Center for Visualization and Data Analytics (CVADA)
Rutgers, the State University of New Jersey
4th Floor, CoRE Bldg.
96 Frelinghuysen Road
Piscataway, NJ 08854-8018
At the Center for Visualization and Data Analytics (CVADA) researchers and educators develop faster ways for data to be collected, distilled, managed, visualized, understood, and shared before, during, and after a crisis. CVADA is creating a foundation in visual and data analytics to enable swiftly sifting through a tsunami of information, in diverse forms, to get early warning of potential threats.
Dr. David S. Ebert, Director, VACCINE
Dr. Fred S. Roberts, Director, CCICADA
CVADA Research Areas:
Public Safety Coalition Projects
Enterprise Resiliency Experiments
Sports Evacuation Planning
Visual Analytics for Security Applications
Information and Gathering Distillation
Information Networks and Analysis
Information-Driven Modeling and Simulation
Information-Driven Decision Making
Boat Allocation Module (BAM)
Boat Allocation Module (BAM2)
Aviation Capability and Capacity Allocation Module (ACCAM)
Unnacompanied Alien Children (UAC)
A Data Integration Framework for Enhancing Emergency Response Situation Reports with Multi-Agency, Multi-Partner Multimedia Data; Public Safety Coalition Projects
Analytical Visualization of the Port Arthur, TX Economic Impact Study
Analyzing High-dimensional Multivariate Network Links with Integrated Anomaly Detection, Highlighting and Exploration
Bristle Maps - A Multivariate Abstraction Technique for Geovisualization
CCC Tech Support
CES - Student Travel
cgSARVA - Coast Guard Search and Rescue Visual Analytics
Chicago LTE Project
COAST - Coastal Operations and Analysis Suite of Tools
Coast Guard PROTECT Visualization
Collegiate Cybersecurity Defense Competitive System (CCDC)
Crisis Informatics Course Development
(Crowdsourcing) Combining Crowdsourcing technology with machine learning to do visual analytics on big qualitative data (video datasets).
Cybersecurity Visual Analytics
Design & Development of the Artifact Genome Project (AGP)
Disposable Cell Phone Analysis
Distributed Rendering for Web-Enabling the Stadium Evacuation Planning Tool
Explore Impact of Visualization on Predictive Analysis
Financial Fraud Visual Analytics
Foreign Animal and Zoonotic Disease Visual Analytics
GARI - Gang Graffiti Image Recognition and Interpretation
GeoJunction: Collaborative Visual-Computational Information Foraging and Contextualization to Support Situation Awareness
(GeoViz & CrimeViz) Situational Surveillance & In-field Criminal Investigative Analytics
GeoTxt.org - A Web Service to Geo-Locate Places in Microblog Posts and Other Textual Information Sources
HS-STEM Career Development Program
iLEAPS - iLaw Enforcement Apps Assistance Program for Students
Impacts on Visualization Literacy on Performance of Visual Analytics
Introducing Sustainable Visual Analytics into Command Center Environments
Jigsaw - Visual Analytics for Investigative Analysis on Document Collections
Justice Institute of British Columbia and VACCINE Collaborative Workshops
Leadership and Coordination
Liberty Project - Red Cross
MADIS - A Data Integration Framework for Enhancing Emergency Response Situation Reports with Multi-Agency, Multi-Partner Multimedia Data
Measuring & Visualizing Information Trustworthiness Using Visual Analytics
MERGE - Mobile Emergency Response GuidE
Mobile Application Communication
Mobile 3D Routing, Emergency Evacuation, and In-Field Criminal Investigative Analytics
Multimedia, Social Media, Text, and Emergency Response Analytics
Multimedia Visual Analytics for Investigative Analysis
Officer Performance Visualization System
ORAM - Operational Risk Assessment Module Visualization
Physical Extraction & Reconstruction of Evidence from Mobile Phones Using JTAG Test Ports
Remote Airborne Sensing Technology for Emergency Responders (RASTER)
(Rosetta Phone) Mobile Imaging, Rosetta Phone, and Light-Weight Visual Analytics for In-Field Analytics
Safety in View: A Public Safety Visual Analytics Tool Based on CCTV Camera Angles of View
SensePlace 2 - Collaborative Visual-Computational Information Foraging and Contextualization to Support Situation Awareness
SMART- Social Media Analytics and Reporting Toolkit - Real time Twitter Analysis
Social Media and Healthcare Analytics for Identification of Emerging Health Threats
(START) START Center Visual Analytics
Symbol Store - Supporting Map Symbol Interoperability
Tech Contract 7 Support for the Cybersecurity Research & Development Program
The Uncertainty of Identity
TRIP - Travel Response Investigative Profiler
User Adoption Learning Tool (Ulearning)
VALET - Visual Analytics Law Enforcement Toolkit
VASA - Visual Analytics for Security Applications
Video Surveillance Visual Analytics
