information visualization for knowledge discovery
DESCRIPTION
(October 2011)TRANSCRIPT
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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
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Interdisciplinary research community - Computer Science & Info Studies - Psych, Socio, Poli Sci & MITH (www.cs.umd.edu/hcil)
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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
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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
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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
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Spotfire: Retinol’s role in embryos & vision
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http://registration.spotfire.com/eval/default_edu.asp
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10M - 100M pixels
Large displays for single or multiple users
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100M-pixels & more
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1M-pixels & less
Small mobile devices
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Information Visualization: Mantra
• Overview, zoom & filter, details-on-demand
• Overview, zoom & filter, details-on-demand
• Overview, zoom & filter, details-on-demand
• Overview, zoom & filter, details-on-demand
• Overview, zoom & filter, details-on-demand
• Overview, zoom & filter, details-on-demand
• Overview, zoom & filter, details-on-demand
• Overview, zoom & filter, details-on-demand
• Overview, zoom & filter, details-on-demand
• Overview, zoom & filter, details-on-demand
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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
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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
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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
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
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Anscombe’s Quartet
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Temporal Data: TimeSearcher 1.3
• Time series• Stocks
• Weather
• Genes
• User-specified patterns
• Rapid search
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Temporal Data: TimeSearcher 2.0
• Long Time series (>10,000 time points)
• Multiple variables
• Controlled precision in match (Linear, offset, noise, amplitude)
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LifeLines: Patient Histories
www.cs.umd.edu/hcil/lifelines
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LifeLines2: Contrast+Creatine
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LifeLines2: Align-Rank-Filter & Summarize
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LifeFlow: Aggregation Strategy
Temporal Categorical Data (4 records)
LifeLines2 format
Tree of Event Sequences
LifeFlow Aggregation
www.cs.umd.edu/hcil/lifeflow
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LifeFlow: Interface with User Controls
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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)
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www.smartmoney.com/marketmap
Treemap: Smartmoney MarketMap
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Market falls steeply Feb 27, 2007, with one exception
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Market falls steeply Sept 22, 2011, some exceptions
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Market mixed, February 8, 2008 Energy & Technology up, Financial & Health Care down
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Market rises, September 1, 2010, Gold contrarians
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Market rises, March 21, 2011, Sprint declines
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newsmap.jp
Treemap: Newsmap (Marcos Weskamp)
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www.hivegroup.com
Treemap: Supply Chain
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www.spotfire.com
Treemap: Spotfire Bond Portfolio Analysis
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Treemap: NY Times – Car&Truck Sales
www.cs.umd.edu/hcil/treemap/
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Treemap (Voronoi): NY Times - Inflation
www.nytimes.com/interactive/2008/05/03/business/20080403_SPENDING_GRAPHIC.html
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State-of-the-art network visualization
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www.centrifugesystems.com
Network from Database Tables
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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
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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
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Footprints of Human Activity
• Footprints in sand as Caesarea
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NodeXL: Network Overview for Discovery & Exploration in Excel
www.codeplex.com/nodexl
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NodeXL: Network Overview for Discovery & Exploration in Excel
www.codeplex.com/nodexl
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NodeXL: Import Dialogs
www.codeplex.com/nodexl
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Tweets at #WIN09 Conference: 2 groups
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WWW2010 Twitter Community
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WWW2011 Twitter Community: Grouped
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CHI2010 Twitter Community
www.codeplex.com/nodexl/
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Flickr clusters for “mouse”
Computer Mickey
Animal
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Flickr networks
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‘GOP’ tweets, clustered (red-Republicans)
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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
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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
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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
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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
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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
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29th Annual SymposiumMay 23-24, 2012
www.cs.umd.edu/hcil
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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
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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