Ontology Based Data Integration Over Document and Column Family Oriented NOSQL

TL;DR

This paper presents an ontology-based data integration framework for NOSQL databases, enabling reasoning and efficient distributed querying across heterogeneous web-scale data sources.

Contribution

It introduces a method to generate local ontologies from schemaless NOSQL sources, create a global ontology through correspondence discovery, and translate SPARQL queries to source-specific languages.

Findings

Generated local ontologies for MongoDB and Cassandra

Created a global ontology linking local schemas

Developed a SPARQL-to-source query translation

Abstract

The World Wide Web infrastructure together with its more than 2 billion users enables to store information at a rate that has never been achieved before. This is mainly due to the will of storing almost all end-user interactions performed on some web applications. In order to reply to scalability and availability constraints, many web companies involved in this process recently started to design their own data management systems. Many of them are referred to as NOSQL databases, standing for 'Not only SQL'. With their wide adoption emerges new needs and data integration is one of them. In this paper, we consider that an ontology-based representation of the information stored in a set of NOSQL sources is highly needed. The main motivation of this approach is the ability to reason on elements of the ontology and to retrieve information in an efficient and distributed manner. Our contributions are the following: (1) we analyze a set of schemaless NOSQL databases to generate local ontologies, (2) we generate a global ontology based on the discovery of correspondences between the local ontologies and finally (3) we propose a query translation solution from SPARQL to query languages of the sources. We are currently implementing our data integration solution on two popular NOSQL databases: MongoDB as a document database and Cassandra as a column family store.