Information Understanding
Benjamin B. Bederson
What is the Problem?
University of Maryland, Human-Computer Interaction Laboratory
 How to perceive and interact with information?
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Detect patterns and outliers
Find details without losing global context
Concentrate on task (stay “in the flow”)
 How to scale up to large information sets?
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Technical problems
Perceptual limitations
Design problems
External Cognition
University of Maryland, Human-Computer Interaction Laboratory
 Step 1: Recognize
human limitations
Human Visual Perception
University of Maryland, Human-Computer Interaction Laboratory
 Step 2: Don’t underestimate the human brain
Interaction
University of Maryland, Human-Computer Interaction Laboratory
 Step 3: Add interaction. If a picture is
worth a thousand words, an interactive
interface is worth a thousand pictures.
Scaling Up
University of Maryland, Human-Computer Interaction Laboratory
 How do you show more than fits
on the screen?
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traditional
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new
paradigms
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abstract
link (web)
scroll (long documents)
overview+detail (e.g., photoshop)
zoom (PhotoMesa demo)
fisheye distortion (FishCal demo)
www.cs.umd.edu/hcil/photomesa
www.cs.umd.edu/hcil/fishcal
What is holding us back?
University of Maryland, Human-Computer Interaction Laboratory
 Performance and computational requirements
 Increased power, but increased effort
 Requires learning new approaches
 => Better performance isn’t enough. We must
offer much better performance without many
extra costs.
Information Understanding
University of Maryland, Human-Computer Interaction Laboratory
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Visualizing Millions of Items
Web-Based Surveys
Simulation in Engineering
Time Series Data
Bioinformatics Visualization
Browsing Large Trees
Data Monitoring with Treemaps
Interactive Web Maps