info vis 4-22-2013-dc-vis-meetup-shneiderman

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

Turning Messy BigData into Actionable SmallData

@benbendc

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

HCI Pride: Serving 5B Users

Mobile, desktop, web, cloud

Diverse users: novice/expert, young/old, literate/illiterate, abled/disabled, cultural, ethnic & linguistic diversity, gender, personality, skills, motivation, ...

Diverse applications: E-commerce, law, health/wellness, education, creative arts, community relationships, politics, IT4ID, policy negotiation, mediation, peace studies, ...

Diverse interfaces: Ubiquitous, pervasive, embedded, tangible, invisible, multimodal, immersive/augmented/virtual, ambient, social, affective, empathic, persuasive, ...

Obama Unveils “Big Data” Initiative (3/2012)

Big Data challenges:

•Developing scalable algorithms for processing imperfect data in distributed data stores

•Creating effective human-computer interaction tools for facilitating rapidly customizable visual reasoning for diverse missions.

http://www.whitehouse.gov/sites/default/files/microsites/ostp/big_data_press_release_final_2.pdf `

Information Visualization & Visual Analytics

• Visual bands• Human percle

• Trend, clus..

• Color, size,..

• Three challe• Meaningful vi

• Interaction: w

• Process mo

1999


• Visual bandwidth is enormous• Human perceptual skills are remarkable

• Trend, cluster, gap, outlier...

• Color, size, shape, proximity...

• Three challenges• Meaningful visual displays of massive da

• Interaction: widgets & window coordinati

• Process models for discovery

1999 2004


• 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

1999 2004 2010

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

Spotfire: DC natality data

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

10M - 100M pixels: Large displays

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, Qliktech, Visual Insight

• Temporal LifeLines, TimeSearcher, Palantir, DataMontage

• Tree Cone/Cam/Hyperbolic, SpaceTree, Treemap

• Network Pajek, UCINet, NodeXL, Gephi, Tom Sawyer In

foV

iz

S

ciV

iz .

infosthetics.com visualcomplexity.com eagereyes.orgflowingdata.com perceptualedge.com datakind.orgvisual.ly Visualizing.org infovis.org

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: 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

EventFlow: Original Dataset

LABA_ICSs Merged

SABAs Merged

Align by First LABA_ICS

Reduce Window Size

EventFlow Team: Oracle support

www.cs.umd.edu/hcil/eventflow

www.umdrightnow.umd.edu/news/umd-research-team-developing-powerful-data-visualization-tool-support-oracle

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)

Treemap: WHC Emergency Room (6304 patients in Jan2006)

Group by Admissions/MF, size by service time, color by age

Treemap: WHC Emergency Room (6304 patients in Jan2006) (only those service time >12 hours)

Group by Admissions/MF, size by service time, color by age

www.hivegroup.com

Treemap: Supply Chain

www.hivegroup.com

Treemap: Nutritional Analysis

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

VisualComplexity.com : Manuel Lima

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

www.centrifugesystems.com

Network from Database Tables

NodeXL: Network Overview for Discovery & Exploration in Excel

www.codeplex.com/nodexl

NodeXL: Import Dialogs

www.codeplex.com/nodexl

Tweets at #WIN09 Conference: 2 groups

Flickr networks

Twitter discussion of #GOP

Red: Republicans, anti-Obama, mention FoxBlue: Democrats, pro-Obama, mention CNNGreen: non-affiliated

Node size is number of followersPolitico is major bridging group

Analogy: Clusters Are OccludedHard to count nodes, clusters

Separate Clusters Are More Comprehensible

Twitter networks: #SOTU

Group-In-A-Box: Twitter Network for #CI2012

Twitter Network for “TTW”

Pennsylvania Innovation Network

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 Patterns: 11,000 vertices, 26,000 edges

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

Interactive Methods to Reveal Patterns

Filtering Node & link attribute values or statistics

Clustering Cluster algorithmically by link connectivity

Grouping Group based on node attributes

Motif Common, meaningful structures Simplification replaced with simplified glyphs

Senate Co-Voting

Group-In-A-Box by Region

Interactive Methods to Reveal Patterns

Filtering Node & link attribute values or statistics

Clustering Cluster algorithmically by link connectivity

Grouping Group based on node attributes

Motif Common, meaningful structures Simplification replaced with simplified glyphs

Motif Simplification

(a) Fan motifs & glyphs (b) Connector motifs & glyphs

Motif Simplification

Clique Motifs & Glyphs: 4, 5 & 6

Senate Co-Voting: 65% Agreement

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

Sense-Making Loop

Thomas & Cook: Illuminating the Path (2004)

Sense-Making Loop: Expanded

Thomas & Cook: Illuminating the Path (2004)

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


Purposeful exploration – Hypothesis testing• Range & distribution• Relationships & correlations• Clusters & gaps• Outliers & anomalies• Aggregation & summary• Split & trellis• Temporal comparisons & multiple views• Statistics & forecasts



Purposeful exploration – Hypothesis testing• Range & distribution• Relationships & correlations• Clusters & gaps• Outliers & anomalies• Aggregation & summary• Split & trellis• Temporal comparisons & multiple views• Statistics & forecasts

Situated decision making - Social context• Annotation & marking• Collaboration & coordination• Decisions & presentations


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

30th Anniversary SymposiumMay 22-23, 2013

www.cs.umd.edu/hcil

For More Information

• Visit the HCIL website for 700+ papers & info on videos www.cs.umd.edu/hcil

• 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 • EventFlow: www.cs.umd.edu/hcil/eventflow

info vis 4-22-2013-dc-vis-meetup-shneiderman

Technology

demand overview

mantra overview

temporal data

data types

dc natality data

training search visualization

remarkable trend

diverse applications