Visual Analytics Research at Tufts

1. Visual Analytics Research at Tufts Remco Chang Assistant Professor Tufts University

2. Problem Statement • The growth of data is exceeding our ability to analyze them. • The amount of digital information generated in the years 2002, 2006, 2010: • 2002: 22 EB (exabytes, 1018) • 2006: 161 EB • 2010: 988 EB (almost 1 ZB) 1: Data courtesy of Dr. Joseph Kielman, DHS 2: Image courtesy of Dr. Maria Zemankova, NSF

3. Problem Statement • The data is often complex, ambiguous, noisy. Analysis of which requires human understanding. • About 2 GB of digital information is being produced per person per year • 95% of the Digital Universe’s information is unstructured 1: Data courtesy of Dr. Joseph Kielman, DHS 2: Image courtesy of Dr. Maria Zemankova, NSF

4. Example: What Does Fraud Look Like? • Financial Institutions like Bank of America have legal responsibilities to report all suspicious activities • Data size: approximately 200,000 transactions per day (73 million transactions per year) • Problems: • Automated approach can only detect known patterns • Bad guys are smart: patterns are constantly changing • No single transaction appears fraudulent • Few experts: fraud detection is considered an “art” • Data is messy: lack of international standards resulting in ambiguous data • Current methods: • 10 analysts monitoring and analyzing all transactions • Using SQL queries and spreadsheet-like interfaces • Limited to the time scale (2 weeks)

5. WireVis: Financial Fraud Analysis • In collaboration with Bank of America • Looks for suspicious wire transactions • Currently beta-deployed at WireWatch • Visualizes 7 million transactions over 1 year • Uses interaction to coordinate four perspectives: • Keywords to Accounts • Keywords to Keywords • Keywords/Accounts over Time • Account similarities (search by example)

6. WireVis: Financial Fraud Analysis Search by Example (Find Similar Accounts) Heatmap View (Accounts to Keywords Relationship) Keyword Network (Keyword Relationships) Strings and Beads (Relationships over Time) R. Chang et al., Scalable and interactive visual analysis of financial wire transactions for fraud detection. Information Visualization,2008. R. Chang et al., Wirevis: Visualization of categorical, time-varying data from financial transactions. IEEE VAST, 2007.

7. What is Visual Analytics? • Visual analytics is the science of analytical reasoning facilitated by interactive visual interfaces [Thomas & Cook 2005] • Since 2004, the field has grown significantly. Aside from tens to hundreds of domestic and international partners, it now hasa IEEE conference (IEEE VAST), an NSF program (FODAVA), and a forthcoming IEEE Transactions journal.

8. Individually Not Unique • Interaction Design • Cognitive Psychology • Intelligence Analysis • etc. Analytical Reasoning and Interaction • Data Mining • Machine Learning • Databases • Information Retrieval • etc Data Representation Transformation Visual Representation • InfoVis • SciVis • Graphics • etc Production, Presentation Dissemination Validation and Evaluation • Tech Transfer • Report Generation • etc • Quality Assurance • User studies (HCI) • etc

9. In Combinations of 2 or 3… Analytical Reasoning and Interaction • Data Mining • Machine Learning • Databases • Information Retrieval • etc Data Representation Transformation Visual Representation • InfoVis • SciVis • Graphics • etc Production, Presentation Dissemination Validation and Evaluation

10. In Combinations of 2 or 3… • Interaction Design • Cognitive Psychology • Intelligence Analysis • etc. Analytical Reasoning and Interaction Data Representation Transformation Visual Representation Production, Presentation Dissemination Validation and Evaluation • Tech Transfer • Report Generation • etc

11. Extending Visual Analytics Principles Who • Global Terrorism Database • Application of the investigative 5 W’s • Bridge Maintenance • Exploring subjective inspection reports • Biomechanical Motion • Interactive motion comparison methods Where What Evidence Box Original Data When R. Chang et al., Investigative Visual Analysis of Global Terrorism, Journal of Computer Graphics Forum,2008.

12. Extending Visual Analytics Principles • Global Terrorism Database • Application of the investigative 5 W’s • Bridge Maintenance • Exploring subjective inspection reports • Biomechanical Motion • Interactive motion comparison methods R. Chang et al., An Interactive Visual Analytics System for Bridge Management, Journal of Computer Graphics Forum,2010. To Appear.

