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  1. Modeling geospatial information with semantic graphs enables search for sites of interest based on relationships between features, without requiring strong a priori models of feature shape or other intrinsic properties. Geospatial semantic graphs can be constructed from raw sensor data with suitable preprocessing to obtain a discretized representation. This report describes initial work toward extending geospatial semantic graphs to include temporal information, and initial results applying semantic graph techniques to SAR image data. We describe an efficient graph structure that includes geospatial and temporal information, which is designed to support simultaneous spatial and temporal search queries. We also report amore » preliminary implementation of feature recognition, semantic graph modeling, and graph search based on input SAR data. The report concludes with lessons learned and suggestions for future improvements.« less
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  6. Graph algorithms are a key component in a wide variety of intelligence analysis activities. The Graph-Based Informatics for Non-Proliferation and Counter-Terrorism project addresses the critical need of making these graph algorithms accessible to Sandia analysts in a manner that is both intuitive and effective. Specifically we describe the design and implementation of an open source toolkit for doing graph analysis, informatics, and visualization that provides Sandia with novel analysis capability for non-proliferation and counter-terrorism.
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  10. In the ACS Data Analytics Project (also known as 'YumYum'), a supercomputer is modeled as a graph of components and dependencies, jobs and faults are simulated, and component fault rates are estimated using the graph structure and job pass/fail outcomes. This report documents the successful completion of all SNL deliverables and tasks, describes the software written by SNL for the project, and presents the data it generates. Readers should understand what the software tools are, how they fit together, and how to use them to reproduce the presented data and additional experiments as desired. The SNL YumYum tools provide themore » novel simulation and inference capabilities desired by ACS. SNL also developed and implemented a new algorithm, which provides faster estimates, at finer component granularity, on arbitrary directed acyclic graphs.« less

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"McLendon, William Clarence,"

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