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SemLinker: automating big data integration for casual users

Alrehamy, Hassan and Walker, Coral 2018. SemLinker: automating big data integration for casual users. Journal of Big Data 5 (1) , 14. 10.1186/s40537-018-0123-x

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Abstract

A data integration approach combines data from different sources and builds a unified view for the users. Big data integration inherently is a complex task, and the existing approaches are either potentially limited or invariably rely on manual inputs and interposition from experts or skilled users. SemLinker, an ontology-based data integration system, is part of a metadata management framework for personal data lake (PDL), a personal store-everything architecture. PDL is for casual and unskilled users, therefore SemLinker adopts an automated data integration workflow to minimize manual input requirements. To support the flat architecture of a lake, SemLinker builds and maintains a schema metadata level without involving any physical transformation of data during integration, preserving the data in their native formats while, at the same time, allowing them to be queried and analyzed. Scalability, heterogeneity, and schema evolution are big data integration challenges that are addressed by SemLinker. Large and real-world datasets of substantial heterogeneities are used in evaluating SemLinker. The results demonstrate and confirm the integration efficiency and robustness of SemLinker, especially regarding its capability in the automatic handling of data heterogeneities and schema evolutions.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
ISSN: 2196-1115
Funders: Cardiff University
Date of First Compliant Deposit: 16 April 2018
Date of Acceptance: 14 March 2018
Last Modified: 08 May 2019 02:41
URI: http://orca-mwe.cf.ac.uk/id/eprint/110732

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