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Tree Structures Hierarchical Information

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DAG, one parent per node. Items structure (nodes associations) In table model? ... Multi-D tasks, plus structure-based tasks: Find descendants, ancestors, ... – PowerPoint PPT presentation

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Title: Tree Structures Hierarchical Information


1
Tree Structures(Hierarchical Information)
  • cs5764 Information Visualization
  • Chris North

2
Where are we?
  • Multi-D
  • 1D
  • 2D
  • Trees
  • Graphs
  • 3D
  • Document collections
  • Design Principles
  • Empirical Evaluation
  • Visual Overviews

3
Trees (Hierarchies)
  • What is a tree?
  • DAG, one parent per node
  • Items structure (nodes associations)
  • In table model?
  • Add parent pointer attribute
  • 1M

4
Examples
  • File system
  • menus
  • org charts
  • Family tree
  • classification/taxonomy
  • Table of contents
  • data structures

5
Tasks
  • Multi-D tasks, plus structure-based tasks
  • Find descendants, ancestors, siblings, cousins
  • Overall structure, height, breadth, dense/sparse
    areas

6
Tree Properties
  • Structure vs. attributes
  • Attributes only (multi-dimensional viz)
  • Structure only (1 attribute, e.g. name)
  • Structure attributes
  • Branching factor
  • Fixed level, categorical

7
Tree Visualization
  • Example TreeView
  • Why is tree visualization hard?
  • Structure AND items
  • Structure harder, consumes more space
  • Data size grows very quickly (exponential)
  • nodes bheight

8
2 Approaches
  • Connection (node link)
  • outliner
  • Containment (node in node)
  • Venn diagram

A
C
B
A
B
C
9
Connection (node link)
10
TreeView
  • Good for directed search tasks
  • subtree filtering (/-)
  • Not good for learning structure
  • No attributes
  • Apx 50 items visible
  • Lose path to root for deep nodes
  • Scroll bar!

11
Mac Finder
Branching factor Small large
12
Hyperbolic Trees
  • Rao, Hyperbolic Tree
  • http//startree.inxight.com/
  • Xerox PARC
  • Inxight
  • Focuscontext

13
Cone Trees
  • Robertson, ConeTrees
  • Xerox PARC
  • 3D for focuscontext

14
PDQ Trees
  • OverviewDetail of 2D tree layout
  • Dynamic Queries on each level for pruning

15
PDQ Trees
16
Disk Tree
  • Ed Chi, Xerox PARC
  • OverviewReduced visual representation

17
WebTOC
  • Website map TreeView size attributes
  • http//www.cs.umd.edu/projects/hcil/webtoc/fhcil.h
    tml

18
FSN
  • SGI file system navigator
  • Jurassic Park
  • Zooming?

19
Ugh!
20
Containment (node in node)
21
2 Approaches
  • Connection (node link)
  • Outliner
  • Containment (node in node)
  • Venn diagram
  • Structure vs. attributes
  • Attributes only (multi-dimensional viz)
  • Structure only (1 attribute, e.g. name)
  • Structure attributes

A
C
B
A
B
C
22
Pyramids
23
3D Containment
24
Treemaps
  • Shneiderman, Treemaps
  • http//www.cs.umd.edu/hcil/treemap3/
  • Maryland
  • zooming

25
Treemap Algorithm
  • Calculate node sizes
  • Recurse to children
  • node size sum children sizes
  • Draw Treemap (node, space, direction)
  • Draw node rectangle in space
  • Alternate direction (slice or dice)
  • For each child
  • Calculate child space as of node space using
    size and direction
  • Draw Treemap (child, child space, direction)

26
Squarified Treemaps
  • Wattenberg
  • Van Wijk

27
  • http//www.research.microsoft.com/masmith/all_map
    .jpg

28
Cushion Treemaps
  • Van Wijk
  • http//www.win.tue.nl/sequoiaview/

29
Dynamic Query Treemaps
  • http//www.cs.umd.edu/hcil/treemap3/

30
Treemaps 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

31
DiskMapper
  • http//www.miclog.com/dmdesc.htm

32
Sunburst
  • Stasko, GaTech
  • Radial layout
  • Animated zooming

33
Sunburst (vs. Treemap)
  • Faster learning time like pie chart
  • Details outward, instead of inward
  • Focuscontext instead of zooming
  • - Not space filling
  • - More space used by non-leaves
  • - Less scalability?
  • All leaves on 1-D space, perimeter
  • Treemap 2-D space for leaves

34
Misc.
35
CHEOPS
  • Beaudoin, Cheops
  • http//www.crim.ca/hci/cheops/index1.html
  • http//tecfa.unige.ch/schneide/cheops/lite1.html

36
The Original Fisheye View
  • George Furnas, 1981 (pg 311)
  • Large information space
  • User controlled focus point
  • How to render items?
  • Normal View just pick items nearby
  • Fisheye View pick items based on degree of
    interest
  • Degree of Interest function of distance from f
    and a priori importance
  • DOI(x) -dist(x,f) imp(x)

f
x
37
Example Tree structure
  • Distance links between f and x
  • Importance level of x in tree

Distance I A a i
ii b i ii
B a i ii
b i ii
Importance I A a i
ii b i ii
B a i ii
b i ii
DOI I A a i
ii b i ii B
a i ii b
i ii
f
38
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39
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40
Challenges
  • Multiple foci
  • George Robertson, Microsoft Research

41
Polyarchies
  • multiple inter-twined trees
  • Visual pivot
  • George Robertson, Microsoft Research

42
Nifty App of the Day
  • SAS JMP

43
Summary
  • Hyperbolic lt1000
  • TreeMap lt3000, attributes, collective
  • Cheops scale up
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