Exploratory visual analysis (EVA) is an essential stage of the data science pipeline, where users often lack clear analysis goals at the start and iteratively refine them as they learn more about their data. Accurate models of users' exploration behavior are becoming increasingly vital to developing responsive and personalized tools for exploratory visual analysis. Yet we observe a discrepancy between the static view of human exploration behavior adopted by many computational models versus the dynamic nature of EVA. In this paper, we explore potential parallels between the evolution of users' interactions with visualization tools during data exploration and assumptions made in popular online learning techniques. Through a series of empirical analyses, we seek to answer the question: how might users' exploration behavior evolve in response to what they have learned from the data during EVA? We present our findings and discuss their implications for the future of user modeling for system design.

翻译：探索性可视化分析（EVA）是数据科学流程中的关键阶段，用户在此过程中往往初始时缺乏明确的分析目标，并随着对数据的深入了解逐步迭代优化目标。精准的用户探索行为模型对于开发响应式、个性化的探索性可视化分析工具日益重要。然而，我们观察到当前许多计算模型采用的静态人类探索行为视角与EVA的动态本质存在差异。本文探索用户与可视化工具交互过程中的演化模式与主流在线学习技术假设之间的潜在关联。通过一系列实证分析，我们试图回答以下问题：在EVA过程中，用户的探索行为如何基于对数据的认知反馈而演化？我们呈现研究发现，并讨论其对未来系统设计中用户建模的启示意义。