WPI Center for Research in Exploratory Data and Information Analysis From Data to Knowledge: Exploring Industrial, Scientific, and Commercial Databases

WPI Center for Research in Exploratory Data and Information Analysis

From Data to Knowledge: Exploring Industrial, Scientific, and Commercial Databases

An Overview of Exploratory Data Visualization

Dr. Matthew Ward

Computer Science Department

Worcester Polytechnic Institute



What is Visualization?

• Graphical presentation of data and information for– Presentation of data, concepts, relationships

– Confirmation of hypotheses

– Exploration to discover patterns, trends, anomalies, structure, associations

• Useful across all areas of science, engineering, manufacturing, commerce, education…..



Visualization Through History

• Hieroglyphics• Charts• Maps• Diagrams



Visualization Today

• Medicine• Earth Sciences• Life Sciences• Engineering• Manufacturing• Economics/Commerce• Communications



The Visualization Process

Raw Data

Derived/Extracted Data

Graphical Components

Display

Transform, Aggregate

Map Data Components

Present One or More Ways

Filter, Select

Normalize

Reorganize, Sort

Zoom, Rotate



Data Characteristics

• Continuous Model (mostly SciVis)– Number of independent variables (1, 2, 3, n)

– Data type (scalar, vector, tensor, multivariate)

– Number of dependent variables (1, many)

• Discrete Model (mostly InfoVis)– Connected

• Graphs, trees, node-link, hierarchical

– Unconnected• Dependent or independent variables (2, 3, n)



Graphical Mappings

• Position (x, y, z)• Color (hue, saturation, value)• Shape (need to be perceptually distinct)• Size• Orientation (can interfere with shape)• Texture (contrast, orientation, frequency)• Motion (2 or 3 D)• Blinking



Many Perceptual Issues

• How accurately do we perceive various graphical features?

• How quickly can we detect/classify something visually?

• How are our abilities affected by training?• How variable is our perception based on the

surrounding field of view?• How is our perception affected by stress, age,

gender, boredom, fatigue…….



1-D Techniques

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10

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1st Qtr 2nd Qtr 3rd Qtr 4th Qtr

Sales



2-D Techniques



3-D Techniques



N-D Techniques



Dynamic Techniques



Nontraditional Techniques



The Need for Interaction

• All stages of the visualization pipeline can benefit from user interaction

• Exploration requires tools for navigation, filtering, selection, view enhancement

• Much of recent innovation has focused on developing intuitive, powerful interaction mechanisms

• Interactions can focus on objects, their attributes, or their interrelationships



Some Interactive Tools



Summary

• Visualization is a powerful tool for qualitative analysis of data and information

• It can be useful for presenting or exploring virtually any data, regardless of size, type, complexity, or application domain

• It can be effectively used to detect, isolate, and classify data features of interest and guide and evaluate the results of quantitative data analysis



Visualization Resources - Books

1. Keller, Peter, and Keller, Mary. Visual Cues: Practical Data Visualization. IEEE Press, 1993.

2. Tufte, Edward. The Visual Display of Quantitative Information. Graphics Press, 1983.

3. Tufte, Edward. Envisioning Information. Graphics Press, 1990. 4. Tufte, Edward. Visual Explanations. Graphics Press, 1997. . 5. Fayyad, Usama, et. al.. Information Visualization in Data Mining and Knowledge

Discovery. Morgan-Kaufmann, 2002. 6. Nelson, Gregory, et. al.. Scientific Visualization: Overviews, Methodologies,

Techniques. IEEE CS Press, 1997.7. Lichtenbelt, Barthold, et. al. Introduction to Volume Rendering. Prentice-Hall, 19988. Spence, Robert. Information Visualization. Addison-Wesley, 2001. 9. Ware, Colin. Information Visualization: Perception for Design. Morgan-Kaufmann,

1999. 10. Chen, Chaomei. Information Visualization and Virtual Environments. Springer,

1999.



Visualization Resources - Journals

• IEEE Transactions on Visualization and Computer Graphics

• Information Visualization

• Computer Graphics and Applications

• Journal of Computational and Graphical Statistics



Visualization Resources - Conferences

• IEEE Visualization Conference• IEEE InfoVis and Volume Visualization Symposia• SPIE Conference on Visualization and Data Analysis• Eurographics Visualization Symposium• ACM Symposium on Software Visualization• Int. Symposium on Intelligent Data Analysis• Int. Conference on Information Visualization• ACM SIGKDD

Documents

WPI Center for Research in Exploratory Data and Information Analysis From Data to Knowledge: Exploring Industrial, Scientific, and Commercial Databases