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CS 5764 Information Visualization Dr. Chris North Purvi Saraiya GTA Today 1. 2. 3. 4. What is Information Visualization? Who cares? What will I learn? How will I learn it? 1. What is Information Visualization? • The use of computer-supported, interactive, visual representations of abstract data to amplify cognition – Card, Mackinlay, Shneiderman The Big Problem Web, scientific data news, products sales shopping census data Data system logsj sports Vision: 100MB/sec Aural: 100KB/sec Smell: Haptics Taste esp Human Data Transfer How? 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! Find the Red Square: Pre-attentive • Which state has highest Income? Avg? Distribution? • Relationship between Income and Education? • Outliers? College Degree % Per Capita Income % 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, … History: Static Graphics Minard, 1869 The Big Problem Human Data Transfer Data visualization The Bigger Problem Human Data Transfer Data interactive visualization Interactive Graphics • Homefinder 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 User Tasks • Easy stuff: Excel can do this • 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, Visualization can do this! Paths, … More than just “data transfer” • Glean higher level knowledge from the data Learn = data knowledge • Hides data • Hampers knowledge • Nothing learned • No insight • Reveals data • Reveals knowledge that is not necessarily “stored” in the data • Insight! Class Motto Show me the data! 2. Who Cares? Presentation is everything 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 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 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 GigaPixel Display 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) 4. How will I learn it? Course Mechanics • http://infovis.cs.vt.edu/cs5764/ • Grading: (See Syllabus online) • • • • 60% 30% 5% 5% Project Homeworks Paper presentation or review Experiment & class participation • Format: • Read research papers (see web site) • In-class discussion • Emphasis on project 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 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? 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 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! 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. To Do … • Read: CMS chapter 1 handout (pg 1-16) • HW 1, due next Mon: SequoiaView • Form project teams • Wed: Intell Analysis exercise & Projects