Visualization of Shallow Trees with Nodal Attributes using Treemap, Table Lens, and Fisheye Table - PowerPoint PPT Presentation

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Visualization of Shallow Trees with Nodal Attributes using Treemap, Table Lens, and Fisheye Table

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Title: Visualization of Shallow Trees with Nodal Attributes using Treemap, Table Lens, and Fisheye Table


1
Visualization of Shallow Treeswith Nodal
Attributesusing Treemap, Table Lens, and
Fisheye Table
  • Fred Bernal
  • Steve Betten
  • Chris Horn
  • May 14, 2001

2
Data Hierarchy
3
Spreadsheet View
4
Experiment Variables
  • Independent Variable
  • Data Visualization Technique
  • Treemap
  • Table Lens
  • Fisheye
  • Dependent Variables
  • Time to correct completion
  • Number of incorrect answers
  • Subjective satisfaction
  • Subjective ease of use

5
Treemap from HCIL
6
Table Lens from Inxight
7
Fisheye Table from HCIL
8
Experiment Design
  • 18 subjects - intermediate computer experience
  • 1 x 3 within-subjects design
  • 5 minutes of training per treatment
  • 4 tasks per treatment
  • Answers differed between treatments
  • Each subject followed one of 6 possible
    permutations for treatment order
  • ABC, ACB, BAC, BCA, CAB, CBA

9
Hypothesis 1 and Result
  • Less time to find a maximum value in a sorted
    list than in unsorted 2-d space.
  • Question Which unit has the greatest output?
  • Fisheye and Table Lens lt Treemap

Confirmed
10
Hypothesis 2 and Result
  • Less time to find values in a range using filters
    than scanning without using filters.
  • Question Out of the units that have an output
    between 20 and 25 (inclusive), how many units
    have at least one problem?
  • Treemap lt Table Lens lt Fisheye

Not Confirmed
11
Hypothesis 3 and Result
  • Less time to find values in a range by filtering
    and sorting than by filtering alone.
  • Question Out of those units that have exactly
    one problem, which unit has the worst (least)
    health?
  • Table Lens lt Treemap lt Fisheye Table

Not Confirmed
12
Hypothesis 4 and Result
  • Less time to find values in a sorted, continuous
    fisheye view than in a sorted, discrete fisheye
    view.
  • Which states, if any, contain a county named D?
    Which states, if any, contain a county named G?
  • Fisheye Table lt Table Lens lt Treemap

Not Confirmed
13
Hypothesis 5 and Result
  • Subjects will be more satisfied with programs
    with greater functionality
  • Table Lens gt Treemap gt Fisheye

Confirmed (partially)
14
Hypothesis 6 and Result
  • Subjects will find programs with straightforward
    interfaces easier to use
  • Treemap Table Lens Fisheye Table

Confirmed
15
Conclusions
  • Impact for practitioners
  • Filtering
  • Suggestions for software developers
  • Treemap layout
  • Table Lens more interactive filtering
  • Fisheye Table reverse sorting, filtering
  • Refinement of theory and suggestions for future
    researchers
  • General Restrict features for each task
  • Fisheye layout Fisheye Table v. Table Lens
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