Visual Analytics Decision Support Environment for Epidemic Modeling and Response Evaluation
Visual Analytics Environment for Public Health Surveillance
Visual Analytics for the DHS Centers of Excellence
Visual Analytics of Microblog Data for Public Response Behavioral Analysis in Disaster Events
(VADL) Educational Materials
CCICADA Initiates the DMARA Project to aid the Coast Guard’s Resource Allocation Challenges in the Arctic
The Arctic is a major area of emphasis for the U. S. Coast Guard (USCG) because of the rapidly changing climate and resulting impact on ice conditions and the stress on USCG areas of responsibility. Following on two visits to USCG District 17 (D17) offices in Juneau, Alaska, by CCICADA partners from Rensselaer Polytechnic Institute (RPI), CCICADA has reached agreement with D17 to undertake a project to formulate models designed to analyze and support decisions concerning current, anticipated, and proposed operations of the Coast Guard in the Arctic, specifically in the Bering Strait region. The Dynamic Modeling for Arctic Resource Allocation (DMARA) project has been developed in conjunction with USCG D17 leadership, and USCG D17 Operations and Logistics staff, consistent with D17 Arctic Shield 2013 priorities.
Three specific modeling questions related to USCG resource allocation were identified for further investigation under the DMARA Project: (1) deployment and resource allocation of communications technology for vessel tracking and monitoring in the Bering Straits; (2) dynamic models of the USCG supply chain in D17; and (3) logistics planning for oil spill response resources in the Arctic. Phase 1 of the project will focus primarily on question 3 - resource allocation modeling for oil spill response.
Resource allocation in the Arctic is a persistent and complex challenge that is at the center of many USCG missions, including navigational safety, oil spill response, search and rescue, and traffic management. The Arctic is an immense, seasonally-variable waterway with very little development along its shores. Access to the Chukchi and Beaufort Seas in the western Arctic Ocean occurs through the Bering Strait, a focus of growing interest as marine traffic increases in warmer and longer ice-free Arctic seasons. The Arctic is an environmentally harsh and sensitive area with little commercial, maritime or safety infrastructure, and great distances to access resources in the case of a maritime, personnel casualty or oil spill event.
In the Arctic, as elsewhere, logistics--the procurement, maintenance and transportation of materials, facilities and personnel—is dependent upon existing infrastructure. Lack of infrastructure makes logistics challenging and heightens the need for comprehensive and thoughtful resource allocation models. In the absence of shore-based infrastructure, long-range planning for refueling and replenishment are required. Distances between ports, coupled with the unpredictability of weather, sea states and environmental conditions, complicate access, deployment and supply of critical resources, as well as removal of waste and, in the case of oil spills, recovered product and waste. Public expectations for four-season response capability in the event of an incident also increase the need for thoughtful and flexible planning and robust resource allocation models.
Currently, USCG policy favors seasonal surges of technology, personnel and equipment, supported by industrial contracts for deployable resources, rather than shore-based, pre-positioned assets. Initially, the DMARA project will assess the tradeoffs and net benefits associated with different asset allocation strategies in the Arctic/Bering Strait for oil spill response, one of the USCG key Arctic missions. Other missions—search and rescue, navigational safety or traffic management, etc.—can be explored in follow-on efforts.
The DMARA project will provide the USCG with robust models that will permit examination of persistent resource allocation challenges, as well as examine strengths and vulnerabilities of existing and potential bilateral agreements for oil spill response. Included in this assessment will be an examination of the net benefits of development of deepwater port resources in various settings (Port Clarence, Kotzebue, Kodiak, etc.), and an examination of the importance of rail and/or road transportation infrastructure linking Nome, Kotzebue and Point Clarence, between 65N and 66N on the Seward Peninsula. The models will also consider tradeoffs and options associated with forward deployment, surge deployment and permanent deployment of needed resources for USCG Arctic oil spill response. Other USCG missions, such as navigational safety, search and rescue, or traffic management, can be investigated in subsequent projects.
