1. CS 5764 Information Visualization Dr. Chris North Purvi Saraiya GTA

2. Today 1.What is Information Visualization? 2.Who cares? 3.What will I learn? 4.How will I learn it?

3. 1. What is Information Visualization? The use of computer-supported, interactive, visual representations of abstract data to amplify cognition –Card, Mackinlay, Shneiderman

4. The Big Problem Data Human How? Data Transfer Web, scientific data news, products sales shopping census data system logsj sports Vision: 100MB/sec Aural: 100KB/sec Smell: Haptics Taste esp

5. Human Vision Highest bandwidth sense Fast, parallel Pattern recognition Pre-attentive Extends memory and cognitive capacity (Multiplication test) People think visually Brain = 8 lbs, vision = 3 lbs Impressive. Lets use it!

6. Find the Red Square: Pre-attentive

7. Which state has highest Income? Avg? Distribution? Relationship between Income and Education? Outliers?

8. Per Capita Income College Degree %

9. %

10. Visual Representation Matters! Text vs. Graphics What if you could only see 1 state’s data at a time? (e.g. Census Bureau’s website) What if I read the data to you? Graphics vs. Graphics depends on user tasks, data, …

11. History: Static Graphics Minard, 1869

12. The Big Problem Data Human visualization Data Transfer

13. The Bigger Problem Data Human interactive visualization Data Transfer

14. Interactive Graphics Homefinder

17. Search Forms Avoid the temptation to design a form-based search engine More tasks than just “search” How do I know what to “search” for? What if there’s something better that I don’t know to search for? Hides the data Only supports Q&A

18. User Tasks Easy stuff: Min, max, average, % These only involve 1 data item or value Hard stuff: Patterns, trends, distributions, changes over time, outliers, exceptions, relationships, correlations, multi-way, combined min/max, tradeoffs, clusters, groups, comparisons, context, anomalies, data errors, Paths, … Excel can do this Visualization can do this!

19. More than just “data transfer” Glean higher level knowledge from the data Learn = data  knowledge Reveals data Reveals knowledge that is not necessarily “stored” in the data Insight! Hides data Hampers knowledge Nothing learned No insight

20. Class Motto Show me the data!

21. 2. Who Cares?

22. Presentation is everything

23. My Philosophy: Optimization Visualization = the best of both Impressive computation + impressive cognition Computer Serial Symbolic Static Deterministic Exact Binary, 0/1 Computation Programmed Follow instructions Amoral Human Parallel Visual Dynamic Non-deterministic Fuzzy Gestalt, whole, patterns Understanding Free will Creative Moral

24. 3. What Will I Learn? Design interactive visualizations Critique existing designs and tools Develop visualization software Empirically evaluate designs Understand current state-of-art An HCI focus A visualization = a user interface for data *

25. Topics Information Types: Multi-D 1D, 2D, 3D spatial Hierarchies/Trees Networks/Graphs Document collections Strategies: Design Principles Interaction strategies Navigation strategies Visual Overviews Multiple Views Empirical Evaluation Development Theory High-Resolution Displays

26. GigaPixel Display

27. Related Courses Scientific Visualization (ESM4714) Computer Graphics (4204, 6xxx) Usability Engineering (5714) Research Methods (5014) Model & Theories of HCI (5724) User Interface Software (5774) Info Storage & Retrieval (5604) Databases (5614), Digital Libraries (6xxx) Data Mining (6xxx)

28. 4. How will I learn it? Course Mechanics http://infovis.cs.vt.edu/cs5764/ Grading: (See Syllabus online) 60% Project 30% Homeworks 5% Paper presentation or review 5% Experiment & class participation Format: Read research papers (see web site) In-class discussion Emphasis on project

29. Research Class Creativity Open ended Often no “right” answer Reasoning/argument is more important Thinking deeply Self motivation, seek to excel Contribute to the state-of-the-art Jump start for thesis research, publication

30. Project Groups of 3 students Visualization for Intelligence Analysis Milestones: Team: choose team (due Wed!) Design Concept & Presentation: problem, lit. review, design, schedule (4 weeks) Formative Eval & Initial Impl Final presentation: final results Final paper: publishable?

31. Project Groups of 3 students Categories: Development: design, implement, evaluate new visualization Evaluation: empirical experiments with users Theory: literature survey, synthesize theory or taxonomy Milestones: Abstract: choose team and topic (due next week!) Proposal: problem, lit. review, design, schedule Mid-semester presentation: initial results Final presentation: final results Final paper: publishable?

32. Paper Presentations 10-15 minutes Read paper, Present visualization Information type Visual mappings Show pictures / demo / video Strengths, weaknesses E.g. Scale, insight factor, user tasks

33. Presentations Goals: 1: understand visualization (mappings, simple examples) 2: strengths, weaknesses Tips: Time is short: 10-15 min = ~7 slides, practice out loud Use pictures, pictures, pictures, pictures, … Use text only to hammer key points The “slide-sorter” test What’s the take-home message? ~2 main points Conclude with controversy Motivate!

34. Implementation detail crap The first step of processing requires the construction of several tree and graph structures to store the database. System then builds visualization of data by mapping data attributes of graph items to graphical attributes of nodes and links in the visualization windows on screen. More boring stuff nobody is ever going to read here or if they do they wont understand it anyway so why bother. If they do read it then they most certainly will not be listening to what you are saying so why bother give a talk? Why not just sit down and let everybody read your slides or just hand out the paper and then say ‘thank you’. This person needs to take Dr. North’s info vis class.

35. To Do … Read: CMS chapter 1 handout (pg 1-16) HW 1, due next Mon: SequoiaView Form project teams Wed: Intell Analysis exercise & Projects

36. Force Adds? Why? Academic goals? Can you keep up?