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

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.