Entity resolution using cloud computing

Alex James; Gregory Tauer; Adam Czerniejewski; Ryan M Brown; Jesse Hartloff; Jillian Chaves; Moises Sudit

doi:10.1117/12.2184178

15 May 2015 Entity resolution using cloud computing

Alex James, Gregory Tauer, Adam Czerniejewski, Ryan M Brown, Jesse Hartloff, Jillian Chaves, Moises Sudit

Proceedings Volume 9499, Next-Generation Analyst III; 94990S (2015) https://doi.org/10.1117/12.2184178
Event: SPIE Sensing Technology + Applications, 2015, Baltimore, MD, United States

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.

Citation Download Citation

Alex James, Gregory Tauer, Adam Czerniejewski, Ryan M Brown, Jesse Hartloff, Jillian Chaves, and Moises Sudit "Entity resolution using cloud computing", Proc. SPIE 9499, Next-Generation Analyst III, 94990S (15 May 2015); https://doi.org/10.1117/12.2184178

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