IN-SPIRE™

Concept: IN-SPIRE is a powerful collection of visual analysis tools designed to support deep investigation of text documents. The Galaxy visualization (top image) uses the metaphor of stars in the night sky where each star represents a document. Closely related documents cluster together while unrelated documents are further apart. Galaxies help users to understand what is in a document collection and allows them to explore the context of their specific interests.

In ThemeView™ (bottom image), dominant themes are shown in a relief map of natural terrain. Themes are represented by peaks and their height indicates relative strength within the document set. It spatially corresponds to the Galaxy view so the two complementary visualizations can be used together.

Many analytical tools are provided to work in concert with the visualizations, allowing users to investigate the document groupings, query the document contents, investigate time-based trends, and much more.

Status: More information can be found at the IN-SPIRE web site.

Starlight

Concept: The Starlight information model is uniquely flexible and powerful. Starlight uses an advanced, object-oriented approach that is much more conducive to exploratory information analysis than are typical relational data models. Additionally, the Starlight information model is capable of tightly integrating disparate, multiformat (and multimedia) information and data, enabling their concurrent analysis. Further, the model captures interrelationships among database elements and their properties in a way that more effectively mirrors the true nature of most problem domains than do other approaches.

Status: More details about Starlight can be found on the Starlight Website.

Mozart™

Concept: The accessibility of information through the increasing number of interconnected networks has fostered an improved economic climate of competitiveness and technological advantage for the United States, but it has also fostered vulnerabilities. Sensitive or proprietary information, which keeps American business competitive in the global market, has become much more exposed and is jeopardized by ease of access to the information through the Internet.

Mozart is being developed to combat this information security problem and provide continued protection from this new threat. Mozart™ assesses web sites automatically by incorporating Internet site harvesting, web page characterization, link visualization, and complete analysis functionality.

Mozart is a Windows-based software application that automates the assessment of an external, internal, or archived web site. Internet page information is methodically discovered, harvested, and analyzed according to user specified parameters.

Status: More details about Mozart can be found on the Mozart Website.

Assessment Wall

Concept: Visual analytics research often produces powerful but complex interfaces to support information discovery and analysis. How can we simplify this discourse with information, making adoption easier and shortening the time to useful insights and productive analysis? The Assessment Wall is one such effort, designed to help users more easily monitor and explore continually updating text sources such as news or reports, through a high-resolution touch-screen interface. Our goals are to help users find the latest on topics known to be of interest along with emergent and unexpected themes. Additional interactivity allows discovery of relationships and patterns, all in context of the broader streaming data. Usability doesn’t have to come at the expense of power and insight.

Status: The current version of the Assessment Wall is available as part of IN-SPIRE. A flyer is also available.

NVAC Mobile

Concept: Current analytical tools are primarily developed for traditional single-user desktop systems. However, the state of mobile technologies continues to get more powerful and capable with every new generation. At the same time, wireless and cell communications infrastructure is becoming more ubiquitous and reliable. Our motivation is to exploit these technologies and create the next generation of mobile visual analysis techniques and technologies for a wide range of user environments and applications. Examples range from response teams and investigators responding to a disaster to analysts and business professionals on the move. We are working to facilitate true analytical capabilities no matter where the users are.

Status: NVAC Mobile is part of a suite of research projects funded by the National Visualization and Analytics Center.

OmniViz Pro™

Concept: Battelle's OmniViz Pro™ visualization and data mining software has been developed for handling large volumes of information relevant to the scientific and business needs of the life and chemical sciences industries. The methods provide easily interpreted visualization schemes for high-dimensional data, coupled with a suite of interactive tools to enable efficient data mining. The OmniViz Pro™ visualizations are applicable to all data types, allowing integrated views of diverse information including unstructured text, numerical data, categorical information, and genomic sequences. The approaches are designed to allow rapid testing and refinement of hypotheses as well as to increase the likelihood of finding the unexpected.

Status: OmniViz Pro™ is a commercially available product. For more information, visit http://www.omniviz.com

Prospective Analysis

Concept: Prospective Analysis is a method for analysis of alternative future outcomes which draws heavily from Indications and Warning tradecraft, from analysis of competing hypotheses, and from scenario-based risk analysis adapted from the Global Futures Partnership. The prototype generates interactive prospective models, sustains them based on high volumes of evidence with linkage to source data, and supports “what-if” analytic interaction.

Status: Key concepts and interactions from the prototype have been incorporated into the IN-SPIRE software and used in other on-going visualization software projects at PNNL.

ThemeRiver™

Concept: The ThemeRiver™ visualization helps users identify time-related patterns, trends, and relationships across a large collection of documents. The themes in the collection are represented by a "river" that flows left to right through time. The river widens or narrows to depict changes in the collective strength of selected themes in the underlying documents. Individual themes are represented as colored "currents" flowing within the river. The theme currents narrow or widen to indicate changes in individual theme strength at any point in time.

Status: An interactive proof of concept prototype has been developed. Download a QuickTime video about ThemeRiver (20MB)

SURMISE™

Concept: SURMISE is PNNL’s first visualization technique to explore multiple queries or hypotheses within the same collection of documents. Like a detective, SURMISE analyzes a dataset in the context of multiple hypotheses specified by the user. The tool then rapidly evaluates how the data relates to those hypotheses, grouping relevant information and identifying information that is not relevant to support the theory.

Dynamic Analysis

Concept: Dynamic analysis gives users the opportunity to define which images in a collection should be considered similar. The tool reorganizes and displays the images based on the user’s guidance. This approach could be used to analyze image databases including photos, images captured by research or medical instruments, or satellite imagery, as well as applied to text or other kinds of data.

DNA Memory

Concept: Scientific datasets are extremely large, which makes analyzing, computing, and learning from the data extremely time consuming and resource intensive. PNNL has created techniques that reduce the burden of working with generated data, such as climate models, and observed data, such as that gathered by scientific instruments. We capture the essence of the large data set in a representation that is a fraction of the size. These smaller, visual representations of the data can be analyzed and compared with other data sets more quickly and easily.

Our researchers have developed a technique that could generate visual representations of sequential data such as data streams from brainwave or heartbeat monitors. We teamed with biologists interested in comparing and analyzing DNA data from genomes, which would typically fill pages and pages. Instead, we made a picture with the DNA data by assigning different colors to the nucleotides in DNA. By applying image-processing techniques, distinct features begin to emerge and areas for further study can be identified at a glance.