Information Visualization for Knowledge Discovery

Information Visualization forKnowledge Discovery

Ben Shneiderman [email protected] @benbendc

Founding Director (1983-2000), Human-Computer Interaction LabProfessor, Department of Computer Science

Member, Institute for Advanced Computer Studies

University of MarylandCollege Park, MD 20742

Interdisciplinary research community - Computer Science & Info Studies - Psych, Socio, Poli Sci & MITH (www.cs.umd.edu/hcil)

Design Issues

• Input devices & strategies• Keyboards, pointing devices, voice

• Direct manipulation

• Menus, forms, commands

• Output devices & formats• Screens, windows, color, sound

• Text, tables, graphics

• Instructions, messages, help

• Collaboration & Social Media

• Help, tutorials, training

• Search www.awl.com/DTUI

Fifth Edition: 2010

• Visualization

Information Visualization

• Visual bandwidth is enormous• Human perceptual skills are remarkable

• Trend, cluster, gap, outlier...

• Color, size, shape, proximity...

• Three challenges• Meaningful visual displays of massive data

• Interaction: widgets & window coordination

• Process models for discovery

Business takes action

• General Dynamics buys MayaViz

• Agilent buys GeneSpring

• Google buys Gapminder

• Oracle buys Hyperion

• Microsoft buys Proclarity

• InfoBuilders buys Advizor Solutions

• SAP buys (Business Objects buys Xcelsius & Inxight & Crystal Reports )

• IBM buys (Cognos buys Celequest) & ILOG

• TIBCO buys Spotfire

Spotfire: Retinol’s role in embryos & vision

http://registration.spotfire.com/eval/default_edu.asp

10M - 100M pixels

Large displays for single or multiple users

100M-pixels & more

1M-pixels & less

Small mobile devices

Information Visualization: Mantra

• Overview, zoom & filter, details-on-demand










Information Visualization: Data Types

• 1-D Linear Document Lens, SeeSoft, Info Mural

• 2-D Map GIS, ArcView, PageMaker, Medical imagery

• 3-D World CAD, Medical, Molecules, Architecture

• Multi-Var Spotfire, Tableau, GGobi, TableLens, ParCoords,

• Temporal LifeLines, TimeSearcher, Palantir, DataMontage

• Tree Cone/Cam/Hyperbolic, SpaceTree, Treemap

• Network Pajek, JUNG, UCINet, SocialAction, NodeXL

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infosthetics.com flowingdata.com infovis.org www.infovis.net/index.php?lang=2

Anscombe’s Quartet

1 2 3 4

x y x y x y x y

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

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


1 2 3 4

x y x y x y x y

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

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

Property Value

Mean of x 9.0

Variance of x 11.0

Mean of y 7.5

Variance of y 4.12

Correlation 0.816

Linear regression y = 3 + 0.5x


Temporal Data: TimeSearcher 1.3

• Time series• Stocks

• Weather

• Genes

• User-specified patterns

• Rapid search

Temporal Data: TimeSearcher 2.0

• Long Time series (>10,000 time points)

• Multiple variables

• Controlled precision in match (Linear, offset, noise, amplitude)

LifeLines: Patient Histories

www.cs.umd.edu/hcil/lifelines

LifeLines2: Contrast+Creatine

LifeLines2: Align-Rank-Filter & Summarize

LifeFlow: Aggregation Strategy

Temporal Categorical Data (4 records)

LifeLines2 format

Tree of Event Sequences

LifeFlow Aggregation

www.cs.umd.edu/hcil/lifeflow

LifeFlow: Interface with User Controls

Treemap: Gene Ontology

www.cs.umd.edu/hcil/treemap/

+ Space filling

+ Space limited

+ Color coding

+ Size coding - Requires learning

(Shneiderman, ACM Trans. on Graphics, 1992 & 2003)

www.smartmoney.com/marketmap

Treemap: Smartmoney MarketMap

Market falls steeply Feb 27, 2007, with one exception

Market falls steeply Sept 22, 2011, some exceptions

Market mixed, February 8, 2008 Energy & Technology up, Financial & Health Care down

Market rises, September 1, 2010, Gold contrarians

Market rises, March 21, 2011, Sprint declines

newsmap.jp

Treemap: Newsmap (Marcos Weskamp)

www.hivegroup.com

Treemap: Supply Chain

www.spotfire.com

Treemap: Spotfire Bond Portfolio Analysis

Treemap: NY Times – Car&Truck Sales

www.cs.umd.edu/hcil/treemap/

Treemap (Voronoi): NY Times - Inflation

www.nytimes.com/interactive/2008/05/03/business/20080403_SPENDING_GRAPHIC.html

State-of-the-art network visualization

www.centrifugesystems.com

Network from Database Tables

Discovery Process: Systematic Yet Flexible

Preparation• Own the problem & define the schedule• Data cleaning & conditioning• Handle missing & uncertain data• Extract subsets & link to related information

