Visual Analytics for Command, Control, and Interoperability Environments

Visually-Adapted Analytical Techniques

Visually-Adapted Analytical Techniques deals with developing algorithms that focus on leveraging the individual and collective expertise of the 2.3 million DHS personnel by scaling the cognitive effort of the user, thus helping them perform their tasks in a more effective and efficient manner. The analytical algorithms are designed from the start to take into consideration the user, the task, the display, the device and the environment. All of these factors drive the design of the analysis algorithm.


Mission Driven Research Projects(MDRPs):

Visual Analytics for Investigative Analysis on Text Documents

Development of a tool for automated investigative analysis on vast collections of documents which will help law enforcement agencies in detecting embedded plots or threats.
Penn State: MacEachren, Mitra / Georgia Tech: Stasko / SFU: Fisher
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Situation Surveillance & In-field Criminal Investigative Analytics

Develop an exploratory visual analytic mapping method to support criminal investigative analytics by processing large geographically referenced data, then searching for events of interest.
Purdue: Ebert, Boutin, Delp, Cleveland / Penn State: MacEachren, Hardisty / Virginia Tech: Barrett, Eubank, Marathe / Rutgers: Roberts
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Cybersecurity Visual Analytics

Develop a tool to visualize logged network traffic and automating analytical tasks to identify agents behind cyber attacks.
Stanford: Hanrahan, Gerth /Purdue: Cleveland
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Video Surveillance Visual Analytics

This system automatically examines surveillance video for behavioral signs of threats by observing patterns of movement and other telltale signs exhibited by people.
Purdue: Delp / UNCC: Fan, Ribarsky / SFU: Fisher
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Financial Visual Analytics

This system helps the user to analyze large collections of financial data and spot money laundering or fraudulent financing with the help of visual analytics.
UNCC: Kosara, Ribarsky / Purdue: Elmqvist
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Foreign Animal and Zoonotic Disease Visual Analytics

This project focuses on applying new exploratory visual analytics methods for spatial, space-time foreign animal and zoonotic disease data which will lead us to better disease outbreak prediction.
Purdue: Ebert, Cleveland / Penn State: Hardisty, MacEachren
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GeoJunction: Collaborative Visual-Computational Information Foraging and Contextualization to Support Situation Awareness

This project focuses on developing a tool which helps its users to search and contextualize place-time aware data to support situation awareness and interpret evidence and make decisions in a continually changing environment.
Penn State: MacEachren, Mitra / USC: Hovy
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Multimedia Visual Analytics for Investigative Analysis

This tool intelligently categorizes multimedia content like videos, images or documents from different sources for homeland security applications and helps analysts forage through the organized data.
UNCC: Fan, Ribarsky / Purdue: Delp
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Study of Terrorism and Responses to Terrorism (START) Center Visual Analytics

Having collect large amounts of data on terrorism and terrorism response at the START Center, the Visual Analytics component proposes to refine and explore the data by analyzing it visually.
Purdue: Ebert, Elmqvist / UNCC: Kosara / START: Smarick / Rutgers: Roberts, Pottenger
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Visual Analytics for the DHS Center of Excellence Projects

Leverage visual analytics to create an interactive visual database of all research and educational material produced by the Centers for exposure to the scientific community, government agencies and the general public.
Purdue: Elmqvist, Ebert / GeorgiaTech: Stasko / SFU: Dill / Rutgers: Roberts / USC: Hovy / UIUC: Roth
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Overview

VACCINE is dedicated to creating methods and tools to analyze and manage vast amounts of information for all mission areas of homeland security.

View VACCINE Information:


VACCINE Newsletter

     Volume 18: July 2014

     Volume 17: June 2014

     Volume 16: May 2014

     Volume 15: April 2014

     Volume 14: March 2014

     Volume 13: February 2014

     Volume 12: January 2014

     Volume 11: December 2013

     Volume 10: November 2013

     Volume 9: October 2013

     Volume 8: September 2013

     Volume 7: August 2013

     Volume 6: July 2013

     Volume 5: June 2013

     Volume 4: May 2013

     Volume 3: April 2013

     Volume 2: March 2013

     Volume 1: February 2013

 


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