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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, overview+detail, focus+context
• 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
CB
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 FinderBranching 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 CHI’91
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WebTOC• Website map: Outliner + size attributes• http://www.cs.umd.edu/projects/hcil/webtoc/fhcil.html
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PDQ Trees
• Overview+Detail 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…
Experiment: (5-50)=1.4, (72-112)=0.2, …