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Information Visualization


The Pacific Northwest National Laboratory has a long history of information visualization research leading to high-impact tools for its customers. The success of PNNL’s information visualization software, such as IN-SPIRE™ and Starlight™, and publications in top visualization journals and conference proceedings are the results of PNNL researchers’ dedication to helping people make sense of massive amounts of complex data.



IN-SPIRE™ provides tools for exploring textual data, including Boolean and “topical” queries, term gisting, and time/trend analysis tools. This suite of tools allows the user to rapidly discover hidden information relationships by reading only pertinent documents. IN-SPIRE™ has been used to explore technical and patent literature, marketing and business documents, web data, accident and safety reports, newswire feeds and message traffic, and more. It has applications in many areas, including information analysis, strategic planning, and medical research.

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.

Related Items

IN-SPIRE™ benefits from the RAKE/CAST Keyword Extraction and Story Flow research projects.

Facets for Discovery and Exploration in Text Collections

Rose, S; Roberts, I; Cramer, N, "Facets for Discovery and Exploration in Text Collections", IEEE Workshop on Interactive Visual Text Analytics for Decision Making, 2011 IEEE VisWeek.


Faceted classifications of text collections provide a useful means of partitioning documents into related groups, however traditional approaches of faceting text collections rely on comprehensive analysis of the subject area or annotated general attributes. In this paper we show the application of basic principles for facet analysis to the development of computational methods for facet classification of text collections. Integration with a visual analytics system is described with summaries of user experiences.

Describing Story Evolution from Dynamic Information Streams

Rose SJ, RS Butner, WE Cowley, ML Gregory, and J Walker. 2009. Describing Story Evolution from Dynamic Information Streams. In IEEE Symposium on Visual Analytics Science and Technology (IEEE VAST) VAST 2009, Oct. 12-13, 2009, Atlantic City, NJ, pp. 99-106. IEEE , Piscataway, NJ.


Sources of streaming information, such as news syndicates, publish information continuously. Information portals and news aggregators list the latest information from around the world enabling information consumers to easily identify events in the past 24 hours. The volume and velocity of these streams causes information from prior days' to quickly vanish despite its utility in providing an informative context for interpreting new information. Few capabilities exist to support an individual attempting to identify or understand trends and changes from streaming information over time. The burden of retaining prior information and integrating with the new is left to the skills, determination, and discipline of each individual. In this paper we present a visual analytics system for linking essential content from information streams over time into dynamic stories that develop and change over multiple days. We describe particular challenges to the analysis of streaming information and explore visual representations for showing story change and evolution over time.

Visual Analysis of Weblog Content

Gregory ML, DA Payne, D McColgin, NO Cramer, and DV Love. 2006. "Visual Analysis of Weblog Content." In International Conference on Weblogs and Social Media '07. pp. 227-230. Boulder, March 26-28, 2007.


In recent years, one of the advances of the World Wide Web is social media and one of the fastest growing aspects of social media is the blogosphere. Blogs make content creation easy and are highly accessible through web pages and syndication. With their growing influence, a need has arisen to be able to monitor the opinions and insight revealed within their content. This paper describes a technical approach for analyzing the content of blog data using a visual analytic tool, IN-SPIRE, developed by Pacific Northwest National Laboratory. We will describe both how an analyst can explore blog data with IN-SPIRE and how the tool could be modified in the future to handle the specific nuances of analyzing blog data.

Analysis Experiences Using Information Visualization

Hetzler, E. and Turner A. 2004. "Analysis experiences using information visualization." IEEE Computer Graphics and Applications, 24:5, pp. 22-26.


To deliver truly useful tools, researchers must learn how to map between the knowledge domains inherent in information collections and the knowledge domains in users' minds. The true measure of this work is not what the software shows, but what the user is able to understand by using it. This article summarizes lessons learned from an observational study of the application of the In-Spire visually-oriented text exploitation system in an operational analysis environment.

