Web‐based visual data exploration for improved radiological source detection. (7th July 2017)

• Record Type: Journal Article
• Authors: 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.
• Is Part Of: Concurrency and computation. Volume 29:Number 18(2017)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-07-07
• Subjects: visualization -- databases -- data storage and indexing -- web‐based system -- data integration; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4203
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: 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 paper describes the design and implementation of a visualization system that addresses the problems associated with data exploration within the context of the BDC. The visualization system is based on a JavaScript front end communicating via REST with a back end web server.