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Title: Pattern ANalytics To Support High-performance Exploitation and Reasoning (PANTHER).

Abstract

Abstract not provided.

Authors:
 [1];  [1];  [1]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1177972
Report Number(s):
SAND2015-2557R
579871
DOE Contract Number:
AC04-94AL85000
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English

Citation Formats

Wilson, Andrew T., Czuchlewski, Kristina Rodriguez, and Betty, Rita. Pattern ANalytics To Support High-performance Exploitation and Reasoning (PANTHER).. United States: N. p., 2015. Web. doi:10.2172/1177972.
Wilson, Andrew T., Czuchlewski, Kristina Rodriguez, & Betty, Rita. Pattern ANalytics To Support High-performance Exploitation and Reasoning (PANTHER).. United States. doi:10.2172/1177972.
Wilson, Andrew T., Czuchlewski, Kristina Rodriguez, and Betty, Rita. 2015. "Pattern ANalytics To Support High-performance Exploitation and Reasoning (PANTHER).". United States. doi:10.2172/1177972. https://www.osti.gov/servlets/purl/1177972.
@article{osti_1177972,
title = {Pattern ANalytics To Support High-performance Exploitation and Reasoning (PANTHER).},
author = {Wilson, Andrew T. and Czuchlewski, Kristina Rodriguez and Betty, Rita},
abstractNote = {Abstract not provided.},
doi = {10.2172/1177972},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2015,
month = 4
}

Technical Report:

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  • Sandia has approached the analysis of big datasets with an integrated methodology that uses computer science, image processing, and human factors to exploit critical patterns and relationships in large datasets despite the variety and rapidity of information. The work is part of a three-year LDRD Grand Challenge called PANTHER (Pattern ANalytics To support High-performance Exploitation and Reasoning). To maximize data analysis capability, Sandia pursued scientific advances across three key technical domains: (1) geospatial-temporal feature extraction via image segmentation and classification; (2) geospatial-temporal analysis capabilities tailored to identify and process new signatures more efficiently; and (3) domain- relevant models of humanmore » perception and cognition informing the design of analytic systems. Our integrated results include advances in geographical information systems (GIS) in which we discover activity patterns in noisy, spatial-temporal datasets using geospatial-temporal semantic graphs. We employed computational geometry and machine learning to allow us to extract and predict spatial-temporal patterns and outliers from large aircraft and maritime trajectory datasets. We automatically extracted static and ephemeral features from real, noisy synthetic aperture radar imagery for ingestion into a geospatial-temporal semantic graph. We worked with analysts and investigated analytic workflows to (1) determine how experiential knowledge evolves and is deployed in high-demand, high-throughput visual search workflows, and (2) better understand visual search performance and attention. Through PANTHER, Sandia's fundamental rethinking of key aspects of geospatial data analysis permits the extraction of much richer information from large amounts of data. The project results enable analysts to examine mountains of historical and current data that would otherwise go untouched, while also gaining meaningful, measurable, and defensible insights into overlooked relationships and patterns. The capability is directly relevant to the nation's nonproliferation remote-sensing activities and has broad national security applications for military and intelligence- gathering organizations.« less
  • Query-driven visualization and analytics is a unique approach for high-performance visualization that offers new capabilities for knowledge discovery and hypothesis testing. The new capabilities akin to finding needles in haystacks are the result of combining technologies from the fields of scientific visualization and scientific data management. This approach is crucial for rapid data analysis and visualization in the petascale regime. This article describes how query-driven visualization is applied to a hero-sized network traffic analysis problem.
  • This report summarizes preliminary research into uncertainty quantification for pattern ana- lytics within the context of the Pattern Analytics to Support High-Performance Exploitation and Reasoning (PANTHER) project. The primary focus of PANTHER was to make large quantities of remote sensing data searchable by analysts. The work described in this re- port adds nuance to both the initial data preparation steps and the search process. Search queries are transformed from does the specified pattern exist in the data? to how certain is the system that the returned results match the query? We show example results for both data processing and search,more » and discuss a number of possible improvements for each.« less