2003.04.10CHI 2003 – Visualization & Navigation1 Efficient User Interest Estimation in Fisheye...

2003.04.10 CHI 2003 – Visualization & Navigation 1

Efficient User Interest Efficient User Interest Estimation in Fisheye ViewsEstimation in Fisheye Views

Jeffrey Heer and Stuart K. Card1 Palo Alto Research Center, Inc.2 University of California, Berkeley

1,2 1


RoadmapRoadmap

Motivation + Background Implementation Evaluation Conclusion


Fisheye ViewsFisheye Views


Degree of Interest (DOI)Degree of Interest (DOI)

Models user’s spontaneous interest across the tree– This model can then be used to inform presentation

-3 -3 -3 -3 0 -1 -2 -2

-2 -2 0 -1

-1

0

0

Computed Degree of Interest

0 -1

0 -1

-1

0

0

Cull low Degree of Interest


User Modeling in Fisheye ViewsUser Modeling in Fisheye Views

6 6 6 6 0 2 4 4

5 5 1 3

4

3

2

Degree of Interest Intrinsic Importance + Distance from Point of Interest

3 3 3 3 3 3 3 3

2 2 2 2

1

0

1

Intrinsic Importance Distance from Point of Interest


DEMODEMO


… … the need for speedthe 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.


Naïve Interest ComputationNaïve Interest Computation

-3 -3 -3 -3 0 -1 -2 -2

-2 -2 0 -1

-1

0

0

Requires visiting the entire tree!


Least Common Ancestor PruningLeast Common Ancestor Pruning

-3 -3 -3 0 -1 -2 -2

-2 -2 0 -1

-1

0

0

-2 -2 0 -1

-1 0

0

-3-3

Limit computation to the subtree rooted at least common ancestor.

However, no savings if new focus is here

Furthermore, this method exploits a specific DOI distribution not necessarily generalizable


Solution: Disinterest ThresholdingSolution: Disinterest Thresholding

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

-2 -2 -2 -2 0 -1 -2 -2

-2 -2 0 -1

-1

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Computed DOI: minDOI = -1

0 -1

0 -1

-1

0

0

Cull low Degree of Interest


Disinterest ThresholdingDisinterest Thresholding

-2 -2 -2 -2 0 -1 -2 -2

-2 -2 0 -1

-1

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0


Node Attribute RegistryNode 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 … …..


EvaluationEvaluation

0.000

0.020

0.040

0.060

0.080

0.100

0.120

0.140

1 10 100 1000 10000 100000 1000000

Number of Nodes

DO

I Cal

cula

tio

n T

ime

(sec

)

naïve

least common ancestor

disinterest

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.


LimitationsLimitations

Doesn’t 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.


Thanks!!Thanks!! Questions? Questions?

Jeffrey Heer [email protected]

Stuart K. Card [email protected]

0 -1 -2 -2

0 -1

0


MotivationMotivation

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.

Amount ofAccessibleKnowledge

Amount ofAmount ofAccessibleAccessibleKnowledgeKnowledge

Cost [Time]Cost [Time]Cost [Time]

Amount ofAccessibleKnowledge

Amount ofAmount ofAccessibleAccessibleKnowledgeKnowledge

Cost [Time]Cost [Time]Cost [Time]


Information VisualizationInformation Visualization

1.2 b/s (Reading)2.3 b/s (Pictures)

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


Node Attribute RegistryNode 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!


Possible QuestionsPossible Questions

What about other DOI distributions?– examples?

2003.04.10CHI 2003 – Visualization & Navigation1 Efficient User Interest Estimation in Fisheye...

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