Visualization for Information Analysis and Exploration · Information Visualization •• Using interactive computer visualizations Using interactive computer visualizations to represent

Visualization for

Visualization for

Inform

ation Analysis and Exploration

Inform

ation Analysis and Exploration

John Stasko

John Stasko

Inform

ation Interfaces Research Group

Inform


Sept. 17, 2008

Sept. 17, 2008

Inform


Inform


School of Interactive Computing

School of Interactive Computing

Georgia Institute of Technology

Georgia Institute of Technology

Exercise

Exercise

••Get out pencil & paper

Get out pencil & paper

2

Data Explosion

Data Explosion

••Society is more complex

Society is more complex

––There simply is more “stuff”

There simply is more “stuff”

3

••Computers, internet and web give people

Computers, internet and web give people

access to an incredible amount of data

access to an incredible amount of data

––news, sports, financial, purchases, etc...

news, sports, financial, purchases, etc...

Data Overload

Data Overload

••Confound: How to m

ake use of the data

Confound: How to m

ake use of the data

––How do we m

ake sense of the data?

How do we m

ake sense of the data?

––How do we harness this data in decision

How do we harness this data in decision--

making processes?

making processes?

––How do we avoid being overw

helmed?

How do we avoid being overw

helmed?

4

––How do we avoid being overw

helmed?

How do we avoid being overw

helmed?

The Challenge

The Challenge

••Transform

the

Transform

the data

data

into

into inform

ation

inform

ation

(understanding, insight) thus making it

(understanding, insight) thus making it

useful to people

useful to people

5

Premise of my W

ork

Premise of my W

ork

••Visualization of data helps people

Visualization of data helps people

understand it better

understand it better 6

Human Vision

Human Vision

••Highest bandwidth sense

Highest bandwidth sense

––~100 MB/s

~100 MB/s

––Parallel

Parallel

––Strong pattern recognition

Strong pattern recognition

7

––Much done preattentively, ie, without

Much done preattentively, ie, without

thought

thought

Visualization

Visualization

••Definition

Definition

––“The use of computer

“The use of computer--supported, interactive

supported, interactive

visual representations of data to amplify

visual representations of data to amplify

cognition.”

cognition.”

••From [Card, Mackinlay Shneiderm

an ‘98]

From [Card, Mackinlay Shneiderm

an ‘98]

8

••From [Card, Mackinlay Shneiderm

an ‘98]

From [Card, Mackinlay Shneiderm

an ‘98]

Visualization

Visualization

••Often thought of as process of creating a

Often thought of as process of creating a

graphic or an image

graphic or an image

••Really is a cognitive process

Really is a cognitive process

––Form

a m

ental image of something

Form

a m

ental image of something

––Internalize an understanding

Internalize an understanding

9

––Internalize an understanding

Internalize an understanding

••“The purpose of visualization is insight,

“The purpose of visualization is insight,

not pictures”

not pictures”

––Insight: discovery, decision m

aking,

Insight: discovery, decision m

aking,

explanation, analysis, exploration, learning

explanation, analysis, exploration, learning

Main Idea

Main Idea

••Visuals help us think

Visuals help us think

––Provide a frame of reference, a temporary

Provide a frame of reference, a temporary

storage area

storage area

••Cognition

Cognition →→

Perception

Perception

10

••Pattern m

atching

Pattern m

atching

••External cognition aid

External cognition aid

––Role of external world in thinking and reason

Role of external world in thinking and reason

Larkin & Simon ’87

Larkin & Simon ’87

Card, Mackinlay, Shneiderm

an ‘98

Card, Mackinlay, Shneiderm

an ‘98

When to Apply?

When to Apply?

