Understanding Innovation Trajectories for Visual Analytics

Understanding Innovation Trajectories
for Visual Analytics
Ben Shneiderman
[email protected]
Founding Director (1983-2000), Human-Computer Interaction Lab
Professor, Department of Computer Science
Member, Institute for Advanced Computer Studies
Interdisciplinary research community
- Computer Science & Info Studies
- Psych, Socio, Poli Sci & MITH
(www.cs.umd.edu/hcil)
Designing the User Interface
•  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 •  Visualization
www.awl.com/DTUI
Fifth Edition: 2010
Information Visualization / Visual Analytics
• 
Visual bandwidth is enormous
•  Human perceptual skills are remarkable
•  Trend, cluster, gap, outlier...
•  Color, size, shape, proximity...
• 
•  Human image storage is fast and vast
Three challenges
•  Meaningful visual displays of massive data
•  Interaction: widgets & window coordination
•  Process models for discovery:
Integrate statistics & visualization
Support annotation & collaboration
Preserve history, undo & macros
Visual Analytics Success Stories
•  General Dynamics buys MayaViz
•  Agilent buys GeneSpring
•  Google buys Gapminder
•  Oracle buys (Hyperion buys Xcelsius)
•  Microsoft buys Proclarity
•  InfoBuilders buys Advizor Solutions
•  SAP buys (Business Objects buys
Infomersion & Inxight & Crystal Reports )
•  IBM buys (Cognos buys Celequest) & ILOG
•  TIBCO buys Spotfire
SciViz .
• 
• 
• 
1-D Linear
2-D Map
3-D World
Document Lens, SeeSoft, Info Mural
• 
• 
• 
• 
Multi-Var
Temporal
Tree
Network
Spotfire, Tableau, GGobi, TableLens, ParCoords,
InfoViz
Visual Analytics: Data Types
GIS, ArcView, PageMaker, Medical imagery
CAD, Medical, Molecules, Architecture
LifeLines, TimeSearcher, Palantir, DataMontage
Cone/Cam/Hyperbolic, SpaceTree, Treemap
Pajek, JUNG, UCINet, SocialAction, NodeXL
infosthetics.com
flowingdata.com
infovis.org
www.infovis.net/index.php?lang=2
NodeXL:
Network Overview for Discovery & Exploration in Excel
www.codeplex.com/nodexl
NodeXL: Import Dialog Box
www.codeplex.com/nodexl
NodeXL: Senate Voting Patterns
www.codeplex.com/nodexl
casci.umd.edu/NodeXL_Teaching
Tweets at #WIN09 Conference: 2 groups
WWW2010 Twitter Community
CHI2010 Twitter Community
www.codeplex.com/nodexl/
Flickr networks
Flickr clusters for “mouse”
Computer
Animal
Mickey
Figure 7.11. : Lobbying Coalition Network connecting organizations (vertices) that have jointly filed
comments on US Federal Communications Commission policies (edges). Vertex Size represents
number of filings and color represents Eigenvector Centrality (pink = higher). Darker edges connect
organizations with many joint filings. Vertices were originally positioned using FruchtermanRheingold and hand-positioned to respect clusters identified by NodeXL’s Find Clusters algorithm.
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
http://www.elsevier.com/wps/find/bookdescription.cws_home/723354/description
Social Media Research Foundation
Social Media Research Foundation
smrfoundation.org
We are a group of researchers who
want to create open tools, generate
and host open data, and support open
scholarship related to social media.
smrfoundation.org
The STICK Project
•  NSF SciSIP Program
- Science of Science & Innovation Policy
•  Goal: Scientific approach to science policy
•  The STICK Project
- Science & Technology Innovation Concept Knowledge-base
•  Goal: Monitoring, Understanding, and Advancing
the (R)Evolution of Science & Technology Innovations
The STICK Project
•  Long-term objectives
•  Database of IT, biotech & nanotech innovations
•  Visual analytic tools for monitoring & sensemaking
•  Theories of innovation trajectories
•  Near-term objectives
•  Ontology for innovations & people
•  Search & cleaning tools
•  Case studies
•  Cloud computing
•  Tree visualizations: treemaps, cone trees & hyperbolic trees
Early Work: Fashions in IT Innovations
Innovation Trajectories: Web Services
Sales
Publications
R&D Investment
Sources: LexisNexis for publications; Gartner for R&D investment; IDC for Application Sales
Case Study: Cloud computing
Treemap: Gene Ontology
+  Space filling
+  Space limited
+  Color coding
+  Size coding
- Requires learning
(Shneiderman, ACM Trans. on Graphics, 1992 & 2003)
www.cs.umd.edu/hcil/treemap/
Treemap: Smartmoney MarketMap
www.smartmoney.com/marketmap
Market falls steeply Feb 27, 2007, with one exception
Market falls steeply May 20, 2010
Market mixed, February 8, 2008
Energy & Technology up, Financial & Health Care down
Market rises 319 points, November 13, 2007,
with 5 exceptions
Treemap: Newsmap (Marcos Weskamp)
newsmap.jp
Treemap: Supply Chain
www.hivegroup.com
Treemap: Integrated, Action-Oriented
www.hivegroup.com
Treemap: Spotfire Bond Portfolio Analysis
www.spotfire.com
Treemap: NY Times – Car&Truck Sales
www.cs.umd.edu/hcil/treemap/
Cone tree: 3D Interactive Animations
Interactive 3D animated user interfaces
(Robertson, Card & Mackinlay, ACM CHI 1991 & CACM 1993)
Hyperbolic Trees: Focus & Context
(Lamping & Rao, ACM UIST1994 & CHI 1995)
Patents
Academic
Papers
Trade Press
Articles
Case Study: Tree Visualization Impact
TM=Treemaps
CT=Cone Trees
HT=Hyperbolic Trees
Patents
Academic
Papers
Case Study: Tree Visualization Citations
TM=Treemaps
CT=Cone Trees
HT=Hyperbolic Trees
Provocative Explanations
•  3D visualizations look great,
but have limitations for visual analytics
•  Animation is appealing, but it undermines
spatial stability
•  Patents may limit innovation if applied too early
•  Working with real users on real problems helps
•  Evaluations can accelerate refinements &
focus attention on high payoff tasks
Take Away Messages
•  Crossing the Chasm still has valuable lessons
•  Diffusion of Innovation guides the process
•  Studying innovation trends will be accelerated by
•  Appropriate ontologies
•  Clearer metrics of success
•  Better search & data cleaning tools
•  Causal theories
Thanks to: National Science Foundation grant SBE-0915645
Ping Wang, Yan Qu & Grad students Cody Dunne & Puneet Sharma
terpconnect.umd.edu/~pwang/STICK/