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

Aurélie Cohé, Bastien Liutkus, Gilles Bailly

IEEE Transactions on Visualization and Computer Graphics

|September 22, 2015

Summary

This summary is machine-generated.

SchemeLens improves navigation of complex system diagrams by reducing distortion. This novel fisheye technique enhances readability and speeds up target identification in network diagrams.

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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.