New Paper to Appear at ACM CHI Examines Analytic Focus During Visual Analysis

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.

Zhilan Zhou will present the VACLab’s work on this topic at the 2021 CHI Conference. In the paper, Zhou et al. describe their work developing new general techniques to capture and present a user’s analytic focus during visual analysis and leverage it to surface relevant literature that contextualize the visualized findings. The paper presents a prototype in an existing visual analytics platform for structured health data. The evaluation of the prototype implementation shows the feasibility of modeling users’ focus during exploratory visual analysis and leveraging it to broaden the exploration into other relevant data sources.

More details and a PDF of the paper can be found on the article’s page on this website.

Reference

Zhilan Zhou, Ximing Wen, Yue Wang, David Gotz. Modeling and Leveraging Analytic Focus During Exploratory Visual Analysis. ACM CHI Conference on Human Factors in Computing Systems (2021).