AxiSketcher: Interactive Nonlinear Axis Mapping of Visualizations through User Drawings

Bum Chul Kwon, Hannah Kim, Emily Wall, Jaegul Choo, Haesun Park, Alex Endert

Research output: Contribution to journalArticle

25 Citations (Scopus)

Abstract

Visual analytics techniques help users explore high-dimensional data. However, it is often challenging for users to express their domain knowledge in order to steer the underlying data model, especially when they have little attribute-level knowledge. Furthermore, users' complex, high-level domain knowledge, compared to low-level attributes, posits even greater challenges. To overcome these challenges, we introduce a technique to interpret a user's drawings with an interactive, nonlinear axis mapping approach called AxiSketcher. This technique enables users to impose their domain knowledge on a visualization by allowing interaction with data entries rather than with data attributes. The proposed interaction is performed through directly sketching lines over the visualization. Using this technique, users can draw lines over selected data points, and the system forms the axes that represent a nonlinear, weighted combination of multidimensional attributes. In this paper, we describe our techniques in three areas: 1) the design space of sketching methods for eliciting users' nonlinear domain knowledge; 2) the underlying model that translates users' input, extracts patterns behind the selected data points, and results in nonlinear axes reflecting users' complex intent; and 3) the interactive visualization for viewing, assessing, and reconstructing the newly formed, nonlinear axes.

Original languageEnglish
Article number7534876
Pages (from-to)221-230
Number of pages10
JournalIEEE Transactions on Visualization and Computer Graphics
Volume23
Issue number1
DOIs
Publication statusPublished - 2017 Jan 1

Keywords

  • axis mapping
  • axis visualization
  • human-centered visual analytics
  • interactive model steering
  • sketch

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

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