1. Information Understanding Benjamin B. Bederson

2. University of Maryland, Human-Computer Interaction Laboratory What is the Problem?  How to perceive and interact with information? 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? Technical problems Perceptual limitations Design problems

3. University of Maryland, Human-Computer Interaction Laboratory External Cognition  Step 1: Recognize human limitations

4. University of Maryland, Human-Computer Interaction Laboratory Human Visual Perception  Step 2: Don’t underestimate the human brain

5. University of Maryland, Human-Computer Interaction Laboratory Interaction  Step 3: Add interaction. If a picture is worth a thousand words, an interactive interface is worth a thousand pictures.

6. University of Maryland, Human-Computer Interaction Laboratory Scaling Up  How do you show more than fits on the screen? abstract link (web) scroll (long documents) overview+detail (e.g., photoshop) zoom (PhotoMesa demo) fisheye distortion (FishCal demo) traditional new paradigms www.cs.umd.edu/hcil/photomesa www.cs.umd.edu/hcil/fishcal

7. University of Maryland, Human-Computer Interaction Laboratory What is holding us back?  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.

8. University of Maryland, Human-Computer Interaction Laboratory Information Understanding 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