Interactive Information Visualization of One Million Items

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Interactive Information Visualization ofOne
Million Items

• Jean-Daniel Fekete
• University of Maryland

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Scaling issues in Information Visualization

• Seeing more data items or more dimensions
• No aggregation, no sampling
• What are the limits?
• Technical
• screen resolution / dimension, 10ms redisplay
  speed
• Perceptual
• visual system accuracy, perception-action loop
  speed
• Cognitive
• how much can we understand and how long does it
  take?

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Visualizing one million items

• Treemap of a Unix file system containing 1
  million files
• Rectangle sizes related to file sizes
• Color coded by type redexecutable,
  bluetext,greenimage, yellowprogram,
  grayunknown
• What can we see?

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Techniques

• Use accelerated graphics with OpenGL
• 2GHz Pentium4
• 1600x1200 pixels resolution
• Now off-the-shelf!
• Push existing visualization techniques to their
  limits
• Space filling (treemaps)
• Overlapping (scatter plots)

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Relying onAccelerated Graphics

• Balance the CPU/GPU work
• GPU can perform many operations for free
• Geometric transformations
• Color transformations
• Color interpolation
• Translucency
• Counting overlaps
• CPU prepares data and sends it to GPU
• Bottleneck is communication

CPU
GPU
Screen
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Relying onAccelerated Graphics

• Breaks the 106 barrier
• 1 million items at interactive speed
• Permits use of animation
• E.g. for understanding view transitions
• But requires
• optimizing algorithms
• using unusual programming techniques
• adapting visualization techniques

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Example of Adapted Visualization Techniques

• No rectangle outlines
• Spares pixels
• Avoids sending the geometry twice
• Color shading
• Separate similar items
• Free with accelerated graphics cards

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Animated Transitions
 












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Dynamic Labeling
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Conclusion

• You can now break the 106 barrier!
• Was limited to 104
• E.g. can visualize the phylogenic tree of species
• Still technically limited by graphics hardware,
  but close to the perceptual limits
• New IBM screen with 10 million pixels
• Need more work to understand how humans can make
  sense of this amount of data
• Send your 106 data sets!

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Credits

• Thanks to HCIL for inviting me and providing the
  rich environment for this work
• Thanks to Catherine Plaisant, Ben Shneiderman and
  Ben Bederson for their help and advice
• www.cs.umd.edu/hcil/millionvis