dist funcintrovaappsatgwrap-up 1/25 visual analytics research at tufts remco chang assistant...
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Dist FuncIntro VA Apps ATG Wrap-up1/25
Visual Analytics Research at Tufts
Remco Chang
Assistant ProfessorTufts University
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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, DHS2: Image courtesy of Dr. Maria Zemankova, NSF
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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, DHS2: Image courtesy of Dr. Maria Zemankova, NSF
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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)
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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)
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WireVis: Financial Fraud Analysis
Heatmap View(Accounts to Keywords Relationship)
Strings and Beads(Relationships over Time)
Search by Example (Find Similar Accounts)
Keyword Network(Keyword Relationships)
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.
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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 has a IEEE conference (IEEE VAST), an NSF program (FODAVA), and a forthcoming IEEE Transactions journal.
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Individually Not Unique
Analytical Reasoning
and Interaction
Visual Representation
Production, Presentation
Dissemination
Data Representation Transformation
Validation and Evaluation
• Data Mining• Machine
Learning• Databases• Information
Retrieval• etc
• Tech Transfer• Report Generation• etc
• Quality Assurance• User studies (HCI)• etc
• Interaction Design• Cognitive Psychology• Intelligence Analysis• etc.
• InfoVis• SciVis• Graphics• etc
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In Combinations of 2 or 3…
Analytical Reasoning
and Interaction
Visual Representation
Production, Presentation
Dissemination
Data Representation Transformation
Validation and Evaluation
• Data Mining• Machine
Learning• Databases• Information
Retrieval• etc
• InfoVis• SciVis• Graphics• etc
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In Combinations of 2 or 3…
Analytical Reasoning
and Interaction
Visual Representation
Production, Presentation
Dissemination
Data Representation Transformation
Validation and Evaluation
• Interaction Design• Cognitive Psychology• Intelligence Analysis• etc.
• Tech Transfer• Report Generation• etc
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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
Where
When
Who
What
Original Data
EvidenceBox
R. Chang et al., Investigative Visual Analysis of Global Terrorism, Journal of Computer Graphics Forum, 2008.
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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 methodsR. Chang et al., An Interactive Visual Analytics System for Bridge Management, Journal of Computer Graphics Forum, 2010. To Appear.
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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 methodsR. Chang et al., Interactive Coordinated Multiple-View Visualization of Biomechanical Motion Data , IEEE Vis (TVCG) 2009.
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Human + ComputerA Mixed-Initiative Perspective• So far, our approach is mostly user-driven
• Human vs. Artificial IntelligenceGarry 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 IntelligenceHydra 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
1. http://www.collisiondetection.net/mt/archives/2010/02/why_cyborgs_are.php
Computer Translation Human
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Examples of Human + Computer Computing
• CAPCHA– RE-CAPCHA– General Crowd-Sourcing
• Adaptive / Intelligent User Interfaces (IUI)
• User assisted clustering / searching
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Simple Example
• Distance Function
other
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Application 1: Find Important Features
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0.2• Data set: X, 178x13• 3 classes • add 10 random number columns as extra features
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1st Step: Success
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Trying to separate circled green dots from all blue dots
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Result
• Recall the structure of data set
• Weight vector:– Randomly generated features gets low weights
0.096 0.150 0.062 0 0.018 0.011 0.025 0.039 0.037 0.047 0.091 0.186 0.127
0.038 0.011 0 0.017 0 0.046 0 0 0 0
Original Wine Dataset, each instance has 13
feature values
10 Randomly generated feature values for every
instance
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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
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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.
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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
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Conclusion
Analytical Reasoning
and Interaction
Visual Representa
tion
Production, Presentatio
n Disseminati
on
Data Representat
ion Transformat
ion
Validation and
Evaluation
• 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
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(2) Investigative GTD
Where
When
Who
What
Original Data
EvidenceBox
R. Chang et al., Investigative Visual Analysis of Global Terrorism, Journal of Computer Graphics Forum (Eurovis), 2008.
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WHY?
This group’s attacks are not bounded by geo-locations but instead, religious beliefs.
Its attack patterns changed with its developments.
(2) Investigative GTD: Revealing Global Strategy
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Domestic Group
A geographically-bounded entity in the Philippines.
The ThemeRiver shows its rise and fall as an entity and its modus operandi.
(2) Investigative GTD:Discovering Unexpected Temporal Pattern
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What is in a User’s Interactions?
• 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)
Visualization HumanOutput
Input
Keyboard, Mouse, etc
Images (monitor)
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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.
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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”
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