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User Research for Visual Analytics

By: Oriana Love  On: Wed, Jan. 25, 2012  
Visual analytics research is not simply about creating a pretty picture or telling any story. To prove useful, the result of our work must be engaging and approachable, and it must support interactions that help users discover the right genre of story based on their data, tasks and goals. It follows that the visual analytics community must take a user-centric approach by concentrating on the eventual end users to build useful—not just beautiful—visualizations.

GreenLynx

By: Richard May  On: Wed, Jan. 25, 2012  
GreenLynx was developed to quickly assess the connections among a collection of phones based on the forensic data on the phones themselves. Connections between phones can be assessed through phone calls, text messages, and shared contacts. This analysis usually results in a large graph of connections; making sense of these complicated and large graphs can be challenging. GreenLynx focuses on ease of use and speeding up the time from raw phone data to useful decisions. Investigators can filter based on a date range, area code, or a single phone number.

Multimedia Analytics

By: Debbie Payne  On: Thu, Mar. 31, 2011  
Multimedia data has become as common today as email was 20 years ago. Consumer electronic markets have enabled individuals to record and transmit images or video with cell phones. Even the simple point-and-shoot camera can not only add date stamps but also geo-code where the photo was taken. Because of our ability to tweet, text and email from work, home and on our mobile devices, each day we create vast amounts of information about the world around us and consume information from commercial sectors via podcasts, internet streaming broadcasts, radio, cable, and satellite. To some, this may seem like nirvana, but for those who must address certain policy issues or handle complex emerging events, their tasks quickly become overwhelming.

A major challenge in analytics is creating the capability to process, organize, and analyze multi-modal data. In the past few years, we have seen an explosion of content: videos from YouTube™, images from Flickr™, and more news and blog outlets all vying for our attention. The amount of available digital information (more than can be read in lifetime) will only increase an analyst’s load. Compounding the problem is the fact that most analytic tools are unimodal, only able to analyze and represent one type of data, causing analysts to use several tools to understand the content based on its modality. As the number of modalities included in an analysis increases, so do the tools; the analyst begins a juggling act of combining various analytic products from each tool. Unfortunately, this aggregation operation is not managed within a single process or tool but in the analyst’s mind. Today’s analytic environment and computational models have not addressed the need for an integrated analytic environment, especially at the semantic level.

Defining a Framework for Visual Analytics

By: Frank Greitzer  On: Fri, Mar. 04, 2011  
Visual analytics (VA) research seeks to enhance decision making, knowledge discovery, and insight in complex operational environments. The Cognitive Foundations for Visual Analytics project is aimed at researchers, developers, users, and testers of VA products to evaluate the effectiveness of VA technologies and to provide guidance for future research, development, selection, and deployment of VA technologies.

Graph Analytics

By: Pak Chung Wong  On: Fri, Feb. 11, 2011  
Graph analytics is the study and analysis of data that can be transformed into a graph representation consisting of nodes and links. The problem of graph analytics is among the most important research and development (R&D) areas within the visual analytics community. Researchers with diverse backgrounds from information visualization, human-computer interaction, computer graphics, graph drawing, and data mining have pursued graph analytics R&D from scientific, technical, and social approaches. The transdisciplinary nature of the area separates itself from the other graph studies such as 1) graph drawing, which studies optimal topological layout, and 2) graph mining, which champions knowledge discovery though algorithmic computation.
The Northwest Regional Technology Center (NWRTC) is a virtual resource center supporting regional resiliency by enabling homeland security solutions for emergency management and Federal, State, local, and private sector stakeholders in the Northwest. It was created by the Pacific Northwest National Laboratory (PNNL) in 2005 to lead collaborative efforts between technology developers and the user community that will ultimately accelerate the technology and development process.