1 / 43

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

dandre
Download Presentation

Tree Structures (Hierarchical Information)

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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

More Related