Discovering Knowledge Through Visual Analysis

Thomas JJ, PJ Cowley, OA Kuchar, LT Nowell, JR Thomson, and PC Wong. 2001. "Discovering Knowledge Through Visual Analysis." Journal of Universal Computer Science 7(6):517-529. doi:10.3217/jucs-007-06-0517


This paper describes our vision for the near future in digital content analysis as it relates to the creation, verification, and presentation of knowledge. We focus on how visualization enables humans to make discoveries and gain knowledge. Visualization, in this context, is not just the picture representing the data but also a two-way interaction between humans and their information resources for the purposes of knowledge discovery, verification, and the sharing of knowledge with others. We present visual interaction and analysis examples to demonstrate how one current visualization tool analyzes large, diverse collections of text. This is followed by lessons learned and the presentation of a core concept for a new human information discourse.

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screenshot of Canopy

Canopy is a suite of visual analytic tools designed to support deep investigation of large multimedia collections. Canopy combines the understanding of data represented in multiple formats: video, image, and text and presents that information to users through new visual representations. Users can explore relationships between documents and among subcomponents of documents. Canopy incorporates cutting-edge extraction techniques, state-of-the-art content analysis algorithms and novel interactive information visualizations so that analysts can comprehend and articulate the big picture. Canopy helps to reduce analysts’ workload and ultimately the effort of identifying critical intelligence for decision makers.


  • Aids and expedites triage of multimedia data. For example, Canopy can help with analytic problems such as, “I have data from a large collection of files; help me investigate this collection to determine the most relevant files without my having to watch every movie, view every image, and read all the text.”
  • Bootstraps the analysis process by providing visual clues to potential data relationships and highlights connections, giving the user an understanding of all the data and additional context of its structure. This facilitates discovering previously unknown content and/or unexpected or non-obvious relationships.
  • Provides insight into multimedia content similarities and relationships by discovering and visualizing the relationships in an interactive and dynamic user interface.
  • Provides true multimedia analysis, not just stovepipe analysis of an individual type of data augmented with metadata. For example, a Word document with components such as embedded images and text is evaluated as a cohesive information item, where the association of these various document elements is preserved.

Related Items

Canopy benefits from the Lighthouse and Arc Weld research projects.

Interactive Visual Comparison of Multimedia Data through Type-specific Views

Burtner, R., Bohn, S., & Payne, D. (2013, February). Interactive Visual Comparison of Multimedia Data through Type-specific Views. In IS&T/SPIE Electronic Imaging (pp. 86540M-86540M). International Society for Optics and Photonics.


Analysts who work with collections of multimedia to perform information foraging understand how difficult it is to connect information across diverse sets of mixed media. The wealth of information from blogs, social media, and news sites often can provide actionable intelligence; however, many of the tools used on these sources of content are not capable of multimedia analysis because they only analyze a single media type. As such, analysts are taxed to keep a mental model of the relationships among each of the media types when generating the broader content picture. To address this need, we have developed Canopy, a novel visual analytic tool for analyzing multimedia. Canopy provides insight into the multimedia data relationships by exploiting the linkages found in text, images, and video co-occurring in the same document and across the collection. Canopy connects derived and explicit linkages and relationships through multiple connected visualizations to aid analysts in quickly summarizing, searching, and browsing collected information to explore relationships and align content. In this paper, we will discuss the features and capabilities of the Canopy system and walk through a scenario illustrating how this system might be used in an operational environment.

Scalable Reasoning System (SRS)

SRS Social Media Analysis

The Scalable Reasoning System (SRS) is an analytic framework for developing web-based visualization applications. Using a growing library of both visual and analytic components, custom applications can be created for any domain, from any data source.

SRS incorporates the simplicity and accessibility of web-based solutions with the power of an extensible and adaptable back-end analytics platform. SRS applications have been deployed to:

  • Analyze unstructured text
  • Explore hierarchical taxonomies
  • Support real-time analysis of trends and patterns in streaming social media data
  • Organize and provide visual search and navigation of large document repositories

Related Items

SRS benefits from the RAKE/CAST Keyword Extraction and Story Flow research projects.

The Scalable Reasoning System: Lightweight Visualization for Distributed Analytics

Pike W, J Bruce, B Baddeley, D Best, L Franklin, R May, D Rice, R Riensche, and K Younkin. 2009 The Scalable Reasoning System: Lightweight Visualization for Distributed Analytics. Information Visualization. 8(1): 71-84.


