A Visual Data Exploration Framework for Complex Problem Solving Based on Extended Cognitive Fit Theory

Authors:
Ying Zhu;Xiaoyuan Suo;G. Scott Owen
Affiliations:
Department of Computer Science, Georgia State University, Atlanta, USA;Mathematics and Computer Science Department, Webster University, St. Louis, USA;Department of Computer Science, Georgia State University, Atlanta, USA
Venue:
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
Year:
2009

Citing 7
Cited 0

Task-analytic approach to the automated design of graphic presentations

ACM Transactions on Graphics (TOG)
The CERT guide to system and network security practices

The CERT guide to system and network security practices
Task-Specific Visualization Design

IEEE Computer Graphics and Applications
A task-oriented view of information visualization

CHI '99 Extended Abstracts on Human Factors in Computing Systems
External Representations Contribute to the Dynamic Construction of Ideas

DIAGRAMS '02 Proceedings of the Second International Conference on Diagrammatic Representation and Inference
Semiology of graphics

Semiology of graphics
An integrated task-based framework for the design and evaluation of visualizations to support preferential choice

Proceedings of the working conference on Advanced visual interfaces

Quantified Score

Hi-index	0.00

Visualization

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.