期刊
VISUAL INFORMATICS
卷 5, 期 1, 页码 23-42出版社
ELSEVIER
DOI: 10.1016/j.visinf.2020.12.002
关键词
Visual analytics; Data distribution; Pattern; Abstraction; Data organisation; Data arrangement; Data variation; Pattern discovery
类别
资金
- Fraunhofer Center for Machine Learning within the Fraunhofer Cluster for Cognitive Internet Technologies
- DFG [1894]
- EU in project SoBigData++
- Austrian Science Fund (FWF) project KnowVA [P31419-N31]
- SESAR in project TAPAS
- SESAR in project SIMBAD
This article discusses the definition of "pattern" in data visualization and visual analytics, proposing a practical concept definition. Patterns are formed by relationships between data elements, and understanding these relationships can predict possible pattern types. The model supports and refines established principles of visualization.
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. (C) 2021 The Author(s). Published by Elsevier B.V. on behalf of Zhejiang University and Zhejiang University Press Co. Ltd.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据