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EventLines: Data-aware time compression for discrete event timelines
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AI
DOI：10.1177/14738716261430755.png)
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Abstract
En 中文
Many real-world datasets-publications over time, project progress, and health records-can be modeled as sequences of discrete events. These event sequences often exhibit irregular temporal distributions, where events cluster together in rapid succession, interspersed with periods of inactivity. Standard timeline charts with linear time axes fail to adequately represent such data, creating cluttered regions during event clusters while leaving other areas unutilized. We introduce EventLines, a novel technique that dynamically adjusts the time scale to match the underlying event distribution, enabling more efficient use of screen space. To address the challenges of non-linear time scaling, EventLines employs the time axis's visual representation itself to communicate the varying scale. We present findings from a crowdsourced graphical perception study that examines how different time scale representations influence temporal perception.
Keywords:
discrete events
event sequences
timelines
temporal visualization
crowdsourced user study
Journal
I
IF:
2
Papers:
21
Citations:
0
