1 / 16

Trees

This review by Chris North explores advanced methods for visualizing multi-dimensional data using hierarchical tree structures. Key strategies such as dynamic queries, multiple views, and interaction techniques like brushing and linking are discussed to enhance user engagement and data comprehension. The text delves into the complexity of tree visualization, examining the challenges posed by data size and structural representation. Empirical evaluations and design guidelines offer insights into optimizing visual overviews, zooming, and context-focused exploration for tasks related to triangulating patterns in extensive datasets. ###

allegra-mendez
Télécharger la présentation

Trees

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. Trees cs5984: Information Visualization Chris North

  2. 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

  3. Next • Data space: • 3-D • Trees • Networks • Document collections • Workspaces • Theory • …

  4. 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

  5. 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

  6. 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 B C A B C

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

  8. Mac Finder Branching factor: Small large

  9. Today • Rao, “Hyperbolic Tree”, book pg 382 • Joy, maulik

  10. Nifty site of the day: X-Files • http://www.thexfiles.com/main_flash.html

  11. ConeTree / CamTree • Video CHI’91

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

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

  14. PDQ Trees

  15. 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

  16. 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, …

More Related