The first phase of the DMARA project will develop a model that allows decision-makers to assess the tradeoffs between pre-event resource expenditures and post-event response results, including time to an appropriate response and impacts of an incident. The portion of the model considering post-event response will incorporate constraints on the transport of resources from their initial locations to a spill site or appropriate staging area. The modeling approach will be flexible enough to consider response capabilities for multiple distinct geographical regions (e.g., Bering Strait, North Slope Borough, Northwest Borough, Chukchi Sea, Beaufort Sea, etc.) and can incorporate regional priorities. The model can examine resource allocation and budget expenditures over a long planning horizon (5-10 years) and thus can assess various levels of investment into long-term infrastructure capabilities, permanent pre-positioned resources, and seasonal resource surges.
Following development of the initial project, the goal of a follow-on long-term study is to develop models that provide the USCG with robust plans for other missions in the face of dynamic uncertainties. The proposed models can focus both on near-term (e.g., as drilling in the Arctic scales up) and long-term (e.g., the ‘steady-state’ of Arctic drilling operations) response capabilities of the USCG. The models can consider not only where to locate response equipment, resources, and bases but when to locate these response resources. The timing of this location becomes important in both planning robustly for the uncertainties in the environment and in how Arctic operations will scale up over the near-term.
U.S. Coast Guard Accredits Analytical System Developed by VACCINE
In efforts to prioritize and efficiently manage the repair of boats and stations damaged by Superstorm Sandy, the U.S. Coast Guard has accredited a system called Coast Guard Search and Rescue Visual Analytics (cgSARVA) developed in collaboration with Purdue University.
The Coast Guard accredited the system on April 22, 2013, at its headquarters in Washington, D.C.
The cgSARVA tool was created by researchers at the Purdue-led center Visual Analytics for Command, Control and Interoperability Environments, or VACCINE, a U.S. Department of Homeland Security Center of Excellence.
"The accreditation is the first time anything produced by a DHS Center of Excellence has been verified and validated for use by the Coast Guard," says David Ebert, VACCINE director and Silicon Valley Professor of Electrical and Computer Engineering. "The cgSARVA tool can help DHS agencies and law enforcement agencies across the country."
The tool has enabled an interactive visualization, analysis and assessment of search-and-rescue missions completed by each Coast Guard station in hurricane stricken parts of New York and New Jersey.
"The cgSARVA tool is especially helpful in guiding operations and resource decisions by carefully analyzing data in a way that ensures the best return on investment," says Vice Adm. Rob Parker, Coast Guard Atlantic Area commander. "This project serves as a great example of positive partnerships that are being forged between the Coast Guard, the DHS Center of Excellence, and academia."
Purdue initially designed the computer-based visualization to help Coast Guard analysts assess adjustments to boat stations and capabilities on the Great Lakes. It was later used in the Mid-Atlantic region to reallocate resources for Hurricane Irene in 2011 and last year in the aftermath of Superstorm Sandy, which severely damaged 14 Coast Guard stations in the region. The Coast Guard is using the tool to prioritize rebuilding of damaged stations and to help determine which stations should and shouldn't be rebuilt.
"The system can look at what happens if you were not able to immediately rebuild a given station with a certain search-and-rescue caseload," Ebert says. "How long would it take other stations to respond if this station were not here? And if this station were not here, how many cases would have to be handled simultaneously by nearby stations? So it doesn't take all the input and give a final answer, but it provides criteria of the workload and the benefit and what happens if a station closes."
Following Superstorm Sandy, Coast Guard analysts were charged with prioritizing the rebuilding of damaged small-boat stations to determine the order in which stations were to be repaired.
"The cgSARVA model formulation proved to be tremendously insightful for the Coast Guard as it began to prioritize the repair of its stations," says Commander Kevin Hanson, analysis team leader. "Even upon receiving full funding for all damages, the Coast Guard is unable to execute all repairs at the same time and the outputs from cgSARVA have been instrumental in assisting senior leadership in prioritizing work."
Using cgSARVA, the Coast Guard was able to quickly and easily determine how resources might be reallocated in New Jersey, allowing the Coast Guard to operate with increased efficiency.
