Self-adaptive based model for ambiguity resolution of the Linked Data Query for Big data analytics

Nurfadhlina Mohd Sharef, Yasser M. Shafazand, Mohd Zakree Ahmad Nazri, Nor Azura Husin

Research output: Contribution to journalArticle

Abstract

Integration of heterogeneous data sources is a crucial step in big data analytics, although it creates ambiguity issues during mapping between the sources due to the variation in the query terms, data structure and granularity conflicts. However, there are limited researches on effective big data integration to address the ambiguity issue for big data analytics. This paper introduces a self-adaptive model for big data integration by exploiting the data structure during querying in order to mitigate and resolve ambiguities. An assessment of a preliminary work on the Geography and Quran dataset is reported to illustrate the feasibility of the proposed model that motivates future work such as solving complex query.

Original languageEnglish
Pages (from-to)176-182
Number of pages7
JournalInternational Journal of Integrated Engineering
Volume10
Issue number6
Publication statusPublished - 1 Jan 2018
Externally publishedYes

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Data integration
Data structures
Big data

Keywords

  • Ambiguity Resolution
  • Big data
  • Heterogeneous data integration
  • Link Data Query
  • Self-adaptive model

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Materials Science (miscellaneous)
  • Mechanics of Materials
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

Cite this

Self-adaptive based model for ambiguity resolution of the Linked Data Query for Big data analytics. / Sharef, Nurfadhlina Mohd; Shafazand, Yasser M.; Ahmad Nazri, Mohd Zakree; Husin, Nor Azura.

In: International Journal of Integrated Engineering, Vol. 10, No. 6, 01.01.2018, p. 176-182.

Research output: Contribution to journalArticle

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