An Algorithm of Conversion Between Relational Data and Graph Schema

Mouden, Zakariyaa Ait El; Jakimi, Abdeslam; Hajar, Moha

doi:10.1007/978-3-030-03577-8_65

Cited by 6 publications

(3 citation statements)

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“…Many algorithms and approaches were developed to convert between schemas, models or databases. We cite for example the conversion between XML and objectrelational model [12], the conversion between UML and XML [13,14], the conversion between relational data and graph schemas [15] and recently the conversion of SQL databases to NoSQL [16] such as the conversion from MySQL to Cassandra which is a NoSQL column-oriented database [17].…”

Section: Related Workmentioning

confidence: 99%

“…The graph schema construction is generated using the algorithm of conversion between relational data and graph schema published in [15].…”

Section: Graph Schemamentioning

confidence: 99%

“…graph_schema() is the function that allows us to build a graph schema from a relational database. The graph schema is used generally to describe or visualize a set of tuples stored in an SQL database such as MySQL or Oracle db, more details about this function in [15]. The first part of the algorithm (from line 2 to line 7) takes a node as a parameter, then generates its MongoDB syntax from the calculated graph schema in line 1, this part summarizes the instructions from (17) to (27) detailed in the previous subsection.…”

Section: Conversion Algorithmmentioning

confidence: 99%

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A New Algorithm for Storing and Migrating Data Modelled by Graphs

Mouden

Jakimi

2020

Int. J. Onl. Eng.

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<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>

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