Handling Visual Complexity

August 18, 2011 § Leave a comment

The scaling problem is ubiquitous in computer science, including visualisation. We must consider both a visualisation’s Efficiency (computational complexity), as well as it’s Effectiveness (visual complexity).

There are three approaches to handling visual complexity;

  1. 3 dimensions. (though this doesn’t necessarily help that much due to occlusion and unnatural navigation issues)
  2. Interaction. (allowing for the spread of data over another dimension such as time)
  3. Clustering. (viewing abstractions of the data)
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Interaction Flow is important, and it is essential that the cognitive complexity of the navigating visualisation, is minimised with a simplistic mental map. In order to preserve the mental map, the cognitive difference between Keyframes should be small. These guidelines suggest how this cognitive difference can be minimised, by preserving:
  1. Bearings.
  2. Topology.
  3. Proximity.
(One simple algorithm that follows these guidelines is connecting the keyframes with springs).
(Another simple, but inelegant method of animating in-between frames is using linear interpolation, though this doesn’t work well with abstract data. This is where Affine Transformations come in (rotation, translation, stretch), a few of these can be followed easily by humans simultaneously. This can be implemented through least squares approximation. Multiple affines can also be computed through clustering like k-means.
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Clustering abstracts the data up a level or two.
Clustered Graph = (G,T), where G = Classical Graph, T = Tree.
‘Precis’ (summaries) are required, groups of nodes under a set level, an anti-chain.
Basic human interaction to be implemented includes manual ‘drilling down’, followed by automatic ‘drilling up’ (which can be the ‘least recently used’ or ‘lowest degree of interest’ item).
  • How do you do this geometrically? This is a tough and interesting area, expanding the layout (and compacting areas) whilst maintaining a mental map.
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