论文标题
估计可视化益处的有限度量
A Bounded Measure for Estimating the Benefit of Visualization
论文作者
论文摘要
信息理论可用于分析可视化过程的成本效益。但是，当前的福利量度包含一个无限的术语，该术语既不容易估计，也不直观地解释。在这项工作中，我们建议通过用有限的术语替换无限的术语来修改现有的成本效益措施。我们检查了许多有限的措施，其中包括Jenson Shannon Divergence和作为这项工作的一部分制定的新的分歧度量。我们使用视觉分析来支持多标准比较，将搜索缩小到具有更好的数学属性的选项。我们将剩余的选项应用于两个可视化案例研究，以在实际情况下实例化其用途，而收集到的现实世界数据进一步为有限度量的选择提供了信息，可用于估计可视化的好处。
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.
