Ptolemy: A Semantic Map of Exploratory Data Analysis

2026-09-14

AI Summary

该研究通过构建一个名为Ptolemy的语义地图来解决探索性数据分析过程中难以追踪已分析内容的问题，利用结构化描述生成嵌入式位置表示每个分析步骤，以提高全局定位和局部比较能力。

Abstract

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.

Problem

• Exploratory Data Analysis
• Analysis History
• Semantic Map

Innovation

• Semantic Map
• Exploratory Data Analysis
• Analysis History
• Embeddings
• Data View