一种评价可视化效益的有界测度
A Bounded Measure for Estimating the Benefit of Visualization
摘要：
信息论可用于分析可视化过程的成本效益。然而，目前衡量效益的标准包含一个不受限制的术语，既不容易估计，也不容易直观地解释。在这项工作中，我们建议修改现有的成本效益措施，取代无界的一个有界的。我们研究了一些有界的措施，包括詹森-香农分歧和一个新的分歧措施制定这项工作的一部分。我们使用可视化分析来支持多标准比较，将搜索范围缩小到具有更好数学属性的选项。我们将这些剩余的选项应用到两个可视化案例研究中，以实例化它们在实际场景中的用途，而收集到的真实世界数据则进一步为有界度量的选择提供信息，该度量可用于估计可视化的好处。
英文摘要：
Information theory can be used to analyze the cost-benefit of visualization processes. However, the current measure of benefit contains an unbounded term that is neither easy to estimate nor intuitive to interpret. In this work, we propose to revise the existing cost-benefit measure by replacing the unbounded term with a bounded one. We examine a number of bounded measures that include the Jenson-Shannon divergence and a new divergence measure formulated as part of this work. We use visual analysis to support the multi-criteria comparison, narrowing the search down to those options with better mathematical properties. We apply those remaining options to two visualization case studies to instantiate their uses in practical scenarios, while the collected real world data further informs the selection of a bounded measure, which can be used to estimate the benefit of visualization.
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