How Does User Behavior Evolve During Exploratory Visual Analysis?

TL;DR

This paper investigates how user behavior changes during exploratory visual analysis, highlighting the dynamic nature of user interactions and the need for models that adapt to evolving exploration strategies.

Contribution

It introduces an empirical analysis of user behavior evolution in EVA, challenging static models and suggesting dynamic approaches for better user modeling.

Findings

User interactions evolve as users learn about data

Static models do not capture the dynamic nature of exploration

Implications for designing adaptive visualization tools

Abstract

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.