visual analytics research at tufts

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Visual Analytics Research at Tufts

Remco Chang

Assistant ProfessorTufts University


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


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


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)


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)


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.


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.


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


In Combinations of 2 or 3…


and Interaction



Dissemination



• Data Mining• Machine

Learning• Databases• Information

Retrieval• etc

• InfoVis• SciVis• Graphics• etc


In Combinations of 2 or 3…


and Interaction



Dissemination



• Interaction Design• Cognitive Psychology• Intelligence Analysis• etc.

• Tech Transfer• Report Generation• etc


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.






inspection reports


comparison methodsR. Chang et al., An Interactive Visual Analytics System for Bridge Management, Journal of Computer Graphics Forum, 2010. To Appear.






inspection reports


comparison methodsR. Chang et al., Interactive Coordinated Multiple-View Visualization of Biomechanical Motion Data , IEEE Vis (TVCG) 2009.


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


Examples of Human + Computer Computing

• CAPCHA– RE-CAPCHA– General Crowd-Sourcing

• Adaptive / Intelligent User Interfaces (IUI)

• User assisted clustering / searching


Simple Example

• Distance Function

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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


1st Step: Success

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Trying to separate circled green dots from all blue dots


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


Visual Analytics for Political Science


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


Aggregate Temporal Graph


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.


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


Conclusion


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


Questions?

Thank you!


Backup Slides


(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.


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


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


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)


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.


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”

visual analytics research at tufts

Documents