Trees

cs5984: Information Visualization

Chris North

Review

• Data space:• 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

Next

• Data space:• 3-D

• Trees

• Networks

• Document collections

• Workspaces

• Theory

• …

Trees (Hierarchies)

• What is a tree?• 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

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

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

CB

A

B C

Outliner

• Good for directed search tasks

• Not good for learning structure

• No attributes

• Apx 50 items visible

• Lose path to root for deep nodes

Mac FinderBranching factor:

Small

large

Today• Rao, “Hyperbolic Tree”, book pg 382

» Joy, maulik

Nifty site of the day: X-Files

• http://www.thexfiles.com/main_flash.html

ConeTree / CamTree

• Video CHI’91

WebTOC• Website map: Outliner + size attributes• http://www.cs.umd.edu/projects/hcil/webtoc/fhcil.html

PDQ Trees

• Overview+Detail of 2D layout

• Dynamic Queries on each level for pruning

PDQ Trees

Assignment

• Read for Thurs• 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

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, 10-100 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, …