Perception-Augmented Databases for Effective Visual Analytics
Zafeiria Moumoulidou, Hamza Elhamdadi, Redwan Ahmed Rizvee, Ke Yang, Subrata Mitra, Cindy Xiong Bearfield, Alexandra Meliou
Analysts often use sampling in visual exploration to overcome the computational and perceptual bottlenecks of working with massive datasets. Yet, existing sampling methods optimize for statistical or geometric data properties, without explicit consideration for how humans perceive a visualization. We propose perception-augmented databases: a database framework that stores perceptual models of data as precomputed metadata and exposes a new query operator---perception-aware sampling---that selects subsets optimized for how humans visually perceive the result. We make the following contributions: (1) We introduce perception-augmented databases and design PAwS, a perception-aware sampling operator for scatterplot visualizations that leverages saliency maps---a computer vision tool for predicting areas of visual attention---combined with density and coverage objectives. (2) We design ApproPAwS: a fast approximate visualization method that works directly over a perception-aware compressed synopsis of the data, achieving up to 100x speed-ups with minimal loss in visual fidelity, and without accessing the underlying data at query time. (3) We contribute a perceptual similarity metric that evaluates the quality of a sample by comparing its saliency map to that of the original dataset, a novel evaluation methodology, applicable to any sampling method for scatterplots. (4) Our extensive experimental evaluation over datasets up to 3.5M points demonstrates that our methods consistently outperform five state-of-the-art sampling algorithms. A user study confirms that PAwS samples are preferred by humans, validating our quantitative findings.
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