1. cs5984: Information Visualization Chris North
Trees
cs5984: Information Visualization
Chris North

2. Review Data space: Interaction strategies: Design guidelines
Multi-dimensional
1-D space
2-D space
Interaction strategies:
Dynamic Queries
Multiple views, brushing & linking
Visual overviews
Zooming, overview+detail, focus+context
Design guidelines
Empirical Evaluation

3. Next Data space: Workspaces Theory … 3-D Trees Networks
Document collections
Workspaces
Theory …

4. Trees (Hierarchies) What is a tree? Examples Tasks Items + structure
Add parent pointer attribute
Examples
Family trees, Directories, Org charts, biology taxonomy, menus
Tasks
All previous tasks plus structure-based tasks:
Find descendants, ancestors, siblings, cousins
Overall structure, height, breadth, dense/sparse areas

5. Tree Visualization Example: Outliner Why is tree visualization hard?
Structure AND items
Structure harder, consumes more space
Data size grows very quickly (exponential)
#nodes = bheight

6. 2 Approaches Connection (node & link) Containment (node in node)
Structure vs. attributes
Attributes only (multi-dimensional viz)
Structure only (1 attribute, e.g. name)
Structure + attributes A B C A B C

7. Outliner Good for directed search tasks
Not good for learning structure
No attributes
Apx 50 items visible
Lose path to root for deep nodes

8. Mac Finder
Branching factor:
Small
large

9. Today
Rao, “Hyperbolic Tree”, book pg 382
Joy, maulik

10. Nifty site of the day: X-Files

11. ConeTree / CamTree
Video CHI’91

12. WebTOC Website map: Outliner + size attributes

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

14. PDQ Trees

15. Assignment Read for Thurs Homework #2 due Thurs Spring Break!
Johnson, “Treemaps”, book pg 152
Stasko, “Sunburst”, web
Marcus, marty
Homework #2 due Thurs
Spring Break!
Read for Tues (Mar 13)
Beaudoin, “Cheops”, web
Satya, sumithra
Furnas, “Fisheye View”, book pg 311

16. Scenario: Visualizing Biotech Data
Database of experiments on DNA
1000 experiments?
DNA = long sequence of letters A,C,T,G
100,000 – 1,000,000 letters
Experiment = data values for set of sub-sequences
1000 sub-sequences, letters / sub-sequence
Tasks:
Find experiments given criteria
Find patterns between known set of experiments
Find related experiments
Find trends in experimentation
DNA: AAGTGTTCCGAAATGCAAAAATAGACCCAAAGA…
Experiment: (5-50)=1.4, (72-112)=0.2, …