"A remarkable amount of intellectual rigor has brought us to this point," says Rear Admiral Dean Lee, Deputy for Operations Policy and Capability. "Our analysis team here at headquarters saw tremendous potential in the initial version of cgSARVA and had the organizational vision to expand its capabilities for inclusion in their strategic modeling efforts. Our partnering with Purdue University and the Research and Development Center has yielded insight into our coastal operations that we have never achieved before."
Three Purdue graduate students have been involved in the cgSARVA project, which is ongoing, with researchers continuing to add new capabilities.
The computer-based modeling tool runs on an ordinary computer or laptop.
VACCINE In the News
Nelson, C. & Pottenger, W. (2013 - accepted). Optimization of Emergency Response Using Higher Order Learning and Clustering of 911 Text Messages. The 13th annual IEEE Conference on Technologies for Homeland Security (HST ’13), November 2013, Boston MA.
Chalupsky, H., DeMarco, R., Hovy, E., Kantor, P., Matlin, A., Mitra, P., Ozbas, B., Roberts, F. & Xie, M. (2013 - accepted). Lecture Notes in Computer Science - ADT 2013.
- Shehzad Afzal, Isaac Cho, Calvin Yau, Junghoon Chae, Sungahn Ko, Abish Malik, Kaethe Beck, William Ribarsky, and David Ebert. "Anomaly Exploration and Visual Analytics of Financial Data." Submitted to IEEE VAST 2015.
- Todd Eaglin, Xiaoyu Wang, and William Ribarsky. "Interactive Visual Analytics in Support of Image-Encoded LIDAR Analysis." Submitted to IEEE Symposium on Large Data Analysis and Visualization (LDAV 2015).
- Shehzad Afzal, Isaac Cho, et al. "A Survey of Visual Analysis Approaches for Financial Data Exploration." Submitted to IEEE Transaction on Visualization and Computer Graphics.
- Sebastian Mittelstaedt, Xiaoyu Wang, Todd Eaglin, Dennis Thom, Daniel A. Keim, Thomas Ertl, William Tolone, and William Ribarsky. "An Integrated In-Situ Approach to Impacts from Natural Disasters on Critical Infrastructures." Submitted to HICSS 2015.
- Todd Eaglin, Xiaoyu Wang, William Ribarsky, and William Tolone. "Ensemble Visual Analysis Architecture with High Mobility for Large-Scale Critical Infrastructure Simulations." IS&T/SPIE VDA 2015, Vol.9397-3, pp. 1-15.
- Wenwen Dou, Li Yu, Thomas Kraft, William Ribarsky, and Xiaoyu Wang. "DemographicVis: Analyzing Demographic Information based on User Generated Content." To be published. IEEE VAST 2015.
- Sungahn Ko, Jieqiong Zhao, Jing Xia, Xiaoyu Wang, Greg Abram, Niklas Elmqvist, Shaun Kennedy, Kelly Gaither, William Tolone, William Ribarsky, and David S. Ebert. "VASA: Interactive Computational Steering of Large Asynchronous Simulation Pipelines for Critical Infrastructure." IEEE Transactions on Visualization and Computer Graphics (IEEE VAST, November, 2014), 20(12), pp. 1853-1862.
- Sungahn Ko, Jieqiong Zhao, Jing Xia, Shehzad Afzal, Xiaoyu Wang, Greg Abram, Niklas Elmqvist,, Len Kne, David Van Riper, Kelly Gaither, Shaun Kennedy, William Tolone, William Ribarsky, David S. Ebert, "VASA: Interactive Computational Steering of Large Asynchronous Simulation Pipelines for Societal Infrastructure," IEEE Transactions on Visualization and Computer Graphics, 20 (12):1853-1862, 2014
- A. M. M. Razip, A. Malik, S. Afzal, S. Joshi, R. Maciejewski, Y. Jang, N. Elmqvist, and D. S. Ebert. "A Mobile Visual Analytics Approach for Situational Awareness and Risk Assessment." Proceedings of IEEE PacificVis, 2014.
- Abish Malik, Ross Maciejewski, Sean McCullough, Sherry Towers, David S. Ebert. "Proactive Spatiotemporal Resource Allocation and Predictive Visual Analytics for Community Policing and Law Enforcement." IEEE Transactions on Visualization and Computer Graphics.
