Paper Info
Title
MongoDB-Based Modular Ontology Building for Big Data Integration.
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.
Year
DOI
Venue
2018
10.1007/s13740-017-0081-z
J. Data Semantics
Keywords
Field
DocType
Big Data,Ontology,Data integration,Transformation rules,Ontology merging,NOSQL,MongoDB
Data science,Ontology merging,Data integration,Ontology (information science),Ontology,Ontology-based data integration,Data mining,Computer science,NoSQL,Modular design,Big data,Database
Journal
Volume
Issue
ISSN
7
1
1861-2032
Citations 
PageRank 
References 
0
0.34
18
Authors
2
Name
Order
Citations
PageRank
hanen abbes101.69
Faïez Gargouri224492.29