Visual analytic tools for monitoring and understanding the emergence and evolution of innovations in science & technology

Transcription

1 Visual analytic tools for monitoring and understanding the emergence and evolution of innovations in science & technology Cody Dunne Dept. of Computer Science and Human-Computer Interaction Lab, University of Maryland Links from this talk: bit.ly/stmwant OECD KNOWINNO Workshop November 14-15, 2011 Alexandria, VA, USA 1

2 Outline 1. Academic literature exploration 2. Case study: Tree visualization techniques 3. Case study: Business intelligence news 4. Case study: Pennsylvania innovations 5. STICK approach 2

3 1. Academic literature exploration Users are looking for: 1. Foundations 2. Emerging research topics 3. State of the art/open problems 4. Collaborations & relationships between Communities 5. Field evolution 6. Easily understandable surveys 3

4 Action Science Explorer 4

5 User requirements Control over the paper collection Choose custom subset via query, then iteratively drill down, filter, & refine Overview either as visualization or text statistics Orient within subset Easy to understand metrics for identifying interesting papers Ranking & filtering Create groups & annotate with findings Organize discovery process Share results 5

6 Action Science Explorer Bibliometric lexical link mining to create a citation network and citation context Network clustering and multi-document summarization to extract key points Potent network analysis and visualization tools 6

7 2. Case study: Tree visualization Problem: Traditional 2D node-link diagrams of trees become too large Solutions: Treemaps: Nested Rectangles Cone Trees: 3D Interactive Animations Hyperbolic Trees: Focus + Context Measures: Papers, articles, patents, citations, Press releases, blog posts, tweets, Users, downloads, sales, 7

8 Treemaps: nested rectangles 8

9 Smartmoney MarketMap Feb 27, 2007 smartmoney.com/marketmap 9

10 Cone trees: 3D interactive animations Robertson, G. G., Card, S. K., and Mackinlay, J. D., Information visualization using 3D interactive animation, Communications of the ACM, 36, 4 (1993), Robertson, G. G., Mackinlay, J. D., and Card, S. K., Cone trees: Animated 3D visualizations of hierarchical information, 10 Proc. ACM SIGCHI Conference on Human Factors in Computing Systems, ACM Press, New York, (April 1991),

11 Hyperbolic trees: focus & context Lamping, J. and Rao, R., Laying out and visualizing large trees using a hyper-bolic space, Proc. 7th Annual ACM symposium on User Interface Software and Technology, ACM Press, New York (1994), Lamping, J., Rao, R., and Pirolli, P., A focus+context technique based on hy-perbolic geometry for visualizing large 11 hierarchies, Proc. SIGCHI Conference on Human Factors in Computing Systems, ACM Press, New York (1995),

12 Tree visualization publishing Trade Press Articles TM=Treemaps CT=Cone Trees HT=Hyperbolic Trees Patents Academic Papers 12

13 Tree visualization citations Academic Papers TM=Treemaps CT=Cone Trees HT=Hyperbolic Trees Patents 13

14 Insights Emerging ideas may benefit from open access Compelling demonstrations with familiar applications help Many components to commercial success 2D visualizations w/spatial stability successful Term disambiguation & data cleaning are hard Shneiderman, B., Dunne, C., Sharma, P. & Wang, P. (2011), "Innovation trajectories for information visualizations: Comparing treemaps, cone trees, and hyperbolic trees", Information Visualization. 14

15 3. Case study: Business intelligence news Proquest Term Frequency Term Frequency hyperion 3122 decision support system 39 data mining 889 business process reengineering 36 business intelligence 434 data mart 29 knowledge mgmt. 221 business analytics 21 data warehouse 207 text mining 19 data warehousing 139 predictive analytics 18 cognos 112 business performance mgmt 6 competitive intelligence 86 online analytical processing 5 electronic data itrch. 69 knowledge discovery in database 1 meta data 69 ad hoc query 1 15

16 PQ Business Intelligence Co-occurrence of concepts with organizations Frequency Data Mining National Security Agency NSA White House FBI AT&T American Civil Liberties Union Electronic Frontier Foundation Dept. of Homeland Security CIA Year

17 Business Intelligence Matrix showing Co- Occurrence of concepts and orgs. 18

18 Business Intelligence : (subset) 19

19 Business Intelligence : Data mining NSA CIA FBI White House Pentagon DOD DHS AT&T ACLU EFF Senate Judiciar Committee 20

20 Business Intelligence : Tech1 Google Yahoo Stanford Apple Tech2 IBM, Cognos Microsoft Oracle Finance NASDAQ NYSE SEC NCR MicroStrategy 21

