Interactive visualizations are crucial in ad hoc data exploration and
analysis. However, with the growing number of massive datasets, generating
visualizations in interactive timescales is increasingly challenging. One
approach for improving the speed of the visualization tool is via data
reduction in order to reduce the computational overhead, but at a potential
cost in visualization accuracy. Common data reduction techniques, such as
uniform and stratified sampling, do not exploit the fact that the sampled
tuples will be transformed into a visualization for human consumption.
We propose a visualization-aware sampling (VAS) that guarantees high quality
visualizations with a small subset of the entire dataset. We validate our
method when applied to scatter and map plots for three common visualization
goals: regression, density estimation, and clustering. The key to our sampling
method's success is in choosing tuples which minimize a visualization-inspired
loss function. Our user study confirms that optimizing this loss function
correlates strongly with user success in using the resulting visualizations. We
also show the NP-hardness of our optimization problem and propose an efficient
approximation algorithm. Our experiments show that, compared to previous
methods, (i) using the same sample size, VAS improves user's success by up to
35% in various visualization tasks, and (ii) VAS can achieve a required
visualization quality up to 400 times faster.

提出了可视化感知采样（VAS）方法，通过优化可视化启发式损失函数，从而在保证可视化质量的基础上，实现对大规模数据集的快速采样。实验表明，VAS 相比现有的方法具有更高的成功率和更快的速度，尤其在回归、密度估计和聚类等场景下。