A central challenge in visual analytics is the creation of accessible, widely distributable analysis applications that bring the benefits of visual discovery to as broad a user base as possible. Moreover, to support the role of visualization in the knowledge creation process, it is advantageous to allow users to describe the reasoning strategies they employ while interacting with analytic environments. We introduce an application suite called the scalable reasoning system (SRS), which provides web-based and mobile interfaces for visual analysis. The service-oriented analytic framework that underlies SRS provides a platform for deploying pervasive visual analytic environments across an enterprise. SRS represents a 'lightweight' approach to visual analytics whereby thin client analytic applications can be rapidly deployed in a platform-agnostic fashion. Client applications support multiple coordinated views while giving analysts the ability to record evidence, assumptions, hypotheses and other reasoning artifacts. We describe the capabilities of SRS in the context of a real-world deployment at a regional law enforcement organization.

SRS Lessons Learned Explorer

screenshot of LLEx

At PNNL, lessons learned in critical areas such as safety, management, and security are captured in articles that are shared across the lab on an internal website that once had limited search capabilities. To improve access to this information, our Scalable Reasoning System (SRS) team used its web-based analytics framework to create the Lessons Learned Explorer (LLEx). LLEx implements several SRS widgets that vastly improved the searchability and usability of the Laboratory's lessons learned. For instance, the word cluster widget analyzes unstructured text, partitioning the document collection into clusters using differentiating words detected within it. A faceted browse widget lets users explore different dimensions of structured, categorical data to find relevant articles based on known properties of the data. The treemap widget visually displays a subset of the categories used in the faceted browse widget, allowing users to quickly see the importance of certain articles among all categories. The Story Flow widget identifies prominent themes in data and depicts their change over time.



T.Rex is a visual analytics tool that simplifies the translation and exploration of unknown tabular data sources, adding knowledge through discovery. It is an assessment tool to help rapidly understand a previously unknown data set, quickly identifying patterns of interest in the records, and annotating them to enable future collaboration. T.Rex contains a growing set of deep analytical tools and also supports robust export capabilities so that selected data can be sent to other specialized tools for further analysis.

Related Items

Facets for Discovery and Exploration in Text Collections

Rose, S; Roberts, I; Cramer, N, "Facets for Discovery and Exploration in Text Collections", IEEE Workshop on Interactive Visual Text Analytics for Decision Making, 2011 IEEE VisWeek.


Faceted classifications of text collections provide a useful means of partitioning documents into related groups, however traditional approaches of faceting text collections rely on comprehensive analysis of the subject area or annotated general attributes. In this paper we show the application of basic principles for facet analysis to the development of computational methods for facet classification of text collections. Integration with a visual analytics system is described with summaries of user experiences.

A Multi-Level Middle-Out Cross-Zooming Approach for Large Graph Analytics

Wong PC, PS Mackey, KA Cook, RM Rohrer, HP Foote, and MA Whiting. 2009. A Multi-Level Middle-Out Cross-Zooming Approach for Large Graph Analytics. In IEEE Symposium on Visual Analytics Science and Technology (VAST 2009), ed. J Stasko and JJ van Wijk, pp. 147 - 154. IEEE , Piscataway, NJ.


This paper presents a working graph analytics model that embraces the strengths of the traditional top-down and bottom-up approaches with a resilient crossover concept to exploit the vast middle-ground information overlooked by the two extreme analytical approaches. Our graph analytics model is developed in collaboration with researchers and users, who carefully studied the functional requirements that reflect the critical thinking and interaction pattern of a real-life intelligence analyst. To evaluate the model, we implement a system prototype, known as GreenHornet, which allows our analysts to test the theory in practice, identify the technological and usage-related gaps in the model, and then adapt the new technology in their work space. The paper describes the implementation of GreenHornet and compares its strengths and weaknesses against the other prevailing models and tools.


screenshot of clique

To help analysts detect and assess potentially malicious events in large amounts of streaming computer network traffic, PNNL researchers have developed a new behavioral model-based anomaly detection technique. The Correlation Layers for Information Query and Exploration (CLIQUE) system builds models for the expected behavior for user defined host groups on a network and compares these models to a specified time window which can be real-time streaming or exploratory data to generate early indicators of 'non-normal' network activity.

