Information Visualization &Visual Analytics

Jack van WijkDept. Math. & Computer Science

TU Eindhoven

BPM round table, March 28, 2011

Overview

• InfoVis• Visual Analytics

Why is my hard disk full?

?

SequoiaView

• www.win.tue.nl/sequoiaviewVan Wijk et al., 1999, Bruls et al. 2000

Information Visualization

• The use of computer-supported, interactive, visual representations of abstract data to amplify cognition(Card et al., 1999)

InformationVisualizatio

n

Abstract dataset(table,

graph, tree)

Userimagedata

interaction

Abstract data

• Multivariate data visualization

• Tree visualization

• Graph visualization

scatterplot

tree diagram

node link diagram

InfoVis at TU/e

Focus:

• Large data sets, professional users• Use of computer graphics know-how

– shading, geometry, texture, …

• Software Visualization(similar issues as BPM?)

Software Visualization

• User: developer, architect, manager, …• Some fuzzy questions:

– Is the structure sound?– Can I improve the structure by refactoring?– What has happened with the system?– Does the implementation conform the

architecture?– Where are the weak spots?

Different views on software

• Architecture– System structure– Data– Coordination, temporal aspects

• Code– Structure– Derived data, metrics– Evolution

• Execution– Traces, call graphs

Challenges in Software Visualization

Combination of large amounts of– Multivariate data (metrics)– Hierarchical data (system, subsystem, module, ..)– Graph data (call relations)– Text (names, code)

+ + =

Trees + graphs

• Ubiquitous!

MatrixView

Data:– hierarchy of layers, units, modules, classes,

methods– methods calling each other

MatrixView

A

CBE

D

A B C D EABCDE

Matrix representation of graph

MatrixView

Van Ham 2003, Van Wijk et al., 2003

Hierarchical Edge Bundles

• Again, tree+graph, but now completely different

Holten, 2006

Showing directions in edges

arrow light-to-dark dark-to-light

green-to-red curved tapering

Holten et al., 2009

Result of experiments

Visual Analytics: Beyond visualization

Origin

• Founder: Jim Thomas, NVAC• Illuminating the Path, 2004

Visual Analytics:

The science of analytical reasoning facilitated by interactive visual interfaces

Definition

• The science of analytical reasoning facilitated by interactive visual interfaces– Compact!– Complete!– Perfect!– But what is it?

Video

• VisMaster

An InfoVis perspective

InformationVisualization

Abstract dataset

(table, graph, tree)

Userimagedata

interaction

An InfoVis perspective

InformationVisualization

Abstract dataset

(table, graph, tree)

Userimagedata

interaction

Many, large, heterogenous

datasets

- gigabytes, terabytes, petabytes- tables, images, documents, videos, audio,…Data mining

- statistics, machine learning, pattern recognition, artificial intelligence, …

Professional

- domain expertise- fit in workflow- from data foraging

to presentation- teamwork

HCI perception cognitive psychology

software engineering graphics

mathematics design artdata management statistics

The key ingredients

• Huge, heterogenous data sets• Integration of data mining and visualization• Integration in workflow• Support for all stages of data analysis• Support for multiple users

• Keyword: INTEGRATION• Result = product of parts (2 x 2 x 2 x 2 x 2 = 32)

FAQ

We know this already, isn’t it just:• applied infoVis, visual data mining, visual data

analysis, statistical graphics, …

Sure, Visual Analytics builds on existing technologies and earlier examples exist…

One year of time-series data

Van Wijk et al., 19990:00 12:00 24:00

365 graphs

#people at work

After clustering

Van Wijk et al., 19990:00 12:00 24:00

365 graphs

#people at work

Command Post of the Future

• Steven Roth et al.• Visage (1996), CoMotion, MAYA Viz

Interaction, heterogenous data, knowledge sharing, teamwork, decision making, …

FAQ

We know this already, isn’t it just:• applied infoVis, visual data mining, visual data

analysis, statistical graphics, …

Sure, Visual Analytics builds on existing technologies and earlier examples exist…

but integrating all of these is still novel, difficult, and challenging.

FAQ

• This Visual Analytics, that’s American, right?

• No, wrong.

• EU-funded Coordination Action Project• 26 partners, 12 countries• Developing roadmap• Organizing events• Communication platform• Video (youtube: vismaster)

Jörn KohlhammerDaniel Keim

Summary

Visual Analytics:• Great!• Big!• Challenging!