the risk of redundant work or overlooked patterns. We present Ptolemy, a navigational interface that externalizes analysis history as a semantic map. Each analytic step is represented as a point positioned by embeddings derived from a structured description of its effective data view (e.g., columns, filters, transformations), allowing spatial distance to reflect analytic similarity. In a mixed-methods study comparing map, canvas, and tree representations, we find that maps improve global orientation and local comparison, while ordered layouts reduce decision cost. These findings surface a trade-off between orientation and actionability, and highlight design principles for supporting strategic exploration in EDA.
