R. Scott Butner

Describing story evolution from dynamic information streams

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 present a fundamental visual representation for showing story change and evolution over time.

Low-temperature gasification of high-moisture biomass

Pacific Northwest Laboratory is developing a catalytic reactor system for the thermochemical gasification of high-moisture biomass feedstocks. The high-moisture feedstocks which have been tested include many underutilized materials such as energy crops, agricultural residues, aquatic biomass and food processing wastes. The reactor system was operated at low temperature, 350°C to 450°C, and high pressure, 2000–5000 psig. The catalyst was a reduced nickel metal on a silica-alumina support which was sometimes used in combination with sodium carbonate. This paper describes the results of batch reactor tests 1n which > 95% carbon conversion was achieved in producing a product gas consisting essentially of methane and carbon dioxide.

Knowledge encapsulation framework for technosocial predictive modeling

Analysts who use predictive analytics methods need actionable evidence to support their models and simulations. Commonly, this evidence is distilled from large data sets with significant amount of culling and searching through a variety of sources including traditional and social media. The time/cost effectiveness and quality of the evidence marshaling process can be greatly enhanced by combining component technologies that support directed content harvesting, automated semantic annotation, and content analysis within a collaborative environment, with a functional interface to models and simulations. Existing evidence extraction tools provide some, but not all, the critical components that would empower such an integrated knowledge management environment. This paper describes a novel evidence marshaling solution that significantly advances the state of the art. Its embodiment, the Knowledge Encapsulation Framework (KEF), offers a suite of semi-automated and configurable content harvesting, vetting, annotation and analysis capabilities within a wiki-enabled and user-friendly visual interface that supports collaborative work across distributed teams of analysts. After a summarization of related work, our motivation, and the technical implementation of KEF, we will explore the model for using KEF and results of our research.

Psychosocial and Cultural Modeling in Human Computation Systems: A Gamification Approach

“Gamification”, the application of gameplay to real-world problems, enables the development of human computation systems that support decision-making through the integration of social and machine intelligence. One of gamification’s major benefits includes the creation of a problem solving environment where the influence of cognitive and cultural biases on human judgment can be curtailed through collaborative and competitive reasoning. By reducing biases on human judgment, gamification allows human computation systems to exploit human creativity relatively unhindered by human error. Operationally, gamification uses simulation to harvest human behavioral data that provide valuable insights for the solution of real-world problems.

Knowledge Signatures for Information Integration

Information analysis, scientific discovery, web navigation and many other information related activities rely on the collection, analysis, and integration of unstructured, disparate data. In spite of significant research efforts, automatic methods for information integration are still vastly inferior to human capabilities. Tasks which are simple for humans, such as recognizing the underlying similarities between superficially different objects or sorting out the semantics of ambiguous statements, continue to present significant technical challenges for automated processes.

Technosocial predictive analytics for illicit nuclear trafficking

Illicit nuclear trafficking networks are a national security threat. These networks can directly lead to nuclear proliferation, as state or nonstate actors attempt to identify and acquire nuclear weapons-related expertise, technologies, components, and materials. The ability to characterize and anticipate the key nodes, transit routes, and exchange mechanisms associated with these networks is essential to influence, disrupt, interdict or destroy the function of the networks and their processes. The complexities inherent to the characterization and anticipation of illicit nuclear trafficking networks requires that a variety of modeling and knowledge technologies be jointly harnessed to construct an effective analytical and decision making workflow in which specific case studies can be built in reasonable time and with realistic effort. In this paper, we explore a solution to this challenge that integrates evidentiary and dynamic modeling with knowledge management and analytical gaming, and demonstrate its application to a geopolitical region at risk.

Tethys: The Marine and Hydrokinetic Technology Environmental Impacts Knowledge Management System -- Requirements Specification -- Version 1.0

The marine and hydrokinetic (MHK) environmental impacts knowledge management system (KMS), dubbed Tethys after the mythical Greek goddess of the seas, is being developed for the U.S. Department of Energy (DOE) Office of Energy Efficiency and Renewable Energy Wind and Hydropower Technologies Program (WHTP) by Pacific Northwest National Laboratory (PNNL). This requirements specification establishes the essential capabilities required of Tethys and clarifies for WHTP and the Tethys development team the results that must be achieved by the system.