CS 5764 Information Visualization

1. CS 5764Information Visualization Dr. Chris North GTA: Beth Yost

2. Today • What is Information Visualization? • Who cares? • What will I learn? • 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 Web, … Human Data Data Transfer How? Vision:

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. College Degree % Per Capita Income

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 Human Data Data Transfer visualization

13. The Bigger Problem Data Human Data Transfer interactive visualization

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 Excel can do this • 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, … 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 • 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 Visualization = the best of both Impressive computation + impressive cognition

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

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

26. 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)

27. 4. How will I learn it?Course Mechanics • http://infovis.cs.vt.edu/cs5764/ • Grading: • 5% Paper presentation or review • 20% Homeworks (4) • 25% Pop quizzes and in-class activities • 50% Project • Format: • Read research papers (see web site) • In-class discussion • Emphasis on project

28. 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

29. 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?

30. 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

31. 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!

32. 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.

33. Force Adds? • Why? • Academic goals? • Can you keep up?