An Overview of Exploratory Data Visualization

1. An Overview of Exploratory Data Visualization Dr. Matthew Ward Computer Science Department Worcester Polytechnic Institute From Data to Knowledge: Exploring Industrial, Scientific, and Commercial Databases

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

3. Visualization Through History • Hieroglyphics • Charts • Maps • Diagrams From Data to Knowledge: Exploring Industrial, Scientific, and Commercial Databases

4. Visualization Today • Medicine • Earth Sciences • Life Sciences • Engineering • Manufacturing • Economics/Commerce • Communications From Data to Knowledge: Exploring Industrial, Scientific, and Commercial Databases

5. The Visualization Process Raw Data Filter, Select Transform, Aggregate Derived/Extracted Data Normalize Map Data Components Graphical Components Reorganize, Sort Present One or More Ways Zoom, Rotate Display From Data to Knowledge: Exploring Industrial, Scientific, and Commercial Databases

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

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

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

9. 1-D Techniques From Data to Knowledge: Exploring Industrial, Scientific, and Commercial Databases

10. 2-D Techniques From Data to Knowledge: Exploring Industrial, Scientific, and Commercial Databases

11. 3-D Techniques From Data to Knowledge: Exploring Industrial, Scientific, and Commercial Databases

12. N-D Techniques From Data to Knowledge: Exploring Industrial, Scientific, and Commercial Databases

13. Dynamic Techniques From Data to Knowledge: Exploring Industrial, Scientific, and Commercial Databases

14. Nontraditional Techniques From Data to Knowledge: Exploring Industrial, Scientific, and Commercial Databases

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

16. Some Interactive Tools From Data to Knowledge: Exploring Industrial, Scientific, and Commercial Databases

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

18. Visualization Resources - Books • Keller, Peter, and Keller, Mary. Visual Cues: Practical Data Visualization. IEEE Press, 1993. • Tufte, Edward. The Visual Display of Quantitative Information. Graphics Press, 1983. • Tufte, Edward. Envisioning Information. Graphics Press, 1990. • Tufte, Edward. Visual Explanations. Graphics Press, 1997. . • Fayyad, Usama, et. al.. Information Visualization in Data Mining and Knowledge Discovery. Morgan-Kaufmann, 2002. • Nelson, Gregory, et. al.. Scientific Visualization: Overviews, Methodologies, Techniques. IEEE CS Press, 1997. • Lichtenbelt, Barthold, et. al. Introduction to Volume Rendering. Prentice-Hall, 1998 • Spence, Robert. Information Visualization. Addison-Wesley, 2001. • Ware, Colin. Information Visualization: Perception for Design. Morgan-Kaufmann, 1999. • Chen, Chaomei. Information Visualization and Virtual Environments. Springer, 1999. From Data to Knowledge: Exploring Industrial, Scientific, and Commercial Databases

19. Visualization Resources - Journals • IEEE Transactions on Visualization and Computer Graphics • Information Visualization • Computer Graphics and Applications • Journal of Computational and Graphical Statistics From Data to Knowledge: Exploring Industrial, Scientific, and Commercial Databases

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