••Many other techniques for data analysis

Many other techniques for data analysis

––Data m

ining, DB queries, m

achine learning…

Data m

ining, DB queries, m

achine learning…

••Visualization m

ost useful in

Visualization m

ost useful in exploratory

exploratory

11

••Visualization m

ost useful in

Visualization m

ost useful in exploratory

exploratory

data analysis

data analysis

––Don’t know what you’re looking for

Don’t know what you’re looking for

––Don’t have a priori questions

Don’t have a priori questions

––Want to know what questions to ask

Want to know what questions to ask

Part of our Culture

Part of our Culture

••“I see what you’re saying”

“I see what you’re saying”

••“Seeing is believing”

“Seeing is believing”

••“A picture is worth a thousand words”

“A picture is worth a thousand words”

12

Some quick (static) examples…

Some quick (static) examples…

13

NYC W

eather

NYC W

eather

2220 numbers

14

E. Tufte, Visual Display of Quant Info

London Subway

London Subway

www.thetube.com

15

True Geography

True Geography

www.kottke.org/plus/misc/images/tubegeo.gif

16

Easy W

alking Lines Added

Easy W

alking Lines Added

rodcorp.typepad.com/photos/art_2003/tube_walklines_final_lmfaint.html

17

Atlanta Flight Traffic

Atlanta Flight Traffic

18

Atlanta Journal

April 30, 2000

InfoVis ‘07

InfoVis ‘07

19

Reinforce m

y point with two examples

Reinforce m

y point with two examples

20

Which cereal has the m

ost/least potassium?

Which cereal has the m

ost/least potassium?

Is there a relationship between potassium and fiber?

Is there a relationship between potassium and fiber?

If so, are there any outliers?

If so, are there any outliers?

Which m

anufacturer makes the healthiest cereals?

Which m

anufacturer makes the healthiest cereals?

Questions:

Questions:

21

22

Potassium

Potassium

Fiber

Even Tougher?

Even Tougher?

••What if you could only see one cereal’s

What if you could only see one cereal’s

data at a time?

data at a time? (e.g. some websites)

(e.g. some websites)

••What if I read the data to you?

What if I read the data to you?

23

Four Data Sets

Four Data Sets

••Mean of the x values = 9.0

Mean of the x values = 9.0

••Mean of the y values = 7.5

Mean of the y values = 7.5

••Equation of the least

Equation of the least--squared regression line is: y = 3 + 0.5x

squared regression line is: y = 3 + 0.5x

••Sums of squared errors (about the m

ean) = 110.0

Sums of squared errors (about the m

ean) = 110.0

••Regression sums of squared errors (variance accounted for by x)

Regression sums of squared errors (variance accounted for by x)

= 27.5

= 27.5

24

= 27.5

= 27.5

••Residual sums of squared errors (about the regression line)

Residual sums of squared errors (about the regression line)

= 13.75

= 13.75

••Correlation coefficient = 0.82

Correlation coefficient = 0.82

••Coefficient of determ

ination = 0.67

Coefficient of determ

ination = 0.67

http://astro.swarthmore.edu/astro121/anscombe.htm

lhttp://astro.swarthmore.edu/astro121/anscombe.htm

l

The Data Sets

The Data Sets

25

The Values

The Values

1 2 3 4

10.0, 8.04 10.0,9.14 10.0, 7.46 8.0, 6.58

8.0, 6.95 8.0,8.14 8.0, 6.77 8.0, 5.76

13.0, 7.58 13.0,8.74 13.0,12.74 8.0, 7.71

9.0, 8.81 9.0,8.77 9.0, 7.11 8.0, 8.84

11.0, 8.33 11.0,9.26 11.0, 7.81 8.0, 8.47

26

14.0, 9.96 14.0,8.10 14.0, 8.84 8.0, 7.04

6.0, 7.24 6.0,6.13 6.0, 6.08 8.0, 5.25

4.0, 4.26 4.0,3.10 4.0, 5.39 19.0,12.50

12.0,10.84 12.0,9.13 12.0, 8.15 8.0, 5.56

7.0, 4.82 7.0,7.26 7.0, 6.42 8.0, 7.91

5.0, 5.68 5.0,4.74 5.0, 5.73 8.0, 6.89

Revisit Starting Exercise

Revisit Starting Exercise

••What did you put on paper?

What did you put on paper?

