mao lin huang university of technology, sydney,

57
Mao Lin Huang University of Technology, Sydney, Visual Representations of Data and Knowledge

Upload: ariel-doyle

Post on 01-Jan-2016

32 views

Category:

Documents


2 download

DESCRIPTION

Visual Representations of Data and Knowledge. Mao Lin Huang University of Technology, Sydney,. Rendering Effective Route Maps. General 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) - PowerPoint PPT Presentation

TRANSCRIPT

Page 1: Mao Lin Huang University of Technology, Sydney,

Mao Lin Huang

University of Technology, Sydney,

Visual Representations of Data and Knowledge

Page 2: Mao Lin Huang University of Technology, Sydney,

2

Rendering Effective Route Maps

Page 3: Mao Lin Huang University of Technology, Sydney,

3

General 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

Page 4: Mao Lin Huang University of Technology, Sydney,

4

More Specifically Constant scale factor

Road lengths on a conventional map vary in several orders of magnitude => 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

Page 5: Mao Lin Huang University of Technology, Sydney,

5

Generalization 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 doesn’t need to know roads shape. Simpler roads are easier to differentiate on a map.

Page 6: Mao Lin Huang University of Technology, Sydney,

6

Demo at mapblast.com

Page 7: Mao Lin Huang University of Technology, Sydney,

7

Simple Visualization Model

Data View PortVisual Mapping

Page 8: Mao Lin Huang University of Technology, Sydney,

8

Film Data Table Example: Attributes

Page 9: Mao Lin Huang University of Technology, Sydney,

9

Visual Mapping Define a Space Map: data marks Map: data attributes graphical mark attributes

Year X Length Y Popularity size Subject color Award? shape

Page 10: Mao Lin Huang University of Technology, Sydney,

10

Example: FilmFinder

38

Page 11: Mao Lin Huang University of Technology, Sydney,

11

Example: FilmFinder

39

Page 12: Mao Lin Huang University of Technology, Sydney,

12

Use of graphical time scales as an approach to visualize histories. [Time Scale + History = Intuitive]

Page 13: Mao Lin Huang University of Technology, Sydney,

13

Page 14: Mao Lin Huang University of Technology, Sydney,

14

Page 15: Mao Lin Huang University of Technology, Sydney,

15

Patient Records

Page 16: Mao Lin Huang University of Technology, Sydney,

16

Galaxies Projection of clustering algorithms into 2D Galaxies are clusters of related data Proximity of galaxies is relevant Designed to add temporal patterns to

clustering

Page 17: Mao Lin Huang University of Technology, Sydney,

17

Galaxies

Page 18: Mao Lin Huang University of Technology, Sydney,

3D Visualization & VR Techniques

Page 19: Mao Lin Huang University of Technology, Sydney,

19

3D Cone Tree

16

Page 20: Mao Lin Huang University of Technology, Sydney,

20

3D Cone Trees

research.microsoft.com/~ggr/gi97.ppt 17

Page 21: Mao Lin Huang University of Technology, Sydney,

21

Perspective Wall

research.microsoft.com/~ggr/gi97.ppt 18

Page 22: Mao Lin Huang University of Technology, Sydney,

22

Example: 3D-Room (The Exploratory)

Robertson, Card, and Mackinlay (1989) 20

Page 23: Mao Lin Huang University of Technology, Sydney,

23

3D Navigation Task (Hallway)

