Visual analytics systems enable highly interactive exploratory data analysis. Across a range of fields, these technologies have been successfully employed to help users learn from complex data. However, these same exploratory visualization techniques make it easy for users to discover spurious findings. This paper proposes new methods to monitor a user's analytic focus during visual analysis of structured datasets and use it to surface relevant articles that contextualize the visualized findings. Motivated by interactive analyses of electronic health data, this paper introduces a formal model of analytic focus, a computational approach to dynamically update the focus model at the time of user interaction, and a prototype application that leverages this model to surface relevant medical publications to users during visual analysis of a large corpus of medical records. Evaluation results with 24 users show that the modeling approach has high levels of accuracy and is able to surface highly relevant medical abstracts.
翻译:视觉分析系统能够进行高度互动的探索性数据分析。 在一系列领域,这些技术被成功地用于帮助用户从复杂的数据中学习。然而,这些探索性可视化技术使用户很容易发现虚假的调查结果。本文提出了在对结构化数据集进行视觉分析时监测用户分析重点的新方法,并用于将可视化结果背景化的表面相关文章。在对电子健康数据进行互动分析的推动下,本文引入了一种正式的分析重点模型,一种在用户互动时动态更新焦点模型的计算方法,以及一种在对大量医疗记录进行视觉分析时将这一模型用于向用户展示相关医学出版物的原型应用。与24个用户进行的评价结果表明,模型方法具有很高的准确性,并且能够展示高度相关的医学摘要。