- Zhang, Jiawei; Chae, Junghoon; Afzal, Shehzad; Malik, Abish; Thom, Dennis; Jang, Yun; Ertl, Thomas; Matei, Sorin A.; Ebert, David S.: "Visual Analytics of User Influence and Location-Based Social Networks." In: Sorin Matei, Martha Russell, Elisa Bertino: Transparancy in Social Media. Heidelberg: Springer, 2015.
- Chae, J., Cui, Y., Jang, Y., Wang, G., Malik, A., Ebert, D., “Trajectory-based Visual Analytics for Anomalous Human Movement Analysis using Social Media,” Eurovis Workshop on Visual Analytics, 2015.
- Chae, J., Thom, D., Jang, Y., Kim, S., Ertl, T., Ebert, D., "Visual Analytics of Microblog Data for Public Behavior Response Analysis in Disaster Events", extended journal paper, Computers and Graphics, 2014.
- Chae, J., Thom, D., Jang, Y., Kim, S., Ertl, T., Ebert, D., "Visual Analytics of Microblog Data for Public Behavior Analysis in Disaster Events," Eurovis Workshop on Visual Analytics, 2013.
- Hanye Xu; Tay, J.; Malik, A.; Afzal, S.; Ebert, D.S., "Safety in view: A public safety visual analytics tool based on CCTV camera angles of view," in Technologies for Homeland Security (HST), 2015 IEEE International Symposium on, vol., no., pp.1-6, 14-16 April 2015
- Sungahnn Ko, Shehzad Afzal, Simon Walton, Yang Yang, Junghoon Chae, Abish Malik, Yun Jang, Min Chen and David Ebert, "Analyzing High-dimensional Multivariate Network Links with Integrated Anomaly Detection, Highlighting and Exploration," In Proceedings of IEEE Visual Analytics Science and Technology, p83-92, 2014
- J. Ribera, K. Tahboub and E. J. Delp, “Automated crowd flow estimation enhanced by crowdsourcing,” Proceedings of the IEEE National Aerospace and Electronics Conference (NAECON), June 2014, Dayton, OH. B. Delgado, K. Tahboub and E. J. Delp, “Automatic detection of abnormal human events of train platforms,” Proceedings of the IEEE National Aerospace and Electronics Conference (NAECON), June 2014, Dayton, OH.
- B. Zhao and E. J. Delp, “Visual Saliency Models Based on Spectrum Processing,” Proceedings of the IEEE Winter Conference on Applications of Computer Vision, January 2015, Hawaii, pp. 976-981.
- K. Tahboub, N. Gadgil, J. Ribera, B. Delgado, and E. J. Delp, "An Intelligent Crowdsourcing System for Forensic Analysis of Surveillance Video," Proceedings of the IS&T/SPIE Conference on Video Surveillance and Transportation Imaging Applications, vol. 9407, San Francisco, February 2015.
- J. Kim, A. Parra, H. Li, E. J. Delp, "Efficient Graph-Cut Tattoo Segmentation," Proceedings of the IS&T/SPIE Conference on Visual Information Processing and Communication, vol. 9410, San Francisco, February 2015.
- J. Ribera, K. Tahboub, and E. J. Delp, "Characterizing The Uncertainty of Classification Methods and Its Impact on the Performance of Crowdsourcing," Proceedings of the IS&T/SPIE Conference on Imaging and Multimedia Analytics in a Web and Mobile World, vol. 9408, San Francisco, February 2015
- Wallgrün, J.O., Karimzadeh, M., MacEachren, A.M., Hardisty, F., Pezanowski, S. and Ju, Y. 2014: "Construction and First Analysis of a Corpus for the Evaluation and Training of Microblog/Twitter Geoparsers." In Purves, R. and Jones, C., editors, GIR'14: 8th ACM SIGSPATIAL Workshop on Geographic Information Retrieval, Dallas, TX: ACM.
- Carsten Görg, Zhicheng Liu, and John Stasko, "Reflections on the Evolution of the Jigsaw Visual Analytics System", Information Visualization, Vol. 13, No. 4, Oct. 2014, pp. 336-345.
- Jaegul Choo, Yi Han, Mengdie Hu, Hannah Kim, James Nugent, Francesco Poggi, Haesun Park, John Stasko, "Exploring Anomalies in GAStech", Proceedings of IEEE VAST '14 (VAST Challenge paper), Paris, France, Nov. 2014, pp. 347-348.
