Rapid Sampling for Visualizations with Ordering Guarantees.
Rapid Sampling for Visualizations with Ordering Guarantees.
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通过订购保证的可视化快速采样。
DOI：
10.14778/2735479.2735485
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发表时间：
2015-01
期刊：
影响因子：
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通讯作者：
Rubinfeld R
中科院分区：
文献类型：
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作者：
Kim A;Blais E;Parameswaran A;Indyk P;Madden S;Rubinfeld R
可视化经常被用作了解趋势和从数据集中收集见解的一种手段，但通常需要很长时间才能生成。在本文中，我们专注于快速生成近似可视化的问题，同时保留分析师感兴趣的重要视觉特性。我们的主要重点将是采样算法，保持有序的视觉属性，我们的技术也将适用于其他一些视觉属性。例如，我们的算法可用于非常快速地生成条形图的近似可视化，其中任何两个条形图之间的比较都是正确的。我们正式表明，我们的采样算法是普遍适用的，并证明在理论上是最优的，因为他们不需要更多的样本比必要的生成的可视化与排序保证。它们在实践中也能很好地工作，正确地对输出组进行排序，同时比传统的采样方案采取数量级更少的样本和更少的时间。
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 properties 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.