Visual Analysis of High-Dimensional Event Sequence Data via Dynamic Hierarchical Aggregation
Visual Analysis of High-Dimensional Event Sequence Data via Dynamic Hierarchical Aggregation
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DOI：
10.1109/tvcg.2019.2934661
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发表时间：
2019-06
影响因子：
5.2
通讯作者：
D. Gotz;Jonathan Zhang;Wenyuan Wang;Joshua Shrestha;D. Borland
中科院分区：
文献类型：
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作者：
D. Gotz;Jonathan Zhang;Wenyuan Wang;Joshua Shrestha;D. Borland
时间事件数据是在广泛的领域中收集的，并且已经开发了各种可视化分析技术，以使分析师能够处理这种形式的数据。这些技术通常显示在共享共同模式的事件序列集合上计算的聚合统计信息。然而，此类技术通常会受到许多现实世界事件序列数据集的高维性的阻碍，这可能会阻碍有效的聚合。针对这一挑战的常见应对策略是在可视化之前将事件类型分组在一起，作为预处理，以便每个组可以在分析中表示为单个事件类型。然而，将这些事件分组作为预处理进行计算也会对分析造成很大的限制。本文提出了一种新的可视化分析方法的动态层次维聚集。该方法利用预定义的维度层次结构来计算量化信息量，相对于在运行时层次结构内的分组的替代级别的感兴趣的度量。然后，该信息以交互方式可视化，使用户能够动态地探索层次结构，以选择在分析中的任何单个步骤中使用的最合适的分组级别。主要贡献包括一个算法，用于交互式地确定一个特定的分析背景下的事件分组的信息量最大的集合，和一个有气味的分散加焦点可视化设计与基于优化的布局算法，支持交互式分层探索的替代事件类型分组。我们将这些技术应用于医疗领域的高维事件序列数据，并报告领域专家访谈的结果。
Temporal event data are collected across a broad range of domains, and a variety of visual analytics techniques have been developed to empower analysts working with this form of data. These techniques generally display aggregate statistics computed over sets of event sequences that share common patterns. Such techniques are often hindered, however, by the high-dimensionality of many real-world event sequence datasets which can prevent effective aggregation. A common coping strategy for this challenge is to group event types together prior to visualization, as a pre-process, so that each group can be represented within an analysis as a single event type. However, computing these event groupings as a pre-process also places significant constraints on the analysis. This paper presents a new visual analytics approach for dynamic hierarchical dimension aggregation. The approach leverages a predefined hierarchy of dimensions to computationally quantify the informativeness, with respect to a measure of interest, of alternative levels of grouping within the hierarchy at runtime. This information is then interactively visualized, enabling users to dynamically explore the hierarchy to select the most appropriate level of grouping to use at any individual step within an analysis. Key contributions include an algorithm for interactively determining the most informative set of event groupings for a specific analysis context, and a scented scatter-plus-focus visualization design with an optimization-based layout algorithm that supports interactive hierarchical exploration of alternative event type groupings. We apply these techniques to high-dimensional event sequence data from the medical domain and report findings from domain expert interviews.