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2
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- Information Visualization = Emerging Field
- Provide Thorough Introduction
- Information Visualization aims
- To use human perceptual capabilities
- To gain insights into large and abstract data sets
- that are difficult to extract using standard query languages
- Abstract and Large Data Sets
- Symbolic
- Tabular
- Networked
- Hierarchical
- Textual information
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3
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- Foundation in Human Visual Perception
- How it relates to creating effective information visualizations.
- Understand Key Design Principles
for Creating Information Visualizations
- Study Major Information Visualization Tools
- Evaluate Information Visualizations Tools
- searchCrystal – Visual Retrieval Interface
- Design New, Innovative Visualizations
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4
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- 1 Human Visual Perception + Human Computer Interaction
- “Information Visualization: Perception for Design”
by Colin Ware, Morgan Kaufmann, 2000
- 2 Key Papers in Information Visualizations
- “Modern Information Retrieval - Chapter 10: User Interfaces and
Visualization” by Marti Hearst
- Key Papers from "Readings in Information Visualization: Using
Visualization to Think" and other sources – available
electronically
- 3 Develop / Evaluate searchCrystal
- 4 Term Projects
- Review + Analyze Visualizations Tools
- Evaluate Visualizations Tool
- Design Visualizations Prototype
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5
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- Class = Graduate Seminar
- Literature Review based on Seed Articles
- Discussion of Assigned Readings
- Viewing of Videos
- Hands-on Experience of Tools
- Evaluation of Tools
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6
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- Course Website
http://scils.rutgers.edu/~aspoerri/Teaching/InfoVisOnline/Home.htm
- Requirement
- “Modern Information Retrieval - Chapter 10: User Interfaces and
Visualization” by Marti Hearst
http://www.sims.berkeley.edu/~hearst/irbook/10/node1.html
- Key Papers from "Readings in Information Visualization: Using
Visualization to Think" and other sources – available
electronically
- Grading
- Short Reviews of Assigned Papers (20%)
- Group Evaluation of InfoVis Tool (20%)
- Research Topic & Class Presentation (20%)
- Final Project (40%)
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7
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- Schedule
- Link to Lecture Slides
- http://scils.rutgers.edu/~aspoerri/Teaching/InfoVisOnline/Schedule.htm
- Lecture Slides
http://scils.rutgers.edu/~aspoerri/Teaching/InfoVisOnline/Lectures/Lectures.htm
- Lecture Slides posted before lecture
- Slides Handout available for download & print-out
- Open in Powerpoint
- File > Print …
- “Print what” = “Handout”
- Select “2 slides” per page
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8
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- Review + Analyze Visualization Tools
- Describe topic and approach you plan to take
- Length: 20 to 25 pages
- Evaluate Visualization Tool
- Describe tool you want to evaluate as well as why and how
- Describe and motivate evaluation design
- Conduct evaluation with 3 to 5 people
- Report on evaluation results
- Potential Tools to evaluate: Star Trees, Personal Brain, … your
suggestion
- Design Visualization Prototype
- Describe data domain, motivate your choice and describe your
programming expertise
- Describe design approach
- Develop prototype
- Present prototype
- Will provide more specific instructions next week.
- Send instructor email with short description of your current project
idea by Week 7
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9
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- Anselm Spoerri
- Computer Vision
- Filmmaker – IMAGO
- Click on the center image to play video
- Information Visualization – InfoCrystal è searchCrystal
- Media Sharing – Souvenir
- In Action Examples: click twice on digital ink or play button
- Rutgers Website
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10
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- Help me
- Develop searchCrystal
- into
- Effective
- Tested
- Visual Retrieval Tool
- How?
- Testing of searchCrystal and Provide Feedback
- Testing of Related Tools
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11
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- Use human perceptual capabilities
to gain insights into large data sets
that are difficult to extract
using standard query languages
- Exploratory Visualization
- Look for structure, patterns, trends, anomalies, relationships
- Provide a qualitative overview of large, complex data sets
- Assist in identifying region(s) of interest and appropriate parameters
for more focussed quantitative analysis
- Shneiderman's Mantra:
- Overview first, zoom and filter, then details-on-demand
- Overview first, zoom and filter, then details-on-demand
- Overview first, zoom and filter, then details-on-demand
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- Scientific Visualization
- Show abstractions, but based on physical space
- Information Visualization
- Information does not have any obvious spatial mapping
- Fundamental Problem
- How to map non–spatial abstractions
into effective visual form?
- Goal
- Use of computer-supported, interactive, visual
representations of abstract data to amplify cognition
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13
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- Increased Resources
- Parallel perceptual processing
- Offload work from cognitive to perceptual system
- Reduced Search
- High data density
- Greater access speed
- Enhanced Recognition of Patterns
- Recognition instead of Recall
- Abstraction and Aggregation
- Perceptual Interference
- Perceptual Monitoring
- Color or motion coding to create pop out effect
- Interactive Medium
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14
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- Information Visualization = Emerging Field
- Key Principles
- Abstraction
- Overview è Zoom+Filter è Details-on-demand
- Direct Manipulation
- Dynamic Queries
- Immediate Feedback
- Linked Displays
- Linking + Brushing
- Provide Focus + Context
- Animate Transitions and Change of Focus
- Output is Input
- Increase Information Density
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15
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- Position (2)
- Orientation (1)
- Size (spatial frequency)
- Motion (2)++
- Blinking?
- Color (3)
- Accuracy Ranking for
Quantitative Perceptual Tasks
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16
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17
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- Interaction Responsiveness
- “0.1” second
- Perception of Motion
- Perception of Cause & Effect
- “1.0” second
- “10” seconds
- Pace of routine cognitive task
- Mapping Data to Visual Form
- Variables Mapped to “Visual Display”
- Variables Mapped to “Controls”
- “Visual Display” and “Controls” Linked
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18
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- "Spatial" Data
- Has inherent 1-, 2- or 3-D geometry
- MRI: density, with 3 spatial attributes, 3-D grid connectivity
- CAD: 3 spatial attributes with edge/polygon connections, surface
properties
- Abstract, N-dimensional Data
- Challenge of creating intuitive mapping
- Chernoff Faces
- Software Visualization: SeeSoft
- Scatterplot and Dimensional Stacking
- Parallel Coordinates and Table Lens
- Hierarchies: Treemaps, Brain,Hyperbolic Tree
- Boolean Query: Filter-Flow, InfoCrystal
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19
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20
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21
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22
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- Scatterplot and Dimensional Stacking
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23
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- Parallel Coordinates by Isenberg
(IBM)
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24
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- Software Visualization - SeeSoft
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25
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26
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27
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- Hierarchy Visualization - ConeTree
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28
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- Hyperbolic Tree è Demo
- http://www.inxight.com/products/sdks/st/
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29
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- http://www.inxight.com/products/sdks/tl/
- Select Ameritrade TableLens
- See if you can find
- The Largest loss value for a fund for this Quarter
Hint: greater than -30.0
- The Largest ten year gain. Hint: greater than +900.0
- The Largest five year gain. Hint: a Lipper fund
- See what else you can find...
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30
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31
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- Document Visualization - ThemeView
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32
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- Xerox PARC
- University of Maryland & Shneiderman
- AT&T Bell Labs & Lucent
- TheBrain
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33
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- Direct Manipulation
- Immediate Feedback
- Linked Displays
- Dynamic Queries
- Tight Coupling Output è
Input
- Overview è Zoom+Filter è Details-on-demand
- Provide Context + Focus
- Animate Transitions
- Increase Information Density
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