- Alex Godwin, Anand Sainath, Sanjay Obla Jayakumar, Vivek Nabhi, Sagar Raut, John Stasko, "Exploring Spatio-Temporal Data as Personal Routes" (Poster), IEEE Information Visualization Conference, Paris, France, Nov. 2014.
- John Stasko, "Value-Driven Evaluation of Visualizations", Proceedings of BELIV 2014, Paris, France, November 2014, pp. 46-53.
- Yafeng Lu, Feng Wang, Ross Maciejewski. "Business Intelligence from Social Media: A Study from the VAST Box Office Challenge." IEEE Computer Graphics and Applications, 34(5): 58-70, 2014
- Yafeng Lu, Robert Kruger, Dennis Thom, Feng Wang, Steffen Koch, Thomas Ertl, Ross Maciejewski. "Integrating Predictive Analytics and Social Media." Proceedings of the IEEE Conference on Visual Analytics Science and Technology, 2014
- Wang, F., Ibarra, J., Adnan, M., Longley, P., Maciejewski, R., “What’s In a Name? Data Linkage, Demography and Visual Analytics,” Eurovis Workshop on Visual Analytics, 2014.
- Lu, Y., Wang, F., Maciejewski, R., “Business Intelligence from Social Media: A Study from the VAST Box-Office Challenge,” IEEE Computer Graphics and Applications, 2014
- Malik, A., Maciejewski, R., Jang, Y., Oliveros, S., Yang, Y., Maule, B., White, M., Ebert, D. S., “A Visual Analytics Process for Maritime Response, Resource Allocation and Risk Assessment,” Information Visualization, 13(2): 93-110, 2014.
- Zhang, Y., Adnan, M., Longley, P., Maciejewski, R., “Exploring Geo-Genealogy Using Internet Surname Search Histories,” Journal of Maps, 9(4):481-485, 2013.
- Kim, S., Maciejewski, R., Malik, A., Jang, Y., Ebert, D. S., Isenberg, T., “Bristle Maps: A Multivariate Abstraction Technique for Geovisualization,” IEEE Transactions on Visualization and Computer Graphics, 19(9): 1438-1454, 2013.
- Lu, Y., Wang, F., Maciejewski, R., “VAST 2013 Mini-Challenge 1: Box Office VAST - Team VADER,” IEEE Conference on Visual Analytics Science and Technology, October, 2013.
- Fan Yang, Xuan Li, Qianmu Li, and Tao Li. "Exploring the diversity in cluster ensemble generation: Random sampling and random projection." Expert Systems with Applications 41, no. 10 (2014): 4844-4866.
- Jingxuan Li, Wei Peng, Tao Li, Tong Sun, Qianmu Li, and Jian Xu. "Social network user influence sense-making and dynamics prediction." Expert Systems with Applications 41, no. 11 (2014): 5115-5124.
- Wubai Zhou, Chao Shen, Tao Li, Shu-Ching Chen, Ning Xie, and Jinpeng Wei "A Bipartite-Graph Based Approach for Disaster Susceptibility Comparisons among Cities," accepted for publication, The 15th IEEE International Conference on Inforamtion Reuse and Integration (IRI 2014), San Francisco, USA, August 13-15, 2014.
- Wubai Zhou, Chao Shen, Tao Li, Shu-Ching Chen, and Ning Xie. "Generating Textual Storyline to Improve Situation Awareness in Disaster Management," accepted for publication, The 15th IEEE International Conference on Inforamtion Reuse and Integration (IRI 2014), San Francisco, USA, August 13-15, 2014.
- Hsin-Yu Ha, Fausto C. Fleites, Shu-Ching Chen, and Min Chen, "Correlation-based Re-ranking for Semantic Concept Detection," accepted for publication, The 15th IEEE International Conference on Information Reuse and Integration (IRI 2014), San Francisco, USA, August 13-15, 2014.
- Hsin-Yu Ha, Fausto C. Fleites, and Shu-Ching Chen, "Building Multi-model Collaboration in Detecting Multimedia Semantic Concepts," 9th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing, October 20-23, 2013, Austin, Texas, USA.
