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


System schematics, like those used for electrical or hydraulic systems, can be massive and complex. Fisheye techniques can help navigate such large documents by maintaining the context around attention region, but the distortion introduced by ancient fisheye techniques can impair the readability of the diagram. We tend to present SchemeLens, a vector-based, topology-aware fisheye technique that aims to keep up the readability of the diagram. Vector-based scaling reduces distortion to parts, however distorts layout. We tend to gift several strategies to reduce this distortion by using the structure of the topology, together with orthogonality and alignment, and a model of user intention to foster smooth and predictable navigation. We tend to evaluate this approach through 2 user studies: Results show that (1) SchemeLens is 16-27% faster than each spherical and rectangular flat-prime fisheye lenses at finding and identifying a targ et alng one or several paths in a very network diagram; (2) augmenting SchemeLens with a model of user intentions aids in learning the network topology.

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