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Published on: October 27, 2016
Visual perception and mixed-initiative interaction for assisted visualization design
Christopher Healey1, Sarat Kocherlakota, Vivek Rao
1Department of Computer Science, North Carolina State University, Raleigh, NC 27695-8206, USA. healey@csc.ncsu.edu
IEEE Transactions on Visualization and Computer Graphics
|January 15, 2008
Summary
This study introduces ViA, a visualization assistant that uses human vision principles and AI to find effective visualizations for complex data. It collaborates with users, improving data analysis through perceptually guided search.
Area of Science:
- Computer Science
- Human-Computer Interaction
- Data Visualization
Background:
- Effective visualization of large, multidimensional datasets is challenging.
- Existing AI search strategies may not fully leverage human perceptual capabilities.
- Integrating human vision principles can enhance visualization discovery.
Purpose of the Study:
- To develop a visualization assistant (ViA) integrating human vision and AI.
- To create a mixed-initiative system for collaborative visualization search.
- To improve the identification of perceptually salient visualizations.
Main Methods:
- Applied low-level human vision knowledge to evaluate and guide visualization search.
- Implemented a mixed-initiative strategy for user-AI collaboration.
- Tested ViA on historical weather data and simulated online auctions.
Main Results:
- ViA's search strategy outperformed exhaustive analysis, simulated annealing, and reactive tabu search.
- Mixed-initiative interaction demonstrated measurable improvements in visualization quality.
- Perceptual guidelines were effectively evaluated using intelligent agent simulations.
Conclusions:
- ViA successfully constructs high-quality visualizations for diverse real-world datasets.
- The integration of perceptual guidelines and mixed-initiative AI enhances visualization effectiveness.
- This approach offers a promising direction for intelligent data visualization tools.
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