时态事件序列中的时间属性分析
Analyzing Time Attributes in Temporal Event Sequences
摘要：
事件数据存在于各种领域中，例如电子健康记录、日常生活活动和网络点击流记录。目前的可视化方法来探索事件数据集中发现序列模式，但在研究事件序列中的时间属性时存在局限性。在研究患者流量分析中的等待时间或就诊时间时，时间属性尤为重要。我们提出了一个可视化的分析方法，允许识别的趋势和离群值的持续时间和事件序列中发生的时间。所提出的方法使用单个顺序和时间模式概述来呈现事件数据。用户驱动的多个事件的比对，排序的序列相似性和一个新的视觉编码的事件允许跨序列和序列内的时间趋势的比较。所提出的可视化允许推导的结果，否则无法获得使用传统的可视化。所提出的方法已被应用到谢菲尔德教学医院NHS基金会信托基金会提供的真实世界的数据集，其中四类的结论。
英文摘要：
Event data is present in a variety of domains such as electronic health records, daily living activities and web clickstream records. Current visualization methods to explore event data focus on discovering sequential patterns but present limitations when studying time attributes in event sequences. Time attributes are especially important when studying waiting times or lengths of visit in patient flow analysis. We propose a visual analytics methodology that allows the identification of trends and outliers in respect of duration and time of occurrence in event sequences. The proposed method presents event data using a single Sequential and Time Patterns overview. User-driven alignment by multiple events, sorting by sequence similarity and a novel visual encoding of events allows the comparison of time trends across and within sequences. The proposed visualization allows the derivation of findings that otherwise could not be obtained using traditional visualizations. The proposed methodology has been applied to a real-world dataset provided by Sheffield Teaching Hospitals NHS Foundation Trust, for which four classes of conclusions were derived.
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