Efficient%20User%20Interest%20Estimation%20in%20Fisheye%20Views

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Efficient User Interest Estimation in Fisheye
Views

• Jeffrey Heer and Stuart K. Card
• 1 Palo Alto Research Center, Inc.
• 2 University of California, Berkeley

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Roadmap

• Motivation Background
• Implementation
• Evaluation
• Conclusion

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Fisheye Views
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Degree of Interest (DOI)

• Models users spontaneous interest across the
  tree
• This model can then be used to inform presentation

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Computed Degree of Interest
Cull low Degree of Interest
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User Modeling in Fisheye Views
Degree of Interest ? Intrinsic Importance
Distance from Point of Interest

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Intrinsic Importance
Distance from Point of Interest
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DEMO
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the need for speed

• Visualization should respond fluidly to user
  actions
• But for each interaction, may have to
• Recompute DOI
• Recompute Layout
• Hard time limit 100ms (Card, Moran, Newell)
• Goal
• Limit all computations to the number of displayed
  nodes.

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Naïve Interest Computation

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Requires visiting the entire tree!
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Least Common Ancestor Pruning
Limit computation to the subtree rooted at least
common ancestor.
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However, no savings if new focus is here
Furthermore, this method exploits a specific DOI
distribution ? not necessarily generalizable
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Solution Disinterest Thresholding

• Saturate DOI function at a disinterest threshold
• Compute DOI only for visible nodes
• Use thresholding to supply defaults for the others

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Cull low Degree of Interest
Computed DOI minDOI -1
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Disinterest Thresholding
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Node Attribute Registry

• Backing array data structure table of node
  attributes.
• Tag visible nodes with table index. When
  attributes are needed (e.g. node.getX()), the
  table is consulted.
• If the node is in the table, the attribute is
  simply returned.
• Else, the suitable default is supplied
• DOI minimum DOI, Position position of first
  visible ancestor

index dirty DOI x y size color etc
0 1 0 213 12 5 ..
1 1 -1 134 58 4 ..
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Evaluation
Setup Time walks through algorithmically
generated DOITrees with increasing tree
depths. Test System PIII 1GHz, 256MB RAM 16 MB
Video RAM DOI Threshold -2
Naïve and LCA grow linearly with the number of
nodes. Disinterest thresholding grows linearly
with number of visible nodes, which in this case
grows logarithmically with total number of nodes.
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Limitations

• Doesnt improve cases where there are a large
  number (10,000) visible nodes.
• Smooth interaction also dependent on the use of
  efficient layout algorithms.
• Only approximates DOI distribution, which may be
  problematic if applications wish to use DOI for
  more than visualization.

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Thanks!! Questions?

• Jeffrey Heer jheer_at_parc.com
• Stuart K. Card card_at_parc.com

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Motivation

• The real design problem is not increased access
  to information, but greater efficiency in finding
  useful information.
• Increasing the rate at which people can find and
  use relevant information improves human
  intelligence.

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Information Visualization

• Leverage highly-developed human visual system to
  achieve rapid understanding of abstract
  information.

1.2 b/s (Reading) 2.3 b/s (Pictures)
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Node Attribute Registry

• DOI function only sets DOI for nodes above the
  disinterest threshold.
• Nodes are transparently added to registry when
  DOI is set.
• If node is already there, then dirty bit is set.
• Registry is resized as necessary.
• After DOI computation, non-dirty nodes are
  removed from registry, dirty bits are cleared.
• Result DOI computation time proportional to the
  number of nodes displayed!

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Possible Questions

• What about other DOI distributions?
• examples?