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Article
An Algorithm for Inferring Big Data Objects Correlation Using Word Net
Procedia Computer Science
  • M. Basel Almourad, Zayed University
  • Mohammed Hussain, Zayed University
  • Talal Bonny, University of Sharjah
Document Type
Conference Proceeding
Publication Date
1-1-2016
Abstract

© 2016 The Authors. The value of big data comes from its variety where data is collected from various sources. One of the key big data challenges is identifying which data objects are relevant or refer to the same logical entity across various data sources. This challenge is traditionally known as schema matching. Due to big data velocity traditional approaches to data matching can no longer be used. In this paper we present an approach for inferring data objects correlation. We present our algorithm that relies on the objects meta-data and it consults the Word Net thesaurus.

Publisher
Elsevier
Disciplines
Keywords
  • Big data,
  • Schema integration,
  • Semantic Relation ships,
  • Word Net
Scopus ID

84971247430

Creative Commons License
Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International
Indexed in Scopus
Yes
Open Access
Yes
Open Access Type
Gold: This publication is openly available in an open access journal/series
Citation Information
M. Basel Almourad, Mohammed Hussain and Talal Bonny. "An Algorithm for Inferring Big Data Objects Correlation Using Word Net" Procedia Computer Science Vol. 83 (2016) p. 1238 - 1243 ISSN: <p><a href="https://v2.sherpa.ac.uk/id/publication/issn/1877-0509" target="_blank">1877-0509</a></p>
Available at: http://works.bepress.com/mohamedbasel-almourad/16/