13. Extending Visual Analytics Principles • Global Terrorism Database • Application of the investigative 5 W’s • Bridge Maintenance • Exploring subjective inspection reports • Biomechanical Motion • Interactive motion comparison methods R. Chang et al., Interactive Coordinated Multiple-View Visualization of Biomechanical Motion Data, IEEE Vis (TVCG) 2009.

14. Human + ComputerA Mixed-Initiative Perspective • So far, our approach is mostly user-driven • Human vs. Artificial Intelligence Garry Kasparov vs. Deep Blue (1997) • Computer takes a “brute force” approach without analysis • “As for how many moves ahead a grandmaster sees,” Kasparov concludes: “Just one, the best one” • Artificial Intelligence vs. Augmented Intelligence Hydra vs. Cyborgs (1998) • Grandmaster + 1 computer > Hydra (equiv. of Deep Blue) • Amateur + 3 computers > Grandmaster + 1 computer1 • How to systematically repeat the success? • Unsupervised machine learning + User • User’s interactions with the computer Computer Translation Human 1. http://www.collisiondetection.net/mt/archives/2010/02/why_cyborgs_are.php

15. Examples of Human + Computer Computing • CAPCHA • RE-CAPCHA • General Crowd-Sourcing • Adaptive / Intelligent User Interfaces (IUI) • User assisted clustering / searching

16. Simple Example • Distance Function

17. Application 1: Find Important Features • Data set: X, 178x13 • 3 classes • add 10 random number columns • as extra features

18. 1st Step: Success Trying to separate circled green dots from all blue dots

19. Result • Recall the structure of data set • Weight vector: • Randomly generated features gets low weights Original Wine Dataset, each instance has 13 feature values 10 Randomly generated feature values for every instance

20. Visual Analytics for Political Science

21. Aggregate Temporal Graph 1000 simulations 60 time steps in each simulation (time step == a node) (edge == transition) Merged time steps if two states are the same

22. Aggregate Temporal Graph

23. Gateways and Terminals Each of the yellow vertices is a Gateway to the vertex set of {A}. That is, every maximal path leaving a yellow vertex eventually passes through A. Vertex G is a Gateway to each of the yellow vertices, or Terminals. That is, every maximal path leaving G passes eventually through each of the yellow vertices.

24. Applications of Aggregate Temporal Graphs • A generalizable representation of problems involving parameter spaces that are too large to explore as a whole, but which are composed of related individual parts can be examined independently • Collaborative Analysis • Each analyst’s trail is a simulation • Each configuration state is a node • Web Analytics • Each visit is a simulation • Each configuration of a page is a node

25. Conclusion • Visual Analytics is a growing new area that is looking to address some pressing needs • Too much (messy) data, too little time • By combining strengths and findings in existing disciplines, we have demonstrated that • There are some great benefits • But there are also some difficult challenges Analytical Reasoning and Interaction Data Representation Transformation Visual Representation Production, Presentation Dissemination Validation and Evaluation

26. Questions? Thank you!

27. Backup Slides

28. (2) Investigative GTD Who Where What Evidence Box Original Data When R. Chang et al., Investigative Visual Analysis of Global Terrorism, Journal of Computer Graphics Forum (Eurovis),2008.

29. (2) Investigative GTD: Revealing Global Strategy This group’s attacks are not bounded by geo-locations but instead, religious beliefs. Its attack patterns changed with its developments. WHY ?

30. (2) Investigative GTD:Discovering Unexpected Temporal Pattern A geographically-bounded entity in the Philippines. Domestic Group The ThemeRiver shows its rise and fall as an entity and its modus operandi.

31. What is in a User’s Interactions? Keyboard, Mouse, etc • Types of Human-Visualization Interactions • Word editing (input heavy, little output) • Browsing, watching a movie (output heavy, little input) • Visual Analysis (closer to 50-50) Input Visualization Human Output Images (monitor)

32. Discussion • What interactivity is not good for: • Presentation • YMMV = “your mileage may vary” • Reproducibility: Users behave differently each time. • Evaluation is difficult due to opportunistic discoveries.. • Often sacrifices accuracy • iPCA – SVD takes time on large datasets, use iterative approximation algorithms such as onlineSVD. • WireVis – Clustering of large datasets is slow. Either pre-compute or use more trivial “binning” methods.

33. Discussion • Interestingly, • It doesn’t save you time… • And it doesn’t make a user more accurate in performing a task. • However, there are empirical evidence that using interactivity: • Users are more engaged (don’t give up) • Users prefer these systems over static (query-based) systems • Users have a faster learning curve • We need better measurements to determine the “benefits of interactivity”