27

Two Related Disciplines

Two Related Disciplines

••Inform

ation Visualization

Inform

ation Visualization

••Visual Analytics

Visual Analytics

28

Inform

ation Visualization

Inform

ation Visualization

••Using interactive computer visualizations

Using interactive computer visualizations

to represent and communicate abstract

to represent and communicate abstract

data

data

––Statistics, databases, software, …

Statistics, databases, software, …

29

••Area emerged approximately 1990

Area emerged approximately 1990

Inform

ation Visualization

Inform

ation Visualization

••Recent research trends

Recent research trends

––InfoVis for the Masses

InfoVis for the Masses

––Challenges of evaluation

Challenges of evaluation

––Interaction is crucial

Interaction is crucial 30

Visual Analytics

Visual Analytics

••Inform

al: Using visual representations to

Inform

al: Using visual representations to

help m

ake decisions

help m

ake decisions

••Form

al: The science of analytical

Form

al: The science of analytical

reasoning facilitated by interactive visual

reasoning facilitated by interactive visual

interfaces

interfaces

31

interfaces

interfaces

••InfoVis++

InfoVis++

••Area emerged approximately 2005

Area emerged approximately 2005

Overview of the R&D Agenda

•Challenges

•Science of Analytical

Reasoning

•Science of Visual

Representations

and Interactions

32

•Data Representations

and Transformations

•Production, Presentation, and

Dissemination

•Moving Research Into

Practice

•Positioning for an Enduring

Success

Visual Analytics: Beyond InfoVis

Visual Analytics: Beyond InfoVis

••Statistics, data representation and statistical graphics

Statistics, data representation and statistical graphics

••Geospatial and Temporal Sciences

Geospatial and Temporal Sciences

••Applied Mathematics

Applied Mathematics

••Knowledge representation, management and discovery

Knowledge representation, management and discovery

••Ontology, semantics, NLP, extraction, synthesis, …

Ontology, semantics, NLP, extraction, synthesis, …

33

••Ontology, semantics, NLP, extraction, synthesis, …

Ontology, semantics, NLP, extraction, synthesis, …

••Cognitive and Perceptual Sciences

Cognitive and Perceptual Sciences

••Comunications: Capture, Illustrate and present a m

essage

Comunications: Capture, Illustrate and present a m

essage

••Decision sciences

Decision sciences

Academic Context

Academic Context

Visual

Analytics

~2005

34

Inform

ation

Visualization

~1990

IEEE InfoVis

IEEE InfoVis

35

IEEE VAST

VAST

Sensemaking

Sensemaking

“A m

otivated , continuous effort to understand

“A m

otivated , continuous effort to understand

connections (w

hich can be among people,

connections (w

hich can be among people,

places, and events) in order to anticipate

places, and events) in order to anticipate

their trajectories and act effectively.”

their trajectories and act effectively.”

36

––Klein, Moon and Hoffman

Klein, Moon and Hoffman

Jigsaw

Jigsaw

••Visualization for Investigative Analysis

Visualization for Investigative Analysis

across Document Collections

across Document Collections

37

The Jigsaw Team

The Jigsaw Team

Carsten Görg

Carsten Görg

Zhicheng Liu

Zhicheng Liu

Vasili Pantazopoulos

Vasili Pantazopoulos

+ 4 new students

+ 4 new students

38

Gennadiy Stepanov

Gennadiy Stepanov

Sarah W

illiams

Sarah W

illiams

Neel Parekh

Neel Parekh

Kanupriyah Singhal

Kanupriyah Singhal

39

Pirolli & Card, ICIA ‘05

Pirolli & Card, ICIA ‘05

Pain Points

Pain Points

••Cost structure of scanning and selecting

Cost structure of scanning and selecting

items for further attention

items for further attention

••Analysts’ span of attention for evidence

Analysts’ span of attention for evidence

and hypotheses

and hypotheses

40

Problem Addressed

Problem Addressed

••Help investigative analysts discover

Help investigative analysts discover

plans, plots and threats embedded across

plans, plots and threats embedded across

the individual documents in large

the individual documents in large

document collections

document collections

41

Documents/

Documents/

case reports

case reports

Blogs

Blogs

DBs

DBs

Example Document

Example Document

42

Our Focus

Our Focus

••Entities within the documents

Entities within the documents

––Person, place, organization, phone number,

Person, place, organization, phone number,

date, license plate, etc.

date, license plate, etc.

