The 5th International Symposium on Smart Graphics(SG-2005) - Frauenwoerth Cloister, Germany, August 22-24, 2005

Visualization Tree, multiple Visualization Tree, multiple linked analytical decisionslinked analytical decisions

Rodrigues Jr., José Fernando

Traina, Agma J. M.

Traina Jr., Caetano

University of São PauloComputer Science Department

ICMC-USPBrazil

(SG-05)

2/30

• Introduction

• Developed System

• Interaction Systematization

• Summarization Features

• Conclusions and Ending

Out

line

Out

line

• Introduction

(SG-05)

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Techniques used to generate scenes whose graphical attributes rely on the data values

Info

vis

Info

vis Information Visualization (Infovis), manages

to develop techniques for the analysis of data sets that do not have an intrinsic graphical nature

Visualization

Information Visualization(InfoVis)

Scientific Visualization

(SG-05)

4/30

Increasing volume of data that cannot be well utilized to produce useful knowledge

Raw visualization techniques are limited in the task of data analysis, while datasets are unlimited both in size and complexity

The efficient analysis of multivariate data can provide assistance in decision making

There is a need for visualization mechanisms that reduce the

drawback of massive datasets.

Mot

ivat

ion

Mot

ivat

ion

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The

pro

blem

The

pro

blem

Due to overlap of graphical items, some regions of the visualization seam like blots in the display

Massively populated datasets tend to result in a visualization scene with an unacceptable level of clutter

Overlap of graphical items

Visual clutter

(SG-05)

6/30

• Introduction

• Developed System

• Interaction Systematization

• Summarization Features

• Conclusions and Ending

Out

line

Out

line

• Introduction

• Developed System

(SG-05)

7/30

Dev

elop

ed s

yste

mD

evel

oped

sys

tem

Visualization Tree– Data analysis multiple visualization techniques

– Graphical overlap Visual pipeline

– Cognitive flow Workspaces refinement and composition

– Visual clutter Tree scheme

– Enhance exploration New interaction systematization based on the tree metaphor

– Overpopulated data sets Frequency plot

– Data summarization Statistics presentation

– Hypothesis formulation Relevance plot

The Visualization Tree system is a systematic effort to enhance the

InfoVis practice by utilizing integrated presentation, interaction

and summarization mechanisms.

(SG-05)

8/30

• Introduction

•Developed System

• Interaction Systematization

• Summarization Features

• Conclusions and Ending

Out

line

Out

line

• Introduction

• Developed System

• Interaction Systematization

(SG-05)

9/30

Mul

tipl

e te

chni

ques

Mul

tipl

e te

chni

ques

Multiple visualization techniques at each workspace permits to explore each technique’s advantages in order to aid the analysis process

Scatter Plots

Parallel Coordinates

Star Coordinates

Table Lens

(SG-05)

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Vis

ual p

ipel

ine

Vis

ual p

ipel

ine

The visual pipeline allows to extend one workspace’s visualization to multiple workspaces

It naturally diminishes graphical items overlap by extending the boundaries in derived workspaces

Via successive pipelines, it is possible to grasp details until only one item

populates its own workspace.

(SG-05)

11/30

Tre

e sc

hem

eT

ree

sche

me

The tree scheme allows to build multiple views in a decision-tree style

Cars

European

Japanese

American

4 cylinders

3 cyliners

1976 - 1982

1970 - 1976

(SG-05)

12/30

Tre

e sc

hem

eT

ree

sche

me

The use of multiple views is a known strategy that can help to diminish user cognitive overhead:– single views create cognitive overhead by

requiring simultaneously comprehension of diverse data

– easier to accomplish than single view memory-based comparison

– “divide and conquer,” aiding memory by reducing the amount of data they need to consider at the same time

In other words, the tree scheme can help to bypass the drawbacks of

visual clutter presentation.

(SG-05)

13/30Cco

mpo

siti

onC

com

posi

tion

Besides refining the views, it may be interesting to merge views for extra analytical possibilities:

– when two or more views have similar, correlated or worthy-comparing semantics

– for easy comparison, it may be worthy to put together branches of the tree in side-by-side, rather than node-like, positioning

Cars

European

Japanese

American

4 cylinders

3 cyliners

1976 - 1982

1970 - 1975

(European 4 cylinder moels) OR

(Older American models)

The composition of workspaces addresses these issues in an easy-to-

use interaction.

(SG-05)

14/30Inte

ract

ion

Inte

ract

ion

syst

emat

izat

ion

syst

emat

izat

ion

The developed system proposes a new interaction systematization to explore multiple linked workspaces

The tree structure keeps track of the decisions made by the analyst

Interaction tasks can be performed either in each node or in the whole tree

The system interaction promotes the creation of classification trees that help to interpret the visualization in a partitioned manner

By promoting multiple views exploration, the systems allows

scalability and flexibility.

(SG-05)

15/30

• Introduction

•Developed System

• Interaction Systematization

• Summarization Features

• Conclusions and Ending

Out

line

Out

line

• Introduction

• Developed System

• Interaction Systematization

• Summarization Features

(SG-05)

16/30Fre

quen

cy p

lot

Fre

quen

cy p

lot

A method that combines selective filtering with automatic frequency calculus within a given selection

The frequency is visually presented through the opacity of the graphical items

Dynamic visual auditingcues can transform the cognition task of view registration into a faster perception

inference task.

(SG-05)

17/30Sta

tist

ics

pres

enta

tion

Sta

tist

ics

pres

enta

tion To summarize the

data, basic statistics are presented over the visualizations

Average (green), standard deviation (yellow), median (cyan) and mode values (magenta)

The use of statistics can characterize an entire visual workspace diminishing cognitive load.

(SG-05)

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X1

X1 = RP1 + MRD

Relevance = 0

X0 = RP0

Relevance = 1

X0

X2

X3

Null RP2 Not Considered

Dist = 1

Relevance = - 1

The relevance point is over the attribute value

The distance is equal the Maximum relevance

distance The distance is the maximum possibleRelevance = 1 + 0 + (-1) =

0/3 = 0

The data is presented according to its relevance to a user’s defined set of interesting points

Rel

evan

ce p

lot

Rel

evan

ce p

lot

The Relevance Plot can be used to determine speculative queries in sets where

categorical selections are not efficient.

(SG-05)

19/30

• Introduction

•Developed System

• Interaction Systematization

• Summarization Features

• Conclusions and Ending

Out

line

Out

line

• Introduction

• Developed System

• Interaction Systematization

• Conclusions and Ending

(SG-05)

20/30

Con

clus

ions

Con

clus

ions

The Visualization Tree systematization can help to diminish InfoVis techniques limitations

The interaction, exploration and summarization functionalities, together, can be considered a step further in multivariate visual analysis

The future of InfoVis do not rely on revolutionary new techniques but on integrated systematizations presenting interaction and summarization capabilities

(SG-05)

21/30

The

End

The

End

Thanks for coming

All this information did not fit in the paper, so the tool (MSWindows) can be downloaded at

http://www.gbdi.icmc.usp.br/~junio/vistree

or at

http://vistree.got.to (alias) We have validated most of the system’s

features based on literature knowledge. However…

To do: perform a systematic evaluation of the tool usability