21 Business Intelligence : Air Force Army Navy GSA UMD* 22

22 Insights Useful groupings in PQ BI terms based on events and long-term collaborators Interactive line charts useful for looking at cooccurrence relationships over time Clustered heatmaps useful for overall cooccurrence relationships stick.ischool.umd.edu 23

23 4. Case study: Pennsylvania innovations Innovation relationships during 1990 State & federal funding Patents (both strong and weak ties) Location Connecting State & federal agencies Universities Firms Inventors 24

24 Patent Tech 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

25 Patent Tech 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

26 No Location Philadelphia Patent Tech Navy SBIR (federal) PA DCED (state) Related patent Pharmaceutical/Medical Pittsburgh Metro 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 Westinghouse Electric 19: Other states

27 No Location Philadelphia Navy Patent Tech SBIR (federal) PA DCED (state) Related patent Pharmaceutical/Medical Pittsburgh Metro 2: Federal agency 3: Enterprise 5: Inventors 9: Universities 10: PA DCED 11/12: Phil/Pitt metro cnty 13-15: Semi-rural/rural cnty Westinghouse Electric 17: Foreign countries 19: Other states

28 Insights Meta-layouts useful for showing: Groups (clusters, attributes, manual) Relationships between them User comments We've never been able to see anything like this This is going to be huge" 29

29 5. STICK approach 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

30 STICK approach cont Scientific, data-driven way to track innovations Vs. current expert-based, time consuming approaches (e.g., Gartner s Hype Cycle, tire track diagrams) Includes both concept and product forms Study relationships between Study the innovation ecosystem Organizations & people Both those producing & using innovations stick.ischool.umd.edu 31

31 STICK Process (overview) Identify concepts Business intelligence, cloud computing, customer relationship management, health IT, web 2.0, electronic health records, biotech Query data sources Processing Automatic entity recognition Crowd-sourced verification Co-occurrence networks Visualizing & analyzing Overall statistics Co-occurrence networks Network evolution Sharing results News Dissertation Academic Patent Blogs 32

32 Process 1. Collecting 2. Processing 3. Visualizing & Analyzing 4. Collaborating Cleaning

33 Collecting Identify Concepts Begin with target concepts Business Intelligence Health IT Cloud Computing Customer Relationship Management Web 2.0 Personal Health Records Nanotechnology Develop sub concepts from domain experts, wikis Data Sources News Dissertation Academic Patent Blogs

34 Collecting (2) Form & Expand Queries ABS( "customer relationship management" OR "customers relationship management" OR "customer relation management" ) OR TEXT( ) OR SUB( ) OR TI( ) Scrape Results

35 Processing Automatic Entity Recognition BBN IdentiFinder Crowd-Sourced Verification Extract most frequent 25% Assign to CrowdFlower Workers check organization names and sample sentences

36 Processing (2) Compute Co-Occurrence Networks Overall edge weights Slice by time to see network evolution Output CSV GraphML

37 Visualizing & Analyzing Spotfire Import CSV, Database Standard charts Multiple coordinated views Highly scalable NodeXL CSV, Spigots, GraphML Automate feature Batch analysis & visualization Excel 2007/2010 template

38 Shared data & analysis repositories Online Research Community Share data, tools, results Data & analysis downloads Spotfire Web Player Communication Co-creation, co-authoring stick.ischool.umd.edu/community 39

39 Ongoing Work Collecting: Processing: Visualizing & Analyzing: Collaborating: Additional data sources and queries Improving entity recognition accuracy Visualizing network evolution Co-occurrence network sliced by time Develop the STICK Open Community site Motivate user participation Improve the resources available Invitation-only testing

40 Outline 1. Academic literature exploration Citation networks and text summarization 2. Case study: Tree visualization techniques Papers, patents, and trade press articles 3. Case study: Business intelligence news News term co-occurrence 4. Case study: Pennsylvania innovations Patents, funding, and locations 5. STICK approach Tracking innovations across papers, patents, news articles, and blog posts 41

41 Take Away Messages Easier scientific, data-driven innovation analysis: Automatic collection & processing of innovation data Easy access to visual analytic tools for finding clusters, trends, outliers Communities for sharing data, tools, & results

42 Visual analytic tools for monitoring and understanding the emergence and evolution of innovations in science & technology Cody Dunne Dept. of Computer Science and Human-Computer Interaction Lab, University of Maryland Links from this talk: bit.ly/stmwant This work has been partially supported by NSF grants IIS (ASE) and SBE (STICK) 43