An Algorithm for Inferring Big Data Objects Correlation Using Word Net | Zendy

Mohamed Basel Almourad | Zendy; Mohammed Hussain | Zendy; Talal Bonny | Zendy

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An Algorithm for Inferring Big Data Objects Correlation Using Word Net

Author(s) -

Mohamed Basel Almourad,

Mohammed Hussain,

Talal Bonny

Publication year - 2016

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2016.04.258

Subject(s) - computer science , schema matching , big data , schema (genetic algorithms) , word (group theory) , variety (cybernetics) , key (lock) , matching (statistics) , data mining , information retrieval , artificial intelligence , natural language processing , data integration , linguistics , philosophy , statistics , computer security , mathematics

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 thesauru

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