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可视化集合中的不确定性。

Visualizing Uncertainty in Sets.

作者信息

Tominski Christian, Behrisch Michael, Bleisch Susanne, Fabrikant Sara Irina, Mayr Eva, Miksch Silvia, Purchase Helen

出版信息

IEEE Comput Graph Appl. 2023 Sep-Oct;43(5):49-61. doi: 10.1109/MCG.2023.3300441. Epub 2023 Sep 14.

Abstract

Set visualization facilitates the exploration and analysis of set-type data. However, how sets should be visualized when the data are uncertain is still an open research challenge. To address the problem of depicting uncertainty in set visualization, we ask 1) which aspects of set type data can be affected by uncertainty and 2) which characteristics of uncertainty influence the visualization design. We answer these research questions by first describing a conceptual framework that brings together 1) the information that is primarily relevant in sets (i.e., set membership, set attributes, and element attributes) and 2) different plausible categories of (un)certainty (i.e., certainty, undefined uncertainty as a binary fact, and defined uncertainty as quantifiable measure). Following the structure of our framework, we systematically discuss basic visualization examples of integrating uncertainty in set visualizations. We draw on existing knowledge about general uncertainty visualization and previous evidence of its effectiveness.

摘要

集合可视化有助于对集合类型的数据进行探索和分析。然而,当数据存在不确定性时,应如何对集合进行可视化仍然是一个开放的研究挑战。为了解决在集合可视化中描述不确定性的问题,我们提出两个问题:1)集合类型数据的哪些方面会受到不确定性的影响?2)不确定性的哪些特征会影响可视化设计?我们通过首先描述一个概念框架来回答这些研究问题,该框架将1)集合中主要相关的信息(即集合成员、集合属性和元素属性)与2)不同合理的(不)确定性类别(即确定性、作为二元事实的未定义不确定性以及作为可量化度量的已定义不确定性)结合在一起。按照我们框架的结构,我们系统地讨论了在集合可视化中整合不确定性的基本可视化示例。我们借鉴了关于一般不确定性可视化的现有知识及其有效性的先前证据。

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