Pacific Northwest National Lab

Printer-friendly version

Contact Info

Pacific Northwest National Laboratory (PNNL) is one of the United States Department of Energy National Laboratories, managed by the Department of Energy's Office of Science. The main campus of the laboratory is in Richland, Washington. PNNL scientists conduct basic and applied research and development to strengthen U.S. scientific foundations for fundamental research and innovation; prevent and counter acts of terrorism through applied research in information analysis, cyber security, and the nonproliferation of weapons of mass destruction; increase the U.S. energy capacity and reduce dependence on imported oil; and reduce the effects of human activity on the environment. PNNL has been operated by Battelle Memorial Institute since 1965.

Sponsored Projects:

TW LogoStreaming Data Characterization (SDC)
Co Investigator: Deborah L. McGuinness
Description: This project aims to leverage the novel notion of semantic importance to characterize the importance among the boundless streaming data, so as to provide better query results in terms of accuracy or recall, as well as improve the system response time.
TW LogoStreaming Data Characterization (SDC)
Principal Investigator: Deborah L. McGuinness
Description: This project aims to develop a flexible window management strategies and algorithms for stream reasoning. We have proposed a stack of technologies including sequential stream reasoning architecture, the notion of semantic importance. Project Poster link: Project Slides link:
TW LogoStreaming Hypothesis Reasoning (Shyre)
Principal Investigator: Deborah L. McGuinness
Description: AIM will advance streaming reasoning techniques to overcome a limitation in contemporary inference that performs analysis only over data in a fixed cache or a moving window. This research will lead to methods that continuously shed light on proposed hypotheses as new knowledge arrives from streams of propositions, with a particular emphasis on the effect that removing the expectation of completeness has on the soundness and performance of symbolic deduction platforms.