Trees

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Trees

• cs5984 Information Visualization
• Chris North

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Review

• Data space
• Multi-dimensional
• 1-D space
• 2-D space
• Interaction strategies
• Dynamic Queries
• Multiple views, brushing linking
• Visual overviews
• Zooming, overviewdetail, focuscontext
• Design guidelines
• Empirical Evaluation

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Next

• Data space
• 3-D
• Trees
• Networks
• Document collections
• Workspaces
• Theory

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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

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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

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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
C
B
A
B
C
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Outliner

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

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Mac Finder
Branching factor Small large
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Today

• Rao, Hyperbolic Tree, book pg 382
• Joy, maulik

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Nifty site of the day X-Files

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

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ConeTree / CamTree

• Video CHI91

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WebTOC

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

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PDQ Trees

• OverviewDetail of 2D layout
• Dynamic Queries on each level for pruning

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PDQ Trees
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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

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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 Experimen
t (5-50)1.4, (72-112)0.2,