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Proceedings Paper

Entity resolution using cloud computing
Author(s): Alex James; Gregory Tauer; Adam Czerniejewski; Ryan M Brown; Jesse Hartloff; Jillian Chaves; Moises Sudit
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Paper Abstract

Roles and capabilities of analysts are changing as the volume of data grows. Open-source content is abundant and users are becoming increasingly dependent on automated capabilities to sift and correlate information. Entity resolution is one such capability. It is an algorithm that links entities using an arbitrary number of criteria (e.g., identifiers, attributes) from multiple sources. This paper demonstrates a prototype capability, which identifies enriched attributes of individuals stored across multiple sources. Here, the system first completes its processing on a cloud-computing cluster. Then, in a data explorer role, the analyst evaluates whether automated results are correct and whether attribute enrichment improves knowledge discovery.

Paper Details

Date Published: 15 May 2015
PDF: 9 pages
Proc. SPIE 9499, Next-Generation Analyst III, 94990S (15 May 2015); doi: 10.1117/12.2184178
Show Author Affiliations
Alex James, CUBRC (United States)
Gregory Tauer, CUBRC (United States)
Adam Czerniejewski, CUBRC (United States)
Ryan M Brown, CUBRC (United States)
Jesse Hartloff, CUBRC (United States)
Jillian Chaves, CUBRC (United States)
Moises Sudit, CUBRC (United States)

Published in SPIE Proceedings Vol. 9499:
Next-Generation Analyst III
Barbara D. Broome; Timothy P. Hanratty; David L. Hall; James Llinas, Editor(s)

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