1. Group One Data Visualization Spring 2005 Doctor of Professional Studies in Computing CSIS School of Computer Science and Information Systems

2. I.Overview II.Foundations of Visualization III.Visualization and KDD IV.I Can See Clearly Now V.XmdvTool Demonstration with ISBSG Case Study Agenda

3. Visualize "to form a mental vision, image, or picture of (something not visible or present to sight, or of an abstraction); to make visible to the mind or imagination" [The Oxford English Dictionary, 1989] Many Variations "Visualization": 1) Visualization in Scientific Computing (Scientific Visualization) 2) Information Visualization 3) Software Visualization I.Overview

4. Running Man

5. Fish Eating Boat

6. I.Foundations of Visualization

11. III.Visualization and KDD Knowledge Discovery from Databases –Data Processing –Machine Learning –Evaluation –Visualization Experiments may be nested Approach Advocated by YALE –Yet Another Learning Environment –http://www-ai.cs.uni-dortmund.de/SOFTWARE/YALE

12. IV.I Can See Clearly Now Data generation is exploding, particularly dimensional data Visualization takes place in context; tools and functionality are driven by user needs and objectives Yang, et al provide an excellent baseline list of core and advanced techniques for consideration Keim introduces an interesting 3-dimention view linking data type, interaction technique, and display type Key Points:

13. How much new information per person? According to the Population Reference Bureau, the world population is 6.3 billion, thus almost 800 MB of recorded information is produced per person each year. It would take about 30 feet of books to store the equivalent of 800 MB of information on paper. Population Reference Bureau Information explosion? We estimate that new stored information grew about 30% a year between 1999 and 2002 The World Wide Web contains about 170 terabytes of information on its surface; in volume this is seventeen times the size of the Library of Congress print collections. Instant messaging generates five billion messages a day (750GB), or 274 Terabytes a year. Email generates about 400,000 terabytes of new information each year worldwide. Data Growth Factoids:

14. Managerial Snap-shot Interactive reporting “What If" analysis What Next ? What should I do ? “Richness” of Information Use/need Visualization takes place in context – different users with different needs have different requirement and techniques.

15. Prescribed action: Alerts and notifications Managerial Snap-shot Interactive reporting “What If" analysis What Next ? What should I do ? Managed Metrics: Scorecard & Dashboards Enterprise Reporting: Navigation needs and reliable information Mutli- dimensional “speed of thought” Analysis and predictive values “Richness” of Information Use/need Typical Output Visualization takes place in context – different users with different needs have different requirement and techniques.

16. Prescribed action: Alerts and notifications Managerial Snap-shot Interactive reporting “What If" analysis What Next ? What should I do ? Managed Metrics: Scorecard & Dashboards Enterprise Reporting: Navigation needs and reliable information Mutli- dimensional “speed of thought” Analysis and predictive values “Richness” of Information Use/need Typical Output Interaction Fixed display Filter and Zoom Slice & Dice, Pivot tables Derived Information Recommend and Act Visualization takes place in context – different users with different needs have different requirement and techniques.

17. Prescribed action: Alerts and notifications Managerial Snap-shot Interactive reporting “What If" analysis What Next ? What should I do ? Managed Metrics: Scorecard & Dashboards Enterprise Reporting: Navigation needs and reliable information Mutli- dimensional “speed of thought” Analysis and predictive values “Richness” of Information Use/need Typical Output Interaction Fixed display Filter and Zoom Slice & Dice, Pivot tables Derived Information Recommend and Act Visualization takes place in context – different users with different needs have different requirement and techniques. Use the data to prove/disprove a hypothesisUse the data to generate hypotheses

18. Filter – reduce the amount of data to increase focus Distortion – enlarge some part of a display to examine details Zooming and Panning – enlarge, make smaller, move through display Manual Pixel re-ordering – top to bottom, bottom to top Comparing – create/examine relationships Refining – generate a new, focused display of data subset Yang, et al identify Core Navigation Tool:

19. Showing names – mouse-overs Layer re-ordering – ordering of overlapping data Manual relocation – separation of overlapping data Extent Scaling – interactive, proportional resizing Dynamic Masking – hiding of irrelevant data Automatic Shifting – automatic overlap reduction Yang, et al identify Advance Navigation Tool:

20. Keim creates a 3-dimentional chart that relates interaction technique, type of data, and visualization technique

21. Simple data Complex data Breakdown and examination of Keim model

22. Interaction and manipulation techniques, similar to Yang

23. Breakdown and examination of Keim model Recommended display type (some of which we will see in the demos)

24. V.XmdvTool Demonstration with ISBSG Case Study Tool Available at http://davis.wpi.edu/~xmdvhttp://davis.wpi.edu/~xmdv Methods –Scatterplots –Glyphs –Parallel Cordinates –Dimensional Stacking N-D Brush –Highlight –Mask –Values –Average

25. Source of Case Study The International Software Benchmarking Standards Group –Mission – Help Improve Management of IT Resources Through a Public Repository –Produces – ISBSG Estimating, Benchmarking & Research Suite (Release 8 in 2003) of Data and Tools –Academic Use – Free or Nominal Charge –Web Site – www.isbsg.orgwww.isbsg.org Same Source As Team One’s Data Mining Project

26. Composition of Study File 451 New Development Projects Fields –Size in Adjusted Function Points –Duration in Months –Maximum Team Size –Work Effort in Hours –Project Delivery Rate