Source of Publication
Procedia Computer Science
© 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.
Big data, Schema integration, Semantic Relation ships, Word Net
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Almourad, M. Basel; Hussain, Mohammed; and Bonny, Talal, "An Algorithm for Inferring Big Data Objects Correlation Using Word Net" (2016). All Works. 409.
Indexed in Scopus
Open Access Type
Gold: This publication is openly available in an open access journal/series