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Journal on Data Semantics
Erschienen in: Journal on Data Semantics 1/2018

27.10.2017 | Original Article

MongoDB-Based Modular Ontology Building for Big Data Integration

verfasst von: Hanen Abbes, Faiez Gargouri

Erschienen in: Journal on Data Semantics | Ausgabe 1/2018

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Abstract

Big Data are collections of data sets so large and complex to process using classical database management tools. Their main characteristics are volume, variety and velocity. Although these characteristics accentuate heterogeneity problems, users are always looking for a unified view of the data. Consequently, Big Data integration is a new research area that faces new challenges due to the aforementioned characteristics. Ontologies are widely used in data integration since they represent knowledge as a formal description of a domain of interest. With the advent of Big Data, their implementation faces new challenges due to the volume, variety and velocity dimensions of these data. This paper illustrates an approach to build a modular ontology for Big Data integration that considers the characteristics of big volume, high-speed generation and wide variety of the data. Our approach exploits a NOSQL database, namely MongoDB, and takes advantages of modular ontologies. It follows three main steps: wrapping data sources to MongoDB databases, generating local ontologies and finally composing the local ontologies to get a global one. We equally focus on the implementation of the two last steps.
Nächster Artikel Ontology for Semantic Data Integration in the Domain of IT Benchmarking

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Fußnoten
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Metadaten
Titel
MongoDB-Based Modular Ontology Building for Big Data Integration
verfasst von
Hanen Abbes
Faiez Gargouri
Publikationsdatum
27.10.2017
Verlag
Springer Berlin Heidelberg
Erschienen in
Journal on Data Semantics / Ausgabe 1/2018
Print ISSN: 1861-2032
Elektronische ISSN: 1861-2040
DOI
https://doi.org/10.1007/s13740-017-0081-z