A New Algorithm for Storing and Migrating Data Modelled by Graphs

Abstract

<span>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. NoSQL solutions 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.</span>

Data Visualization to Explore the Countries Dataset for Pattern Creation

“…My research has limitation that I did not document the visualization projects because most of them did not in research paper instead of that it published in websites such as kaggle and guthip. El Mouden et al (2020) [22] pointed to the idea of data modeling by graphs, where the input and the output are graph schemas were no approaches were proposed to deal with that before. However, this research attempts to show different visualization methods to explore the countries data in addition to using visualization to explain any relationships between the attributes in the dataset.…”