Self-adaptive Based Model for Ambiguity Resolution of The Linked Data Query for Big Data Analytics

  • Nurfadhlina Mohd Sharef Intelligent Computing Research Group Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, MALAYSIA.
  • Yasser M. Shafazand
  • Mohd Zakree Ahmad Nazri
  • Nor Azura Husin

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.

Downloads

Download data is not yet available.
Published
25-11-2018
How to Cite
Mohd Sharef, N., M. Shafazand, Y., Ahmad Nazri, M. Z., & Husin, N. A. (2018). Self-adaptive Based Model for Ambiguity Resolution of The Linked Data Query for Big Data Analytics. International Journal of Integrated Engineering, 10(6). Retrieved from https://publisher.uthm.edu.my/ojs/index.php/ijie/article/view/2774