arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1908.07316cs.HC
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在叠加时间序列和时间事件序列可视化中评估对齐方法

Evaluating Alignment Approaches in Superimposed Time-Series and Temporal Event-Sequence Visualizations

Yixuan Zhang, Sara Di Bartolomeo, Fangfang Sheng, Holly Jimison, Cody Dunne

摘要:

复合时间事件序列可视化包括哨兵事件对齐技术,以应付数据量和品种。先前的工作已经证明了使用单事件对齐来理解围绕哨兵事件的前体、共发和后效事件的实用性。然而,在复合可视化中,单事件对齐的有用性还没有得到充分的评估。此外,最近提出的双事件对齐技术还没有经验评估。在这项工作中,我们围绕时间事件序列和时序分析设计了任务,并在Amazon Mechanical Turk上进行了一项对照实验,以检查四种哨兵事件对齐方法:无哨兵事件对齐(NoAlign),单事件对齐(SingleAlign),左对齐双事件对齐(DualLeft)和拉伸对齐双事件对齐(DualStretch)。方法之间的差异在数据行较多的情况下最为明显。对于理解两个哨兵事件之间的中间事件,双事件对齐是正确性的明显赢家--在理解两个哨兵事件之间的持续时间方面,NoAlign是明显的赢家:正确性-88%对DualStretch的36%-完成时间-55秒对DualLeft的101秒-和错误-1.5%对DualStretch的8.4%。对于了解前驱和后效事件,不同方法之间没有显著差异。本文的免费副本、评估激励和数据以及源代码可在https://osf.io/78fs5上获得

英文摘要:

Composite temporal event sequence visualizations have included sentinel event alignment techniques to cope with data volume and variety. Prior work has demonstrated the utility of using single-event alignment for understanding the precursor, co-occurring, and aftereffect events surrounding a sentinel event. However, the usefulness of single-event alignment has not been sufficiently evaluated in composite visualizations. Furthermore, recently proposed dual-event alignment techniques have not been empirically evaluated. In this work, we designed tasks around temporal event sequence and timing analysis and conducted a controlled experiment on Amazon Mechanical Turk to examine four sentinel event alignment approaches: no sentinel event alignment (NoAlign), single-event alignment (SingleAlign), dual-event alignment with left justification (DualLeft), and dual-event alignment with stretch justification (DualStretch). Differences between approaches were most pronounced with more rows of data. For understanding intermediate events between two sentinel events, dual-event alignment was the clear winner for correctness---71% vs. 18% for NoAlign and SingleAlign. For understanding the duration between two sentinel events, NoAlign was the clear winner: correctness---88% vs. 36% for DualStretch---completion time---55 seconds vs. 101 seconds for DualLeft---and error---1.5% vs. 8.4% for DualStretch. For understanding precursor and aftereffect events, there was no significant difference among approaches. A free copy of this paper, the evaluation stimuli and data, and source code are available at https://osf.io/78fs5

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。

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