CLIQUE's visual interface allows analysts to view detailed displays of network activity and spot the machines, buildings, facilities or other sources of traffic behaving anomalously. Users can navigate through their data temporally, viewing time periods as short as a few minutes or as long as several days. CLIQUE models and visualizations are designed to scale to immense data volumes, operating on datasets that are comprised of billions of transactions per day, helping to meet the data-intensive cyber security challenge.

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High-Throughput Real-Time Network Flow Visualization

Best DM, DV Love, WA Pike, and SJ Bohn. 2010. High-Throughput Real-Time Network Flow Visualization. FloCon2010, New Orleans, LA.


This presentation and demonstration will introduce two interactive, high-throughput visual analysis tools, Traffic Circle and CLIQUE, and will discuss the analytic requirements of the U.S. government cyber security capabilities for which the tools were developed and are being deployed. Both tools take a time-based approach to visual analysis, with Traffic Circle displaying raw data and CLIQUE computing real-time behavioral models. Performance benchmarks will also be discussed; the tools are currently capable of ingesting and presenting data volumes on the order of hundreds of millions of flow records at once.

Real-Time Visualization of Network Behaviors for Situational Awareness

Best DM, SJ Bohn, DV Love, AS Wynne, and WA Pike. 2010. Real-Time Visualization of Network Behaviors for Situational Awareness. In Proceedings of the Seventh International Symposium on Visualization for Cyber Security, pp. 79-90. ACM , New York, NY.


Plentiful, complex, and dynamic data make understanding the state of an enterprise network difficult. Although visualization can help analysts understand baseline behaviors in network traffic and identify off-normal events, visual analysis systems often do not scale well to operational data volumes (in the hundreds of millions to billions of transactions per day) nor to analysis of emergent trends in real-time data. We present a system that combines multiple, complementary visualization techniques coupled with in-stream analytics, behavioral modeling of network actors, and a high-throughput processing platform called MeDICi. This system provides situational understanding of real-time network activity to help analysts take proactive response steps. We have developed these techniques using requirements gathered from the government users for which the tools are being developed. By linking multiple visualization tools to a streaming analytic pipeline, and designing each tool to support a particular kind of analysis (from high-level awareness to detailed investigation), analysts can understand the behavior of a network across multiple levels of abstraction.

SATkit - Structured Analytic Techniques


SATkit is a suite of wiki-based tools that enables analysts to use analytical techniques that facilitate critical thinking and reduce bias. The toolkit includes Analysis of Competing Hypotheses (ACH), Key Assumptions Check, and Multi-Attribute Utility Analysis. These tools can be used by individual analysts or collaboratively in groups. SATKit tools are written as Java widgets that store their data on the wiki, which allows them to be embedded on wiki pages.

Analytic Widgets


Analytic widgets are individual, web browser-based visualizations from the IN-SPIRE suite. Each is a gateway into visualizing and interacting with data. Widgets are simplified interfaces to the same data analysts would explore in more complex applications. Although simple in appearance, they provide rich insight into data, and are also useful for gaining an overview of a dataset before digging deeper in a full visual analytics application.


LiveWall is a prototype hardware and software solution for room-to-room video conferencing. Each side of the video conference is presented full screen and overlayed with a shared desktop environment both sides interact with via touch. The effect is as if the people you are meeting with are on the other side of an interactive piece of glass. LiveWall is being developed as a part of the larger Precision Information Environments projects, which seeks to improve interactions between people responding to emergency situations.

For more information, see the Precision Information Environments website.


screenshot of Starlight

The Starlight Information Visualization System™ graphically depicts information, dramatically accelerating and improving human ability to derive meaningful knowledge from increasingly large and complex information resources. It is simultaneously a powerful information analysis tool and a platform for conducting advanced visualization research.

Starlight is explicitly designed to manipulate the types of relationships humans need to understand in order to solve complex, multifaceted, real-world problems. Graphical representations enable the underlying relationships to be visually interpreted. Viewers can interactively move among multiple representations of the same information in order to uncover correlations that may span multiple relationship types.

For more information, see the Starlight web site.

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