research.microsoft.com/~ggr/gi97.ppt 21

Page 24: Mao Lin Huang University of Technology, Sydney,

24

3D GUI for Web Browsing

22

Page 25: Mao Lin Huang University of Technology, Sydney,

25

3D GUI for Web Browsing

http://research.microsoft.com/ui/TaskGallery/index.htm 23

Page 26: Mao Lin Huang University of Technology, Sydney,

26

Web Forager

http://research.microsoft.com/ui/TaskGallery/index.htm 24

Page 27: Mao Lin Huang University of Technology, Sydney,

27

WebBook

research.microsoft.com/~ggr/gi97.ppt 25

Page 28: Mao Lin Huang University of Technology, Sydney,

28

3D GUI for Desktop

http://research.microsoft.com/ui/TaskGallery/index.htm 26

Page 29: Mao Lin Huang University of Technology, Sydney,

29

Page 30: Mao Lin Huang University of Technology, Sydney,

30

ThemeScape 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

Page 31: Mao Lin Huang University of Technology, Sydney,

31

ThemeScape

Page 32: Mao Lin Huang University of Technology, Sydney,

32

ThemeScape

Page 33: Mao Lin Huang University of Technology, Sydney,

33

Calendar Based Visualization Using 3 dimensions

X-axis: Time of day Y-axis: Days of data period Z-axis: Univariate data samples

Page 34: Mao Lin Huang University of Technology, Sydney,

34

Calendar Based Visualization

Page 35: Mao Lin Huang University of Technology, Sydney,

35

Calendar Based Visualization

Page 36: Mao Lin Huang University of Technology, Sydney,

36

Graph-Driven Visualization of Relational DataGraph-Driven Visualization of Relational Data

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.

Graph VisualizationGraph Visualization

Page 37: Mao Lin Huang University of Technology, Sydney,

37

Classical Graph Layouts Link-node diagrams Layout algorithms (graph drawing) Geometric positioning of nodes & edges Small amount of nodes Avoid node overlaps Reduce edge crossings

hierarchical force-directed orthogonal

symmetric

radial layout

Page 38: Mao Lin Huang University of Technology, Sydney,

38

Using 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).

Page 39: Mao Lin Huang University of Technology, Sydney,

39

Fish-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.

Page 40: Mao Lin Huang University of Technology, Sydney,

40

3D Graph DrawingSGI fsn file-system viewer

Image from:

http://www.sgi.com/fun/images/fsn.map2.jpg

Page 41: Mao Lin Huang University of Technology, Sydney,

Trees

Page 42: Mao Lin Huang University of Technology, Sydney,

42

2 Approaches Connection (node & link)

Enclosure (node in node)

Structure vs. attributes Attributes only (multi-dimensional viz) Structure only (1 attribute, e.g. name) Structure + attributes

A

CB

A

B C

Page 43: Mao Lin Huang University of Technology, Sydney,

43

Containment Approach

Page 44: Mao Lin Huang University of Technology, Sydney,

44

Treemaps (Shneiderman)

Slice and Dice Alternate horizontal and

vertical cuts for levels

Node area node attribute Zoom onto nodes

Space-Filling Structure + 3 attributes

Area, color, label

Page 45: Mao Lin Huang University of Technology, Sydney,

45

Treemaps

Page 46: Mao Lin Huang University of Technology, Sydney,

46

Balanced trees

Page 47: Mao Lin Huang University of Technology, Sydney,

47

Treemaps ~ 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

Page 48: Mao Lin Huang University of Technology, Sydney,

48

Treemap 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)

Page 49: Mao Lin Huang University of Technology, Sydney,

49

Cushion Treemaps

Page 50: Mao Lin Huang University of Technology, Sydney,

50

Squared Treemaps

Page 51: Mao Lin Huang University of Technology, Sydney,

51

Treemaps on the Web Map of the Market: http://www.smartmoney.com/marketmap/ People Map: http://www.truepeers.com/ Coffee Map: http://www.peets.com/tast/11/coffee_selector.asp

Page 52: Mao Lin Huang University of Technology, Sydney,

52

DiskMapper http://www.miclog.com/dmdesc.htm

Page 53: Mao Lin Huang University of Technology, Sydney,

53

2D Tree Drawing (web sitemap)

MosiacG SystemZyers and Stasko

Image from:http://www.w3j.com/1/ayers.270/paper/270.html

Page 54: Mao Lin Huang University of Technology, Sydney,

54

PDQ Trees Overview+Detail of 2D layout Dynamic Queries on each level for pruning

Page 55: Mao Lin Huang University of Technology, Sydney,

55

Space-Optimized Tree Layout

A large data set of approximately 50 000 nodes My Unix root with approx. 3700 directories and files

Page 56: Mao Lin Huang University of Technology, Sydney,

56

Hyperbolic treeThe hyperbolic browser technique performs fish-eye viewing with animated transitions to preserve the user’s mental map. It changes both the viewing area and the zoomed focus point.

Page 57: Mao Lin Huang University of Technology, Sydney,

57

H3

Image from: http://graphics.stanford.edu/papers/h3/fig/nab0.gif