Data Cubes and ZoomGraphs

Chris Gray

cmg@cs.ubc.ca

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IntroZoom graphs

Data cubes

Examples

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Polaris ReviewDatabase exploration.

A formal specificationlanguage for describingvisualizations ofdatabase tables.

An IDE for generatingthe specifications.

A database query gen-eration system to getthe data to be visual-ized.

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Big Picture

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Zoom GraphsLike a space/scale diagram, but discrete.

Going from node to node via an edgerepresents a zoom.

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Differences fromZUIs

Not intended as a desktop replacement.

Multiple dimensions.

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n-dimensional cubesDimensions and measures.

One dimension for each variable.

Each variable has a “measure” – itsmagnitude.

Each dimension can be at a different level ofdetail.

Ordinal data must be given an ordering.

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Lattice≤ relation between some cubes.

Defined by a higher level of detail in onedimension.

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Visual EncodingsPossible to specify visualencodings completely in thegraphs.

ColorQuantitative ramp,qualitative.

SizeHeight, width, or both.

ShapeText, point, line, orpolygon.

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LayersAs user zooms in, more layers emerge.

Labels appear when user is close enough.Aggregated data become separated.

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Example

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ProblemsContinuous zooms are hard to do with thisabstraction.

Design decision.

Arbitrary zooms are not possible.

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ConclusionsData cubes

Zoom graphs

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Questions?

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References

[1] Chris Stolte, Diane Tang and Pat Hanrahan, “MultiscaleVisualization Using Data Cubes”, Proceedings of theEighth IEEE Symposium on Information Visualization, Oc-tober 2002.

[2] Chris Stolte, Diane Tang and Pat Hanrahan, “Polaris”,http://graphics.stanford.edu/projects/polaris/

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