Title: Mao Lin Huang
1Visual Representations of Data and Knowledge
- Mao Lin Huang
- University of Technology, Sydney,
2Rendering Effective Route Maps
3General Idea
- Automatically generate a route map that has the
same properties as a hand drawn map. - Hand drawn maps
- Exaggerated Lengths (non-constant scale factor)
- No irrelevant information
4More Specifically
- Constant scale factor
- Road lengths on a conventional map vary in
several orders of magnitude gt small roads and
neighborhoods are hard to navigate with large
maps - Information irrelevant to navigation
- Names of locations, places, cities, etc. that are
all far away from the route - Takes up space that would be otherwise useful for
showing crossroads and relevant landmarks
5Generalization Techniques
- Generalize Length
- Use more space for short roads, less for longer
ones. Distribute based on importance, not
physical length - Generalize Angle
- Align roads or make room for others
- Generalize Shape
- Navigator doesnt need to know roads shape.
- Simpler roads are easier to differentiate on a
map.
6Demo at mapblast.com
7Simple Visualization Model
Data
View Port
Visual Mapping
8Film Data Table Example
Attributes
9Visual Mapping
- Define a Space
- Map data ? marks
- Map data attributes ? graphical mark attributes
- Year ? X
- Length ? Y
- Popularity ? size
- Subject ? color
- Award? ? shape
10Example FilmFinder
38
11Example FilmFinder
39
12- Use of graphical time scales as an approach to
visualize histories. Time Scale History
Intuitive
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15Patient Records
16Galaxies
- Projection of clustering algorithms into 2D
- Galaxies are clusters of related data
- Proximity of galaxies is relevant
- Designed to add temporal patterns to clustering
17Galaxies
183D Visualization VR Techniques
193D Cone Tree
16
203D Cone Trees
17
research.microsoft.com/ggr/gi97.ppt
21Perspective Wall
18
research.microsoft.com/ggr/gi97.ppt
22Example 3D-Room (The Exploratory)
20
Robertson, Card, and Mackinlay (1989)
233D Navigation Task (Hallway)
research.microsoft.com/ggr/gi97.ppt
21
243D GUI for Web Browsing
22
253D GUI for Web Browsing
http//research.microsoft.com/ui/TaskGallery/index
.htm
23
26Web Forager
http//research.microsoft.com/ui/TaskGallery/index
.htm
24
27WebBook
research.microsoft.com/ggr/gi97.ppt
25
283D GUI for Desktop
http//research.microsoft.com/ui/TaskGallery/index
.htm
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30ThemeScape
- Abstract 3D landscape of information
- Reduce cognitive load using terrain
- Elevation, colour encode theme strength
redundantly - Landscape metaphor translates well
- Peaks are easy to recognize
- Interesting characteristics include ridges and
valleys
31ThemeScape
32ThemeScape
33Calendar Based Visualization
- Using 3 dimensions
- X-axis Time of day
- Y-axis Days of data period
- Z-axis Univariate data samples
34Calendar Based Visualization
35Calendar Based Visualization
36Graph-Driven Visualization of Relational Data
Graph Visualization
An example of visualizing relational data. This
is the visualization of a family tree (graph).
Here each image node represents a person and the
edges represent relationships among these people
in a large family.
37Classical Graph Layouts
- Link-node diagrams
- Layout algorithms (graph drawing)
- Geometric positioning of nodes edges
- Small amount of nodes
- Avoid node overlaps
- Reduce edge crossings
radial layout
symmetric
force-directed
hierarchical
orthogonal
38Using a very large virtual page
The virtual page technique predefines the drawing
of the whole graph, and then provides a small
window and scroll bar to allow the user to
navigate through it (by changing the viewing
area).
39Fish-eye views
The fish-eye technique can keep a detailed
picture of a part of a graph as well as the
global context of the graph. It changes the
zoomed focus point.
403D Graph Drawing
SGI fsn file-system viewer Image
from http//www.sgi.com/fun/images/fsn.map2.jpg
41Trees
422 Approaches
A
- Connection (node link)
- Enclosure (node in node)
- Structure vs. attributes
- Attributes only (multi-dimensional viz)
- Structure only (1 attribute, e.g. name)
- Structure attributes
C
B
A
B
C
43Containment Approach
44Treemaps (Shneiderman)
- Slice and Dice
- Alternate horizontal andvertical cuts for levels
- Node area ? node attribute
- Zoom onto nodes
- Space-Filling
- Structure 3 attributes
- Area, color, label
45Treemaps
46Balanced trees
47Treemaps
- 1000 nodes
- Quantitative attributes
- Good combination of structure attributes
- For unbalanced trees, structure more difficult
- Learning time 20 min
- Evaluation major performance boost over outliner
- Bad aspect ratios long narrow rectangles
- Large scale or deep causes solid black
48Treemap Algorithm
- Calculate sizes
- Recurse to children
- My size sum children sizes
- Draw Treemap (node, space, direction)
- Draw node rectangle in space
- Alternate direction
- For each child
- Calculate child space as of node space using
size and direction - Draw Treemap (child, child space, direction)
49Cushion Treemaps
50Squared Treemaps
51Treemaps on the Web
- Map of the Market http//www.smartmoney.com/mark
etmap/ - People Map http//www.truepeers.com/
- Coffee Map http//www.peets.com/tast/11/coffee_s
elector.asp
52DiskMapper
- http//www.miclog.com/dmdesc.htm
532D Tree Drawing (web sitemap)
MosiacG System Zyers and Stasko Image
from http//www.w3j.com/1/ayers.270/paper/270.htm
l
54PDQ Trees
- OverviewDetail of 2D layout
- Dynamic Queries on each level for pruning
55Space-Optimized Tree Layout
A large data set of approximately 50 000 nodes
My Unix root with approx. 3700 directories and
files
56Hyperbolic tree
The hyperbolic browser technique performs
fish-eye viewing with animated transitions to
preserve the users mental map. It changes both
the viewing area and the zoomed focus point.
57H3
Image from http//graphics.stanford.edu/papers/h3
/fig/nab0.gif