Paper Info
Title
A Visual Data Exploration Framework for Complex Problem Solving Based on Extended Cognitive Fit Theory
Abstract
In this paper, we present a visual data exploration framework for complex problem solving. This framework consists of two major components: an enhanced task flow diagram and data visualization window. Users express their problem solving process and strategies using the enhanced task flow diagram, while multiple frames of visualizations are automatically constructed in the data visualization window and are organized as a tree map. This framework is based an extended Cognitive Fit Theory, which states that a data visualization should be constructed as a cognitive fit for specific tasks and a set of data variables. It also states that the structure of multiple data visualizations should match the structure of the corresponding tasks. Therefore, in our framework, data is presented in either visual or non-visual format based on the cognitive characteristic of the corresponding task. As users explore various problem solving strategies by editing the task flow diagram, the corresponding data visualizations are automatically updated for the best cognitive fit. This visual data exploration framework is particularly beneficial for users who need to conduct specific and complex tasks with large amount of data. As a case study, we present a computer security data visualization prototype.
Year
DOI
Venue
2009
10.1007/978-3-642-10520-3_83
ISVC
Keywords
Field
DocType
complex problem,visual data exploration framework,data visualization,extended cognitive fit theory,corresponding data visualization,data variable,cognitive fit,enhanced task flow diagram,data visualization window,multiple data visualization,computer security data,corresponding task,information visualization,computer security
Computer vision,Data visualization,Data exploration,Information visualization,Computer science,Visual analytics,Complex problem solving,Cognitive fit,Artificial intelligence,Cognition,Machine learning,Data flow diagram
Conference
Volume
ISSN
Citations 
5876
0302-9743
0
PageRank 
References 
Authors
0.34
8
3
Name
Order
Citations
PageRank
Ying Zhu1278.27
Xiaoyuan Suo21059.14
G. Scott Owen3254.06