SchemeLens: A Content-Aware Vector-Based Fisheye Technique for Navigating Large Systems Diagrams

Abstract :

System schematics, such as those used for electrical or hydraulic systems, can be large and complex. Fisheye techniques can help navigate such large documents by maintaining the context around a focus region, but the distortion introduced by traditional fisheye techniques can impair the readability of the diagram. We present SchemeLens, a vector-based, topology-aware fisheye technique which aims to maintain the readability of the diagram. Vector-based scaling reduces distortion to components, but distorts layout. We present several strategies to reduce this distortion by using the structure of the topology, including orthogonality and alignment, and a model of user intention to foster smooth and predictable navigation. We evaluate this approach through two user studies: Results show that (1) SchemeLens is 16–27% faster than both round and rectangular flat-top fisheye lenses at finding and identifying a target along one or several paths in a network diagram; (2) augmenting SchemeLens with a model of user intentions aids in learning the network topology.

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Aurélie Cohé, Bastien Liutkus, Gilles Bailly, James Eagan, Eric Lecolinet. SchemeLens: A Content-Aware Vector-Based Fisheye Technique for Navigating Large Systems Diagrams. IEEE Transactions on Visualization and Computer Graphics, Institute of Electrical and Electronics Engineers, 2016, 22 (1), pp.330-338. ⟨10.1109/TVCG.2015.2467035⟩. ⟨hal-01442946⟩

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