All Figures

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  Figure 1.1: Range of specificity.

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  Figure 1.2: System scalability and dataset size.

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  Figure 3.1: Constructing a spanning tree for quasi-hierarchical web site.

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  Figure 3.2: Constructing a spanning tree for quasi-hierarchical function call graph.

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  Figure 3.3: Exponential volume of hyperbolic space.

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  Figure 3.4: Euclidean equidistant parallels vs. hyperbolic divergent parallels.

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  Figure 3.5: Models of hyperbolic space.

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  Figure 3.6: 1D hyperbolic projection.

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  Figure 3.7: 2D hyperbolic projection.

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  Figure 3.8: Circumference vs. hemisphere.

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  Figure 3.9: Discs vs. spherical

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  Figure 3.10: Circle packing.

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  Figure 3.11: Band layout.

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  Figure 3.12: Child hemisphere placement.

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  Figure 3.13: Active vs. idle frames, obvious case.

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  Figure 3.14: Active vs. idle frames, subtle case.

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  Figure 3.15: Site Manager.

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  Figure 3.16: Linked views.

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  Figure 3.17: Traffic log playback.

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  Figure 3.18: Hyperbolic motion over a 30,000 element Unix file system

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  Figure 3.19: Non-tree links.

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  Figure 3.20: Part of the Stanford graphics group web site drawn as a graph in 3D hyperbolic space.

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  Figure 3.21: Call graph.

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  Figure 3.22: Link structure of a web site laid out in 3D hyperbolic space.

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  Figure 3.23: XML3D interface.

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  Figure 3.24: XML3D vs. 2D interface study results.

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  Figure 3.25: Autonomous System paths analysis.

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  Figure 4.1: Transport protocols.

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  Figure 4.2: Tunnelling.

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  Figure 4.3: MBone shown as arcs on a globe.

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  Figure 4.4: Horizon view, arc height and grouping.

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  Figure 4.5: Textured globes.

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  Figure 4.6: Thresholding.

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  Figure 4.7: Two regional closeup views of the MBone.

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  Figure 4.8: MBone tunnels grouped by backbone status.

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  Figure 4.9: MBone tunnels of the major backbone networks, colored by provider.

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  Figure 4.10: MBone tunnel structure in Texas at two different times.

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  Figure 5.1: Parsed definition graph from MindNet.

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  Figure 5.2: Previously existing textual view in MindNet.

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  Figure 5.3: Traditional layouts avoid crossings to prevent false attachments.

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  Figure 5.4: Plausibility gradient encodes a domain-specific attribute.

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  Figure 5.5: Selective emphasis avoids perception of false attachments.

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  Figure 5.6: Paths in grids.

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  Figure 5.7: Bad approaches.

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  Figure 5.8: Curvilinear grid.

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  Figure 5.9: Attaching definitions to path segments.

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  Figure 5.10: Definition graph layout.

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  Figure 5.11: Very early layout with empty proxy slots.

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  Figure 5.12: Early sparse layout.

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  Figure 5.13: Adjusting grid for maximum information density.

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  Figure 5.14: Viewing levels.

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  Figure 5.15: Constellations.

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  Figure 5.16: Relation type constellations.

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  Figure 5.17: Pie flipper.

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  Figure 5.18: Hovering.

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  Figure 5.19: Zooming.

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  Figure 5.20: Three effective viewing levels.

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  Figure 5.21: Dissimilar query words.

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  Figure 5.22: Bird-feather 10 path dataset.

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  Figure 5.23: Layouts of kangaroo-tail dataset using pre-existing systems.

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  Figure 6.1: Visual popout.

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  Figure 6.2: Avoiding visual artifacts from word sizing.

1.1, 1.2
3.1a, 3.1b, 3.1c, 3.1d, 3.1e, 3.1f, 3.1g, 3.1h, 3.2a, 3.2b, 3.2c, 3.2d, 3.2e, 3.2f, 3.3, 3.4, 3.5a, 3.5b, 3.6, 3.7a, 3.7b, 3.8a, 3.8b, 3.8c, 3.9, 3.10, 3.11a, 3.11b, 3.11c, 3.12a, 3.12b, 3.13a, 3.13b, 3.14a, 3.14b, 3.15, 3.16, 3.17, 3.18a, 3.18b, 3.18c, 3.18d, 3.18e, 3.18f, 3.18g, 3.18h, 3.19a, 3.19b, 3.19c, 3.19d, 3.19e, 3.19f, 3.20, 3.21a, 3.21b, 3.22a, 3.22b, 3.22c, 3.23, 3.24, 3.25a, 3.25b, 3.25c, 3.25d
4.1, 4.2, 4.3, 4.4, 4.5a, 4.5b, 4.6a, 4.6b, 4.7a, 4.7b, 4.8a, 4.8b, 4.8c, 4.8d, 4.9a, 4.9b, 4.9c, 4.9d, 4.10a, 4.10b
5.1, 5.2, 5.3a, 5.3b, 5.4, 5.5a, 5.5b, 5.6, 5.7, 5.8, 5.9a, 5.9b, 5.10a, 5.10b, 5.10c, 5.11, 5.12, 5.13, 5.14a, 5.14b, 5.14c, 5.14d, 5.15a, 5.15b, 5.15c, 5.16a, 5.16b, 5.17, 5.18, 5.19a, 5.19b, 5.19c, 5.19d, 5.19e, 5.19f, 5.19g, 5.19h, 5.19i, 5.19j, 5.20a, 5.20b, 5.20c, 5.21, 5.22, 5.23a, 5.23b
6.1a, 6.1b, 6.1c, 6.2a, 6.2b