MDA Process to Extract the Data Model from Document-oriented NoSQL Database

Amal Brahim, Rabah Ferhat, Gilles Zurfluh

Abstract

In recent years, the need to use NoSQL systems to store and exploit big data has been steadily increasing. Most of these systems are characterized by the property "schema less" which means absence of the data model when creating a database. This property brings an undeniable flexibility by allowing the evolution of the model during the exploitation of the base. However, query expression requires a precise knowledge of the data model. In this article, we propose a process to automatically extract the physical model from a document-oriented NoSQL database. To do this, we use the Model Driven Architecture (MDA) that provides a formal framework for automatic model transformation. From a NoSQL database, we propose formal transformation rules with QVT to generate the physical model. An experimentation of the extraction process was performed on the case of a medical application.

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Paper Citation


in Harvard Style

Brahim A., Ferhat R. and Zurfluh G. (2019). MDA Process to Extract the Data Model from Document-oriented NoSQL Database.In Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-372-8, pages 141-148. DOI: 10.5220/0007676201410148


in Bibtex Style

@conference{iceis19,
author={Amal Brahim and Rabah Ferhat and Gilles Zurfluh},
title={MDA Process to Extract the Data Model from Document-oriented NoSQL Database},
booktitle={Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2019},
pages={141-148},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007676201410148},
isbn={978-989-758-372-8},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - MDA Process to Extract the Data Model from Document-oriented NoSQL Database
SN - 978-989-758-372-8
AU - Brahim A.
AU - Ferhat R.
AU - Zurfluh G.
PY - 2019
SP - 141
EP - 148
DO - 10.5220/0007676201410148