2014/12/09 by Albert Kim, Eric Blais, Kim, Albert +8 · 1 citation
Computer Science · Mathematics · #Advanced Statistical Methods and Models #Anomaly Detection Techniques and Applications #Data Visualization and Analytics #Databases (cs.DB) #FOS: Computer and information sciences
paper · pdf · doi:10.48550/arxiv.1412.3040
openalex publication_date 2014/12/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Visualizations are frequently used as a means to understand trends and gather insights from datasets, but often take a long time to generate. In this paper, we focus on the problem of rapidly generating approximate visualizations while preserving crucial visual proper- ties of interest to analysts. Our primary focus will be on sampling algorithms that preserve the visual property of ordering; our techniques will also apply to some other visual properties. For instance, our algorithms can be used to generate an approximate visualization of a bar chart very rapidly, where the comparisons between any two bars are correct. We formally show that our sampling algorithms are generally applicable and provably optimal in theory, in that they do not take more samples than necessary to generate the visualizations with ordering guarantees. They also work well in practice, correctly ordering output groups while taking orders of magnitude fewer samples and much less time than conventional sampling schemes.