- Hsin-Yu Ha, Fausto C. Fleites, and Shu-Ching Chen, "Content-Based Multimedia Retrieval Using Feature Correlation Clustering and Fusion," International Journal of Multimedia Data Engineering and Management (IJMDEM), Volume 4, No. 2, pp. 46-64, 2013.
- Carsten Görg, Youn-ah Kang, Zhicheng Liu, and John Stasko, "Visual Analytics Support for Intelligence Analysis", IEEE Computer, Vol. 46, No. 7, July 2013, pp. 30-38.
- Carsten Görg, Zhicheng Liu, Jaeyeon Kihm, Jaegul Choo, Haesun Park, John T. Stasko, "Combining Computational Analyses and Interactive Visualization for Document Exploration and Sensemaking in Jigsaw", IEEE Transactions on Visualization and Computer Graphics, Vol. 19, No. 10, October 2013, pp. 1646-1663.
- Zhicheng Liu, Sham Navathe, and John Stasko, "Ploceus: Modeling, Visualizing and Analyzing Tabular Data as Networks", Information Visualization, Vol. 13, No. 1, January 2014, pp. 59-89.
- Youn-ah Kang and John Stasko, "Characterizing the intelligence analysis process through a longitudinal field study: Implications for visual analytics", Information Visualization, Vol. 13, No. 2, April 2014, pp. 134-158.
- Yerramilli, Sudha. "Potential Impact of Climate Changes on the Inundation Risk Levels in a Dam Break Scenario." ISPRS International Journal of Geo-Information 2.1 (2013): 110-134.
- Dodla, Venkata B., and Sudha Yerramilli. "A Geographic Information System Model for Hurricane Track Prediction." American Journal of Geographic Information System 3.2 (2014): 75-87.
- Yerramilli, Sudha., Fonesca, Duber Gomez "Assessing Geographical Inaccessibility to Health Care: Using GIS Network Based Methods." Public Health Research (Accepted, 2014)
- Blanford JI, Bernhardt J, Savelyev A, Wong-Parodi G, Carleton AM, Titley DW, MacEachren AM. (2014) "Tweeting and Tornadoes." In: 11th International ISCRAM Conference. University Park, Pennsylvania
- Karimzadeh, M., Huang, W., Banerjee, S., Wallgrün, J, Hardisty, F., Pezanowski, S., Mitra, P., and MacEachren, A.M. (2013)" GeoTxt: A Web API to Leverage Place References in Text." ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Orlando, FL, November 5-8, 2013.
- McClendon, S., and Robinson, A.C. (2013) "Leveraging Geospatially-Oriented Social Media Communications in Disaster Response." International Journal of Information Systems for Crisis Response and Management. 5(1): 22-40.
- A. Parra, B. Zhao, J. Kim, Joonsoo, E. J. Delp, “Recognition, segmentation andretrieval of gang graffiti images on a mobile device,” Proceedings of the IEEE International Conference on Technologies for Homeland Security, pp. 178 – 183, November 2013, Waltham, MA.
- A. Parra Pozo, B. Zhao, A. Haddad, M. Boutin, E. Delp, “Hazardous Material Sign Detection And Recognition,” Proceedings of the IEEE International Conference on Image Processing, September 2013, Melbourne, Australia.
- B. Zhao, A. Parra, E. Delp, “Mobile-Based Hazmat Sign Detection And Recognition,”Proceedings of the IEEE Global Conference on Signal and Information Processing,December 2013, Austin, TX.
- Wenwen Dou, Li Yu, Xiaoyu Wang, Zhiqiang Ma, and William Ribarsky. "Hierarchical Topics: Visually Exploring Large Text Collections Using Topic Hierarchies." IEEE Transactions on Visualization and Computer Graphics 19(12), pp. 2002-2011 (VAST 2013).
- Jack Guest, Todd Eaglin, KR Subramanian, and William Ribarsky. "Interactive Analysis and Visualization of Situationally Aware Building Evacuations." Information Visualization Journal. doi: 10.1177/1473871613516292.
- William Tolone, Xiaoyu Wang, and William Ribarsky. "Making Sense of the Operational Environment through Interactive, Exploratory Visual Analysis." NATO/OTAN Symposium on Visual Analytics. IST-116/RSY-028.
- William Ribarsky, Xiaoyu Wang, and Wenwen Dou. "Social Media Analytics for Competitive Advantage." Invited paper. Computers & Graphics 38C (2014), pp. 328-331 (Special Issue on EuroVA 2013).
