Authors: Aurélie Cohe, Bastien Liutkus, Gilles Bailly, James Eagan, Eric Lecolinet
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 to find and identify a target along one or several paths in a network diagram; (2) augmenting SchemeLens with a lens adapting itself to user intentions aids in learning the network topology.