Data Integration over NoSQL Stores Using Access Path Based Mappings

Abstract

Abstract. Due to the large amount of data generated by user interactions on the Web, some companies are currently innovating in the domain of data management by designing their own systems. Many of them are referred to as NoSQL databases, standing for 'Not only SQL'. With their wide adoption will emerge new needs and data integration will certainly be one of them. In this paper, we adapt a framework encountered for the integration of relational data to a broader context where both NoSQL and relational databases can be integrated. One important extension consists in the efficient answering of queries expressed over these data sources. The highly denormalized aspect of NoSQL databases results in varying performance costs for several possible query translations. Thus a data integration targeting NoSQL databases needs to generate an optimized translation for a given query. Our contributions are to propose (i) an access path based mapping solution that takes benefit of the design choices of each data source, (ii) integrate preferences to handle conflicts between sources and (iii) a query language that bridges the gap between the SQL query expressed by the user and the query language of the data sources. We also present a prototype implementation, where the target schema is represented as a set of relations and which enables the integration of two of the most popular NoSQL database models, namely document and a column family stores.

SaaS Level based Middleware Database Integrator Platform

“…This helps to provide combined data from separate databases in a single application. In [6], a framework has been evolved for integrating relational as well as NoSQL databases. The efficiency of the framework is the answering the queries after collecting them from integrated data sources.…”

“…But this technique suffers from extra space utilisation for storing duplicate data, and excess time for data migration. In single query approach, data collection is possible from relational as well as from NoSQL database just using a single query [3] [4] [5] [6]. In the context, the single query approach also suffers from the worst case time complexity for data connectivity, maximised time for data aggregation and maximised time for query evaluation.…”

On The Potential Integration of an Ontology-Based Data Access Approach in NoSQL Stores

“…In [9], we have proposed a mapping solution between a relational schema and a set of NoSQL stores/RDBMS. This mapping language can easily be adapted to the domain of ontologies.…”

“…We now adapt the mapping language of [9] to a target ontology. It corresponds to the GAV (Global As View) approach with sound sources [12] which stores possible access paths for a target element.…”

“…MongoDB) or some procedural code using an API (e.g. CouchDB 9 ). This translation implies two operations: (1) generating several queries using ontology-based inferences and (2) searching an efficient rewriting using mapping assertions and their access paths.…”