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Tree Structures (Hierarchical Information)

Tree Structures (Hierarchical Information). cs5764: Information Visualization Chris North. Where are we?. Multi-D 1D 2D Trees Graphs 3D Document collections. Design Principles Empirical Evaluation Visual Overviews. Trees (Hierarchies). What is a tree? DAG, one parent per node

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Tree Structures (Hierarchical Information)

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  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 • 1:M

  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 B C 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 • Focus+context

  13. Cone Trees • Robertson, “ConeTrees” • Xerox PARC • 3D for focus+context

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

  15. PDQ Trees

  16. Disk Tree • Ed Chi, Xerox PARC • Overview:Reduced visual representation

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

  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 B C 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 childspace 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/marketmap/ • People Map: http://www.truepeers.com/ • Coffee Map: http://www.peets.com/tast/11/coffee_selector.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 • + Focus+context 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. Challenges • Multiple foci • George Robertson, Microsoft Research

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

  40. Nifty App of the Day • SAS JMP

  41. Summary • Hyperbolic <1000 • TreeMap <3000, attributes, collective • Cheops = scale up

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