A New Algorithm for Storing and Migrating Data Modelled by Graphs.

El Mouden, Zakariyaa Ait; Jakimi, Abdeslam

NoSQL databases have moved from theoretical solutions to exceed relational databases limits to a practical and indisputable application for storing and manipulation big data. In term of variety, NoSQL databases store heterogeneous data without being obliged to respect a predefined schema such as the case of relational and object-relational databases. Those solutions, also surpass the traditional databases in storage capacity; we consider MongoDB for example, which is a document-oriented database capable of storing unlimited number of documents with a maximal size of 32TB depending on the machine that runs the database and also the operating system. Also, in term of velocity, many researches compared the execution time of different transactions and proved that NoSQL databases are the perfect solution for real-time applications. This paper presents an algorithm to store data modeled by graphs as NoSQL documents, the purpose of this study is to exploit the high amount of data stored in SQL databases and to make such data usable by recent clustering algorithms and other data science tools. This study links relational data to document datastores by defining an effective algorithm for reading relational data, modelling those data as graphs and storing those data as NoSQL documents.

ALGORITHMS; DATA modeling; BIG data; DATA science; RELATIONAL databases; APPLICATION stores; DOCUMENT clustering; NONRELATIONAL databases

International Journal of Online & Biomedical Engineering, 2020, Vol 16, Issue 11, p137

2626-8493

Academic Journal

10.3991/ijoe.v16i11.15545