事件序列可视化摘要技术的比较评估
A Comparative Evaluation of Visual Summarization Techniques for Event Sequences
AI总结：
本文通过众包实验在多任务、数据集和粒度下比较 CoreFlow、SentenTree 与 Sequence Synopsis，发现后者摘要质量最高但理解耗时最长。
摘要：
现实世界中的事件序列通常复杂且异构，这使得使用简单的数据聚合和视觉编码技术难以创建有意义的可视化。因此，可视化研究人员开发了大量视觉摘要技术，以生成序列数据的简明概览。这些技术在摘要结构和内容方面差异很大，而目前在理解这些技术的有效性方面仍存在知识空白。在本研究中，我们介绍了一项基于洞见的众包实验的设计与结果，该实验评估了三种现有的视觉摘要技术：CoreFlow、SentenTree 和 Sequence Synopsis。我们在三个任务、六个数据集以及六个粒度级别上比较了这些技术生成的视觉摘要。我们分析了这些变量对参与者评定的摘要质量以及实验任务完成时间的影响。分析表明，Sequence Synopsis 在所有三个任务中都产生了最高质量的视觉摘要，但理解 Sequence Synopsis 的结果也需要最长时间。我们还发现，参与者从两个方面评价视觉摘要质量：内容和可解释性。我们讨论了这些发现对开发和评估新的视觉摘要技术的启示。
英文摘要：
Real-world event sequences are often complex and heterogeneous, making it difficult to create meaningful visualizations using simple data aggregation and visual encoding techniques. Consequently, visualization researchers have developed numerous visual summarization techniques to generate concise overviews of sequential data. These techniques vary widely in terms of summary structures and contents, and currently there is a knowledge gap in understanding the effectiveness of these techniques. In this work, we present the design and results of an insight-based crowdsourcing experiment evaluating three existing visual summarization techniques: CoreFlow, SentenTree, and Sequence Synopsis. We compare the visual summaries generated by these techniques across three tasks, on six datasets, at six levels of granularity. We analyze the effects of these variables on summary quality as rated by participants and completion time of the experiment tasks. Our analysis shows that Sequence Synopsis produces the highest-quality visual summaries for all three tasks, but understanding Sequence Synopsis results also takes the longest time. We also find that the participants evaluate visual summary quality based on two aspects: content and interpretability. We discuss the implications of our findings on developing and evaluating new visual summarization techniques.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