SocialAction

• Integrates statistics & visualization

• 4 case studies, 4-8 weeks (journalist, bibliometrician, terrorist analyst, organizational analyst)

• Identified desired features, gave strong positive feedback about benefits of integration

Perer & Shneiderman, CHI2008, IEEE CG&A 2009www.cs.umd.edu/hcil/socialaction

Footprints of Human Activity

• Footprints in sand as Caesarea

NodeXL: Network Overview for Discovery & Exploration in Excel

www.codeplex.com/nodexl

NodeXL: Network Overview for Discovery & Exploration in Excel


NodeXL: Import Dialogs


Tweets at #WIN09 Conference: 2 groups

WWW2010 Twitter Community

WWW2011 Twitter Community: Grouped

CHI2010 Twitter Community

www.codeplex.com/nodexl/

Flickr clusters for “mouse”

Computer Mickey

Animal

Flickr networks

‘GOP’ tweets, clustered (red-Republicans)

PatentTech

SBIR (federal)

PA DCED (state)Related patent

2: Federal agency3: Enterprise

5: Inventors

9: Universities

10: PA DCED

11/12: Phil/Pitt metro cnty

13-15: Semi-rural/rural cnty

17: Foreign countries

19: Other states

Pittsburgh Metro

Westinghouse Electric

Pharmaceutical/Medical

No Location Philadelphia

Navy

PatentTech

SBIR (federal)

PA DCED (state)Related patent

2: Federal agency

3: Enterprise

5: Inventors

9: Universities

10: PA DCED

11/12: Phil/Pitt metro cnty

13-15: Semi-rural/rural cnty

17: Foreign countries

19: Other states

Pittsburgh Metro

Westinghouse Electric

Pharmaceutical/Medical

No Location Philadelphia

Navy

Innovation Clusters: People, Locations, Companies

Analyzing Social Media Networks with NodeXL

I. Getting Started with Analyzing Social Media Networks 1. Introduction to Social Media and Social Networks 2. Social media: New Technologies of Collaboration 3. Social Network Analysis

II. NodeXL Tutorial: Learning by Doing 4. Layout, Visual Design & Labeling 5. Calculating & Visualizing Network Metrics 6. Preparing Data & Filtering 7. Clustering &Grouping

III Social Media Network Analysis Case Studies 8. Email 9. Threaded Networks 10. Twitter 11. Facebook 12. WWW 13. Flickr 14. YouTube 15. Wiki Networks

www.elsevier.com/wps/find/bookdescription.cws_home/723354/description

Social Media Research Foundation

Researchers who want to - create open tools - generate & host open data - support open scholarship

Map, measure & understand social media

Support tool projects to collection, analyze & visualize social media data.

smrfoundation.org

UN Millennium Development Goals

• Eradicate extreme poverty and hunger• Achieve universal primary education• Promote gender equality and empower women• Reduce child mortality• Improve maternal health• Combat HIV/AIDS, malaria and other diseases• Ensure environmental sustainability• Develop a global partnership for development

To be achieved by 2015

29th Annual SymposiumMay 23-24, 2012

www.cs.umd.edu/hcil

For More Information

• Visit the HCIL website for 400 papers & info on videos www.cs.umd.edu/hcil

• Conferences & resources: www.infovis.org

• See Chapter 14 on Info Visualization Shneiderman, B. and Plaisant, C., Designing the User Interface: Strategies for Effective Human-Computer Interaction: Fifth Edition (2010) www.awl.com/DTUI

• Edited Collections: Card, S., Mackinlay, J., and Shneiderman, B. (1999) Readings in Information Visualization: Using Vision to Think Bederson, B. and Shneiderman, B. (2003) The Craft of Information Visualization: Readings and Reflections

For More Information

• Treemaps• HiveGroup: www.hivegroup.com • Smartmoney: www.smartmoney.com/marketmap • HCIL Treemap 4.0: www.cs.umd.edu/hcil/treemap

• Spotfire: www.spotfire.com • TimeSearcher: www.cs.umd.edu/hcil/timesearcher • NodeXL: nodexl.codeplex.com• Hierarchical Clustering Explorer:

www.cs.umd.edu/hcil/hce

• LifeLines2: www.cs.umd.edu/hcil/lifelines2 • Similan: www.cs.umd.edu/hcil/similan

Education

Information Visualization for Knowledge Discovery