••Thesis: A plot/threat within the

Thesis: A plot/threat within the

documents will involve a set of entities in


43



coordination

coordination

Entity Identification


••Must identify and extract entities from

Must identify and extract entities from

plain text documents

plain text documents

––Crucial for our work

Crucial for our work

••Not our main research focus

Not our main research focus ––

44

••Not our main research focus

Not our main research focus ––

Collaborate with or use tools from others

Collaborate with or use tools from others

Entities Identified

Entities Identified

45

Connections

Connections

••Entities relate/connect to each other to

Entities relate/connect to each other to

make a larger “story”

make a larger “story”

••Connection definition:

Connection definition:

––Two entities are connected if they appear in

Two entities are connected if they appear in

46

––Two entities are connected if they appear in

Two entities are connected if they appear in

a document together

a document together

––The m

ore documents they appear in

The m

ore documents they appear in

together, the stronger the connection

together, the stronger the connection

Jigsaw

Jigsaw

••Multiple visualizations (views) of

Multiple visualizations (views) of

documents, entities, & their connections

documents, entities, & their connections

••Views are highly interactive and

Views are highly interactive and

coordinated

coordinated

“Putting the pieces together”

“Putting the pieces together”

47

••User actions generate events

User actions generate events

that are transm

itted to and

that are transm

itted to and

(possibly) reflected in other

(possibly) reflected in other

views

views

System Views

System Views

48

The Need for Pixels

The Need for Pixels

49

Demo

Demo

50

Demo

Demo

Console

Console

51

Document View

Document View

52

List View

List View

53

Graph View

Graph View

54

Scatterplot View

Scatterplot View

55

Calendar View

Calendar View

56

Report Cluster View

Report Cluster View

57

Timeline View

Timeline View

58

Shoebox

Shoebox

59

Trial Use

Trial Use

••Transitioning system to real clients

Transitioning system to real clients

60

Future W

ork

Future W

ork

••Entity Identification


••Evaluation

Evaluation

••Collaborative version

Collaborative version

••Themes/concepts

Themes/concepts

••Enhanced evidence

Enhanced evidence

••Wikipedia & Intellipedia

Wikipedia & Intellipedia

••Geospatial View

Geospatial View

••Connectivity search

Connectivity search

••Reliability/uncertainty

Reliability/uncertainty

••Other types of data

Other types of data

61

••Enhanced evidence

Enhanced evidence

marshalling

marshalling

••Present/browse

Present/browse

investigation history

investigation history

••Scalability issues

Scalability issues

••Other types of data

Other types of data

••Web search & situational

Web search & situational

awareness

awareness

••Display wall?

Display wall?

••Deployment

Deployment

Take Away Point

Take Away Point

••Design your visualization systems and

Design your visualization systems and

tools to facilitate analysis and exploration

tools to facilitate analysis and exploration

––Not to just illustrate and reconfirm

existing

Not to just illustrate and reconfirm

existing

knowledge

knowledge

62

••Including flexible, useful interaction is

Including flexible, useful interaction is

one of the best ways to do this

one of the best ways to do this

To Learn More

To Learn More

••http://w

ww.gvu.gatech.edu/ii

http://w

ww.gvu.gatech.edu/ii

63

Acknowledgment

Acknowledgment

••Some slides in this presentation

Some slides in this presentation

borrowed from overviews of visual

borrowed from overviews of visual

analytics by Jim Thomas, NVAC Director

analytics by Jim Thomas, NVAC Director

64

Acknowledgments

Acknowledgments

••Work conducted as part of the

Work conducted as part of the

Southeastern Regional Visualization and

Southeastern Regional Visualization and

Analytics Center, supported by DHS and

Analytics Center, supported by DHS and

NVAC

NVAC

65

••Supported by NSF IIS

Supported by NSF IIS--0414667

0414667

End

End

••Thanks for your attention!

Thanks for your attention!

••Questions?

Questions?

66