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Interactive visualizations are crucial in ad hoc data exploration andanalysis. However, with the growing number of massive datasets, generatingvisualizations in interactive timescales is increasingly challenging. Oneapproach for improving the speed of the visualization tool is via datareduction in order to reduce the computational overhead, but at a potentialcost in visualization accuracy. Common data reduction techniques, such asuniform and stratified sampling, do not exploit the fact that the sampledtuples will be transformed into a visualization for human consumption.  We propose a visualization-aware sampling (VAS) that guarantees high qualityvisualizations with a small subset of the entire dataset. We validate ourmethod when applied to scatter and map plots for three common visualizationgoals: regression, density estimation, and clustering. The key to our samplingmethod's success is in choosing tuples which minimize a visualization-inspiredloss function. Our user study confirms that optimizing this loss functioncorrelates strongly with user success in using the resulting visualizations. Wealso show the NP-hardness of our optimization problem and propose an efficientapproximation algorithm. Our experiments show that, compared to previousmethods, (i) using the same sample size, VAS improves user's success by up to35% in various visualization tasks, and (ii) VAS can achieve a requiredvisualization quality up to 400 times faster.

Visualization-Aware Sampling for Very Large Databases