- William Ribarsky, Xiaoyu Wang, Wenwen Dou, and William Tolone. "Towards a Visual Analytics Framework for Handling Complex Business Processes." HICSS 2014. pp. 1374 – 1383. DOI 10.1109/HICSS.2014.177.
- Walton, S., Berger, K., Ebert, D., Chen, M., "Vehicle Object Retargeting from Dynamic Traffic Videos for Real-Time Visualization," The Visual Computer, 2013.
- Owen, G. S., Domik, G., Ebert, D., Kohlhammer, J., Rushmeier, H., Sousa Santos, B., Weiskopf, D., "How Visualization Courses Have Changed over the Past 10 Years," IEEE Computer Graphics and Applications, 2013.
- Chen, V., Razip, A., Ko, S., Qian, C., Ebert, D., "Multi-aspect Visual Analytics on Large-scale High-dimensional Cyber Security Data," Information Visualization, 2013.
- Oliveros-Torres, S., Yang, Y., Jang, Y., Ebert, D., “Visual Analytics for Risk-based Decision Making, Long-Term Planning, and Assessment Process,” Eurovis Workshop on Visual Analytics, 2014.
- Beck, K., Beamon, S., Delp, E., and Ebert, D., "Learning and Law Enforcement: How Community-Based Teaching Facilitates Improved Information Systems," IEEE 47th Hawaii International Conference on System Sciences (HICSS), 2014 (pp. 4966-4969).
- Chae, J., Thom, D., Jang, Y., Kim, S., Ertl, T., Ebert, D., "Visual Analytics of Microblog Data for Public Behavior Analysis in Disaster Events," Eurovis Workshop on Visual Analytics, 2013.
- Ebert, D., Fisher, B., Isenberg, P., “Interaction with Information for Visual Reasoning (Dagstuhl Seminar 13352).,” Dagstuhl Reports 3(8):151-167 (2013).
- Lu, A., Ebert, D., Gao, J., Zhang, S., Joshi, A., "Guest Editorial: Special Issue on Visualization and Visual Analytics," Tsinghua Science and Technology, Vol. 18, No. 2, April 2013 (IEEE Xplore).
- Ebert, D., Fisher, B., Kantor, P., Watters, C., “Introduction to Decision Support and Operational Management Analytics Minitrac,” HICSS 2013:1484, 2013.
Student Opportunities at CVADA:
Curtis McGinity, Operations Research (Graduate Student)
Holly Powell, Systems and Industrial Engineering (Graduate Student)
Jonathan Bullinger, Communication Sciences (Graduate Student)
Jacob Baron, Mathematics (DHS Fellow)
Andrew Dobson, Computer Science (DHS Fellow)
Brad Greening, Mathematics (DHS Fellow)
E2E Project Information
USCG BAM Optimization Tool
USCG Fisheries Enforcement: RIPTIDE
Rutgers, the State University of New Jersey (CCICADA Lead)
University of Illinois at Urbana-Champaign
University of Southern California - Information Sciences Institute
Carnegie Mellon University
Rensselaer Polytechnic Institute
University of Massachusetts-Lowell
City College of New York
Morgan State University
Texas Southern University
Applied Communication Sciences
Regal Decision Systems
Alcatel-Lucent Bell Labs
AT & T Labs- Research
Arizona State University
Florida International University
Georgia Institute of Technology
Morgan State University
Navajo Technical College
Pennsylvania State University
University of California, San Diego
University of Houston, Downtown
University of North Carolina at Charlotte
University of Texas at Austin
University of Washington
Carleton University, Canada
Dalhousie University, Canada
Justice Institute of British Columbia, Canada
Ontario Institute of Technology, Canada
Simon Fraser University, Canada
Swansea University, UK
University of British Columbia, Canada
University of Calgary, Canada
University of Manitoba, Canada
University of Oxford, UK
University of Stuttgart, Germany
University of Victoria, Canada
United States Coast Guard
Domestic Nuclear Defense Office
Customs and Border Protection
Office of Border Patrol
Immigration and Customs Enforcement/Enforcement and Removal Operations
United States Department of Health and Human Services/Office of Refugee Resettlement
Federal Emergency Management Agency
Transportation Security Administration
Federal Bureau of Investigation
National Research Laboratories
Many state, local, and private sector partners as well