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Vis Inf  2021, Vol. 5 Issue (1): 23-33    DOI: 10.1016/j.visinf.2020.12.002
论文     
可视分析中检测模式的理论模型
Natalia Andrienkoa,b, Gennady Andrienkoa,b, Silvia Mikschc, Heidrun Schumannd, Stefan Wrobela,e
aFraunhofer Institute IAIS, Sankt-Augustin, Germany bCity, University of London, London, UK cTU Wien, Vienna, Austria dUniversity of Rostock, Rostock, Germany eUniversity of Bonn, Bonn, Germany
A theoretical model for pattern discovery in visual analytics
Natalia Andrienkoa,b, Gennady Andrienkoa,b, Silvia Mikschc, Heidrun Schumannd, Stefan Wrobela,e
aFraunhofer Institute IAIS, Sankt-Augustin, Germany bCity, University of London, London, UK cTU Wien, Vienna, Austria dUniversity of Rostock, Rostock, Germany eUniversity of Bonn, Bonn, Germany
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摘要: “模式”一词经常出现在可视化和可视分析的文献中,但“模式”指的是什么?本文对数据分布中的模式给出了一个实用的定义,即由两个或多个数据中若干个具有关联关系的数据(组成)元素形成的组合,这些元素可以作为一个统一的整体进行表示和处理。 本文提出的理论模型描述了如何由数据组成元素之间的关联关系来构成模式。知道了这些关联关系的类型,就可以预测可能存在哪些类型的模式。 本文展示了该模型如何强化和完善了可视化已有的基本原理。基于该模型可开发出一系列交互式分析操作,在可视化分析工作流程中将已发现的模式显式地用于进一步的数据分析中。
关键词: 可视化分析数据分布模式抽象数据组织数据安排数据变化模式检测    
Abstract: The word ‘pattern’ frequently appears in the visualisation and visual analytics literature, but what do we mean when we talk about patterns? We propose a practicable definition of the concept of a pattern in a data distribution as a combination of multiple interrelated elements of two or more data components that can be represented and treated as a unified whole. Our theoretical model describes how patterns are made by relationships existing between data elements. Knowing the types of these relationships, it is possible to predict what kinds of patterns may exist. We demonstrate how our model underpins and refines the established fundamental principles of visualisation. The model also suggests a range of interactive analytical operations that can support visual analytics workflows where patterns, once discovered, are explicitly involved in further data analysis.
Key words: Visual analytics    Data distribution    Pattern    Abstraction    Data organisation    Data arrangement    Data variation    Pattern discovery
出版日期: 2021-01-19
通讯作者: Natalia Andrienko     E-mail: atalia.andrienko@iais.fraunhofer.de
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Natalia Andrienko
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Natalia Andrienko, Gennady Andrienko, Silvia Miksch, Heidrun Schumann, Stefan Wrobel. A theoretical model for pattern discovery in visual analytics. Vis Inf, 2021, 5(1): 23-33.

链接本文:

http://www.zjujournals.com/vi/CN/10.1016/j.visinf.2020.12.002        http://www.zjujournals.com/vi/CN/Y2021/V5/I1/23

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