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.

中文翻译：

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通过动态层次聚合对高维事件序列数据进行可视化分析。

时间事件数据跨广泛的领域收集，并且已经开发了各种可视化分析技术，以使使用这种数据形式的分析人员能够获得授权。这些技术通常显示通过共享公共模式的事件序列集计算出的聚合统计信息。但是，由于许多现实世界中的事件序列数据集的高维性会阻止有效的聚合，因此常常会阻碍此类技术。应对这一挑战的通用应对策略是在可视化之前将事件类型分组在一起，作为一种预处理，以便每个组可以在分析中表示为单个事件类型。但是，将这些事件分组作为预处理进行计算也会对分析产生重大限制。本文提出了一种用于动态层次维度聚合的新视觉分析方法。该方法利用维度的预定义层次结构，在运行时相对于感兴趣的度量来计算量化层次结构内分组的替代级别的信息性。然后以交互方式可视化此信息，使用户能够动态浏览层次结构，以选择最合适的分组级别，以在分析中的任何单个步骤使用。主要贡献包括用于交互式确定特定分析上下文的最具信息性的事件分组集的算法，以及具有基于优化的布局算法的香味分散加焦点可视化设计，该算法支持对替代事件类型分组的交互式分层探索。