Visual perception and mixed-initiative interaction for assisted visualization design.
This paper describes the integration of perceptual guidelines from human vision with an AI-based mixed-initiative search strategy. The result is a visualization assistant called ViA, a system that collaborates with its users to identify perceptually salient visualizations for large, multidimensional datasets. ViA applies knowledge of low-level human vision to: (1) evaluate the effectiveness of a particular visualization for a given dataset and analysis tasks; and (2) rapidly direct its search towards new visualizations that are most likely to offer improvements over those seen to date. Context, domain expertise, and a high-level understanding of a dataset are critical to identifying effective visualizations. We apply a mixed-initiative strategy that allows ViA and its users to share their different strengths and continually improve ViA's understanding of a user's preferences. We visualize historical weather conditions to compare ViA's search strategy to exhaustive analysis, simulated annealing, and reactive tabu search, and to measure the improvement provided by mixed-initiative interaction. We also visualize intelligent agents competing in a simulated online auction to evaluate ViA's perceptual guidelines. Results from each study are positive, suggesting that ViA can construct high-quality visualizations for a range of real-world datasets.
Healey
Christopher
C
Department of Computer Science, North Carolina State University, Raleigh, NC 27695-8206, USA. healey@csc.ncsu.edu
Kocherlakota
Sarat
S
Rao
Vivek
V
Mehta
Reshma
R
St Amant
Robert
R
D000465:Algorithms; D001185:Artificial Intelligence; D003196:Computer Graphics; D006801:Humans; D007091:Image Processing, Computer-Assisted; D014584:User-Computer Interface; D014796:Visual Perception