使用数据立方体的多尺度可视化

Chris Stolte, Diane Tang, P. Hanrahan
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引用次数: 163

摘要

大多数分析师首先对数据进行概述,然后逐渐将他们的观点提炼得更加集中和详细。多尺度平移和缩放系统是有效的,因为它们直接支持这种方法。然而,生成大型数据集的抽象概述是困难的,大多数系统只利用一种抽象:视觉抽象。此外,这些现有的系统将分析人员限制在数据的单一缩放路径上,因此只有一组抽象视图。本文提出:(1)一种描述具有数据和视觉抽象的数据多维数据集的多尺度可视化的形式化方法;(2)一种通过遍历具有不同细节级别节点的缩放图,沿一个或多个维度独立缩放的方法。作为如何使用我们的系统设计多尺度可视化的一个示例,我们使用我们的形式化描述了四种设计模式。这些设计模式显示了通用关系数据库多尺度可视化的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multiscale visualization using data cubes
Most analysts start with an overview of the data before gradually refining their view to be more focused and detailed. Multiscale pan-and-zoom systems are effective because they directly support this approach. However generating abstract overviews of large data sets is difficult, and most systems take advantage of only one type of abstraction: visual abstraction. Furthermore, these existing systems limit the analyst to a single zooming path on their data and thus a single set of abstract views. This paper presents: (1) a formalism for describing multiscale visualizations of data cubes with both data and visual abstraction, and (2) a method for independently zooming along one or more dimensions by traversing a zoom graph with nodes at different levels of detail. As an example of how to design multiscale visualizations using our system, we describe four design patterns using our formalism. These design patterns show the effectiveness of multiscale visualization of general relational databases.
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