A theoretical model for pattern discovery in visual analytics

作     者：Natalia Andrienko Gennady Andrienko Silvia Miksch Heidrun Schumann Stefan Wrobel Natalia Andrienko;Gennady Andrienko;Silvia Miksch;Heidrun Schumann;Stefan Wrobel

作者机构：Fraunhofer Institute IAISSankt-AugustinGermany CityUniversity of LondonLondonUK TU WienViennaAustria University of RostockRostockGermany University of BonnBonnGermany 

出 版 物：《Visual Informatics》 (可视信息学（英文）)

年 卷 期：2021年第5卷第1期

页      面：23-42页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This research was supported by Fraunhofer Center for Machine Learning within the Fraunhofer Cluster for Cognitive Internet Technologies by DFG within Priority Programme 1894(SPP VGI) by EU in project SoBigData++ by SESAR in projects TAPAS and SIMBAD by Austrian Science Fund(FWF)project KnowVA(grant P31419-N31) 

主　　题：Visual analytics Data distribution Pattern Abstraction Data organisation Data arrangement Data variation Pattern discovery 

摘      要：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 *** theoretical model describes how patterns are made by relationships existing between data *** the types of these relationships,it is possible to predict what kinds of patterns may *** demonstrate how our model underpins and refines the established fundamental principles of *** 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.