Abstract
Today with the growth of the internet, the use of social networks, mobile telephony, connected and communicating objects. The data has become so big, hence the need to exploit that data has become primordial. In practice, a very large number of companies specializing in the health sector, the banking and financial sector, insurance, manufacturing industry, etc… are based on traditional databases which are often well organized of customer data, machine data, etc ... but in most cases, very large volumes of data from these databases, and the speed with which they must be analyzed to meet the business needs of the company are real challenges. This article aims to respond to a problem of generating NoSQL MongoDB databases by applying an approach based on model-driven engineering (Model Driven Architecture Approach). We provide Model to Model (using the QVT model transformation language), and Model to Code transformations (using the code generator, Acceleo). We also propose vertical and horizontal transformations to demonstrate the validity of our approach on NoSQL MongoDB databases. We have studied in this article the PSM transformations towards the implementation. PIM to PSM transformations are the subject of another work.
Highlights
In this paper we propose the integration of an MDA approach in the context of NoSQL databases in particular the case of MongoDB document-oriented databases
The term NoSQL refers to a type of database management system that goes beyond the relational systems associated with the SQL language by accepting more complex data structures
We present the data models adopted by each category of NoSQL DBMS
Summary
Databases, small or large, have become an essential entity and inseparable from any application or website. With the expansion of the number of Internet users and the multitude of terminals and connected objects, relational databases are no longer able to support large data to store, extract, move and copy, especially if they are distributed over multiple servers. Digital data produced by humans, including video footage, photos and more, attains large volumes per day. These data currently stored in databases that have been designed for them are managed by large database management software, playing the role of intermediaries between the databases on the one hand and the applications and their users on the one hand. The final section concludes this paper, and outlines future work
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