Storyline visualizations are a popular way of visualizing characters and their interactions over time: Characters are drawn as x-monotone curves and interactions are visualized through close proximity of the corresponding character curves in a vertical strip. Existing methods to generate storylines assume a total ordering of the interactions, although real-world data often do not contain such a total order. Instead, multiple interactions are often grouped into coarser time intervals such as years. We exploit this grouping property by introducing a new model called storylines with time intervals and present two methods to minimize the number of crossings and horizontal space usage. We then evaluate these algorithms on a small benchmark set to show their effectiveness.

翻译：情节线可视化是一种流行的可视化方式，用于展示角色及其随时间发生的互动：角色被绘制为x单调曲线，而互动则通过对应角色曲线在垂直条带中的紧密接近来呈现。现有的情节线生成方法假设互动存在全序关系，然而现实世界中的数据往往不包含这种全序。相反，多个互动通常被归入更粗略的时间区间，例如年份。我们利用这种分组特性，引入了一种新的模型——带时间区间的情节线，并提出了两种方法来最小化交叉数量和水平空间占用。随后，我们在一个小型基准集上对这些算法进行了评估，以证明其有效性。