cs5984: Information Visualization Chris North Trees cs5984: Information Visualization Chris North

Review Data space: Interaction strategies: Design guidelines 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

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

Trees (Hierarchies) What is a tree? Examples Tasks 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

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

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

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

Mac Finder Branching factor: Small large

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

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

ConeTree / CamTree Video CHI’91

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

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

PDQ Trees

Assignment Read for Thurs Homework #2 due Thurs Spring Break! 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

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, …