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Title: Web-based visual data exploration for improved radiological source detection

Abstract

Radiation detection can provide a reliable means of detecting radiological material. Such capabilities can help to prevent nuclear and/or radiological attacks, but reliable detection in uncontrolled surroundings requires algorithms that account for environmental background radiation. The Berkeley Data Cloud (BDC) facilitates the development of such methods by providing a framework to capture, store, analyze, and share data sets. In the era of big data, both the size and variety of data make it difficult to explore and find data sets of interest and manage the data. Thus, in the context of big data, visualization is critical for checking data consistency and validity, identifying gaps in data coverage, searching for data relevant to an analyst's use cases, and choosing input parameters for analysis. Downloading the data and exploring it on an analyst's desktop using traditional tools are no longer feasible due to the size of the data. This study describes the design and implementation of a visualization system that addresses the problems associated with data exploration within the context of the BDC. Finally, the visualization system is based on a JavaScript front end communicating via REST with a back end web server.

Authors:
ORCiD logo [1];  [2];  [2];  [2];  [2];  [2];  [2];  [2];  [2];  [2];  [2];  [2];  [2]
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Univ. of California, Davis, CA (United States)
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA), Office of Defense Nuclear Nonproliferation (NA-20); Dept. of Homeland Security (DHS) (United States)
OSTI Identifier:
1461119
Grant/Contract Number:  
AC02-05CH11231; IAA HSHQDC-11-X-00380
Resource Type:
Accepted Manuscript
Journal Name:
Concurrency and Computation. Practice and Experience
Additional Journal Information:
Journal Volume: 29; Journal Issue: 18; Journal ID: ISSN 1532-0626
Publisher:
Wiley
Country of Publication:
United States
Language:
English
Subject:
96 KNOWLEDGE MANAGEMENT AND PRESERVATION; visualization; databases; data storage and indexing; web-based system; data integration

Citation Formats

Weber, Gunther H., Bandstra, Mark S., Chivers, Daniel H., Elgammal, Hamdy H., Hendrix, Valerie, Kua, John, Maltz, Jonathan S., Muriki, Krishna, Ong, Yeongshnn, Song, Kai, Quinlan, Michael J., Ramakrishnan, Lavanya, and Quiter, Brian J. Web-based visual data exploration for improved radiological source detection. United States: N. p., 2017. Web. doi:10.1002/cpe.4203.
Weber, Gunther H., Bandstra, Mark S., Chivers, Daniel H., Elgammal, Hamdy H., Hendrix, Valerie, Kua, John, Maltz, Jonathan S., Muriki, Krishna, Ong, Yeongshnn, Song, Kai, Quinlan, Michael J., Ramakrishnan, Lavanya, & Quiter, Brian J. Web-based visual data exploration for improved radiological source detection. United States. doi:10.1002/cpe.4203.
Weber, Gunther H., Bandstra, Mark S., Chivers, Daniel H., Elgammal, Hamdy H., Hendrix, Valerie, Kua, John, Maltz, Jonathan S., Muriki, Krishna, Ong, Yeongshnn, Song, Kai, Quinlan, Michael J., Ramakrishnan, Lavanya, and Quiter, Brian J. Fri . "Web-based visual data exploration for improved radiological source detection". United States. doi:10.1002/cpe.4203. https://www.osti.gov/servlets/purl/1461119.
@article{osti_1461119,
title = {Web-based visual data exploration for improved radiological source detection},
author = {Weber, Gunther H. and Bandstra, Mark S. and Chivers, Daniel H. and Elgammal, Hamdy H. and Hendrix, Valerie and Kua, John and Maltz, Jonathan S. and Muriki, Krishna and Ong, Yeongshnn and Song, Kai and Quinlan, Michael J. and Ramakrishnan, Lavanya and Quiter, Brian J.},
abstractNote = {Radiation detection can provide a reliable means of detecting radiological material. Such capabilities can help to prevent nuclear and/or radiological attacks, but reliable detection in uncontrolled surroundings requires algorithms that account for environmental background radiation. The Berkeley Data Cloud (BDC) facilitates the development of such methods by providing a framework to capture, store, analyze, and share data sets. In the era of big data, both the size and variety of data make it difficult to explore and find data sets of interest and manage the data. Thus, in the context of big data, visualization is critical for checking data consistency and validity, identifying gaps in data coverage, searching for data relevant to an analyst's use cases, and choosing input parameters for analysis. Downloading the data and exploring it on an analyst's desktop using traditional tools are no longer feasible due to the size of the data. This study describes the design and implementation of a visualization system that addresses the problems associated with data exploration within the context of the BDC. Finally, the visualization system is based on a JavaScript front end communicating via REST with a back end web server.},
doi = {10.1002/cpe.4203},
journal = {Concurrency and Computation. Practice and Experience},
number = 18,
volume = 29,
place = {United States},
year = {2017},
month = {7}
}

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