@inproceedings{c8059102f5b7450c8c0c483682e2f1f3,
title = "Entity resolution using cloud computing",
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.",
keywords = "Cloud Computing, Entity Resolution, Hadoop, HBase, Knowledge Discovery, MapReduce, NLP, Ontology, RDF, User Interface",
author = "Alex James and Gregory Tauer and Adam Czerniejewski and Brown, \{Ryan M.\} and Jesse Hartloff and Jillian Chaves and Moises Sudit",
note = "Publisher Copyright: {\textcopyright} 2015 SPIE.; Next-Generation Analyst III ; Conference date: 20-04-2015 Through 21-04-2015",
year = "2015",
doi = "10.1117/12.2184178",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Hanratty, \{Timothy P.\} and James Llinas and Broome, \{Barbara D.\} and Hall, \{David L.\}",
booktitle = "Next-Generation Analyst III",
address = "United States",
}