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

Area of Science:

• Computer Science
• Human-Computer Interaction
• Information Visualization

Background:

• System schematics are often large and complex, making navigation difficult.
• Traditional fisheye techniques can distort diagrams, impairing readability.
• Efficient navigation of complex visual information is crucial for understanding system layouts.

Purpose of the Study:

• To present SchemeLens, a novel vector-based, topology-aware fisheye technique.
• To improve the readability and navigation of large system schematics.
• To reduce distortion while maintaining contextual overview in diagrams.

Main Methods:

• Developed a vector-based fisheye technique (SchemeLens) that is topology-aware.
• Incorporated strategies using topology structure (orthogonality, alignment) to reduce layout distortion.
• Integrated a model of user intention to facilitate smoother navigation.
• Evaluated SchemeLens through two user studies.

Main Results:

• SchemeLens demonstrated 16-27% faster performance in finding targets within network diagrams compared to traditional fisheye lenses.
• Augmenting SchemeLens with user intention modeling improved learning of network topology.
• The technique successfully balances component scaling with layout preservation.

Conclusions:

• SchemeLens offers a significant improvement for navigating complex system diagrams.
• The topology-aware and vector-based approach enhances both speed and accuracy in information retrieval.
• User intention modeling is a promising direction for intuitive diagram navigation systems.