1 / 34

CS 5764 Information Visualization

Dive into the world of information visualization, discovering the power of interactive visual representations to amplify cognition. Learn how visualization goes beyond data transfer to reveal hidden insights, patterns, and relationships in data.

shaeleigh-cross
Download Presentation

CS 5764 Information Visualization

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

  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, scientific datanews, products salesshoppingcensus datasystem logsj sports Human Data Data Transfer How? 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. 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

  15. 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

  16. 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!

  17. 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

  18. Class Motto Show me the data!

  19. 2. Who Cares?

  20. Presentation is everything

  21. 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

  22. 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

  23. 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 Topics

  24. GigaPixel Display

  25. 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)

  26. 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

  27. 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

  28. 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?

  29. 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

  30. 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!

  31. 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.

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

More Related