Visual Exploration of Hierarchical Data Using Degree-of-Interest Controlled by Eye-Tracking

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Abstract

Effective visual exploration of large data sets is an important problem. A standard technique for mapping large data sets is to use hierarchical data representations (trees, or dendrograms) that users may navigate. If the data sets get large, so do the hierarchies, and effective methods for the navigation are required. Traditionally, users navigate visual representations using desktop interaction modalities, including mouse interaction.
Motivated by recent availability of low-cost eye-tracker systems, we investigate application possibilities to use eye-tracking for controlling the visual-interactive data exploration process. We implemented a proof-of-concept system for visual exploration of hierarchic data, exemplified by scatter plot diagrams which are to be explored for grouping and similarity relationships. The exploration includes usage of degree-of-interest based distortion controlled by user attention read from eye-movement behavior. We present the basic elements of our system, and give an illustrative use case discussion, outlining the application possibilities. We also identify interesting future developments based on the given data views and captured eye-tracking information.
Original languageEnglish
Publication statusPublished - 25 Nov 2016

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Fields of Expertise

  • Information, Communication & Computing

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