Samp;scamp;equenceamp;/scamp; Bamp;scamp;raiding:amp;/scamp; Visual Overviews of Temporal Event Sequences and Attributes

Sara Di Bartolomeo; Yixuan Zhang; Fangfang ShengCody Dunne

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Samp;scamp;equenceamp;/scamp; Bamp;scamp;raiding:amp;/scamp; Visual Overviews of Temporal Event Sequences and Attributes

机译：Samp;scamp;equenceamp;/scamp; Bamp;scamp;raiding:amp;/scamp; Visual Overviews of Temporal Event Sequences and Attributes

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摘要

Temporal event sequence alignment has been used in many domains to visualize nuanced changes and interactions over time. Existing approaches align one or two sentinel events. Overview tasks require examining all alignments of interest using interaction and time or juxtaposition of many visualizations. Furthermore, any event attribute overviews are not closely tied to sequence visualizations. We present Samp;scamp;equenceamp;/scamp; Bamp;scamp;raiding,amp;/scamp; a novel overview visualization for temporal event sequences and attributes using a layered directed acyclic network. Samp;scamp;equenceamp;/scamp; Bamp;scamp;raidingamp;/scamp; visually aligns many temporal events and attribute groups simultaneously and supports arbitrary ordering, absence, and duplication of events. In a controlled experiment we compare Samp;scamp;equenceamp;/scamp; Bamp;scamp;raidingamp;/scamp; and IDMVis on user task completion time, correctness, error, and confidence. Our results provide good evidence that users of Samp;scamp;equenceamp;/scamp; Bamp;scamp;raidingamp;/scamp; can understand high-level patterns and trends faster and with similar error. A full version of this paper with all appendices; the evaluation stimuli, data, and analysis code; and source code are available at amp;inline-formulaamp;amp;tex-math notation="LaTeX"amp;$text{osf.io}/mathrm{mq}2mathrm{wt}$amp;/tex-mathamp;amp;alternativesamp;amp;graphic position="float" orientation="portrait" xlink:href="27tvcg02-bartolomeo-3030442-eqinline-1-small.tif"/amp;amp;/alternativesamp;amp;/inline-formulaamp;.

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《IEEE transactions on visualization and computer graphics》 |2021年第2期|1353-1363|共11页
作者
Sara Di Bartolomeo; Yixuan Zhang; Fangfang ShengCody Dunne;
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作者单位

Northeastern University;

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原文格式 PDF
正文语种英语
中图分类模式识别与装置;
关键词
Diabetes; Glucose; Data visualization; Blood; Task analysis; Insulin; Visualization; Temporal event sequence visualization; network visualization; algorithm; evaluation;

Samp;scamp;equenceamp;/scamp; Bamp;scamp;raiding:amp;/scamp; Visual Overviews of Temporal Event Sequences and Attributes

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