1. 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

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

3. WPI Center for Research in Exploratory Data and Information Analysis From Data to Knowledge: Exploring Industrial, Scientific, and Commercial Databases Visualization Through History Hieroglyphics Charts Maps Diagrams

4. WPI Center for Research in Exploratory Data and Information Analysis From Data to Knowledge: Exploring Industrial, Scientific, and Commercial Databases Visualization Today Medicine Earth Sciences Life Sciences Engineering Manufacturing Economics/Commerce Communications

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

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

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

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

9. WPI Center for Research in Exploratory Data and Information Analysis From Data to Knowledge: Exploring Industrial, Scientific, and Commercial Databases 1-D Techniques

10. WPI Center for Research in Exploratory Data and Information Analysis From Data to Knowledge: Exploring Industrial, Scientific, and Commercial Databases 2-D Techniques

11. WPI Center for Research in Exploratory Data and Information Analysis From Data to Knowledge: Exploring Industrial, Scientific, and Commercial Databases 3-D Techniques

12. WPI Center for Research in Exploratory Data and Information Analysis From Data to Knowledge: Exploring Industrial, Scientific, and Commercial Databases N-D Techniques

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

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

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

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

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

18. WPI Center for Research in Exploratory Data and Information Analysis From Data to Knowledge: Exploring Industrial, Scientific, and Commercial Databases 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, 1998 8.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.

19. WPI Center for Research in Exploratory Data and Information Analysis From Data to Knowledge: Exploring Industrial, Scientific, and Commercial Databases Visualization Resources - Journals IEEE Transactions on Visualization and Computer Graphics Information Visualization Computer Graphics and Applications Journal of Computational and Graphical Statistics

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