Automatic NoSQL to Relational Database Transformation with Dynamic Schema Mapping

Recently, the use of NoSQL databases has grown to manage unstructured data for applications to ensure performance and scalability. However, many organizations prefer to transfer data from an operational NoSQL database to a SQL-based relational database for using existing tools for business intellige...

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Bibliographic Details
Published in:Scientific programming 2020, Vol.2020 (2020), p.1-13
Main Authors: Bukhari, Faisal, Almustafa, Khaled Mohamad, Iqbal, Waheed, Aftab, Zain, Abdullah, Muhammad
Format: Article
Language:English
Subjects:
Algorithms
Big Data
Data warehouses
Decision analysis
Decision making
Flexibility
Identification
Information management
Intelligence (information)
Mapping
Online transaction processing
Query languages
Relational data bases
Researchers
Semantic web
Semantics
Transformations (mathematics)
Unstructured data
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Summary:Recently, the use of NoSQL databases has grown to manage unstructured data for applications to ensure performance and scalability. However, many organizations prefer to transfer data from an operational NoSQL database to a SQL-based relational database for using existing tools for business intelligence, analytics, decision making, and reporting. The existing methods of NoSQL to relational database transformation require manual schema mapping, which requires domain expertise and consumes noticeable time. Therefore, an efficient and automatic method is needed to transform an unstructured NoSQL database into a structured database. In this paper, we proposed and evaluated an efficient method to transform a NoSQL database into a relational database automatically. In our experimental evaluation, we used MongoDB as a NoSQL database, and MySQL and PostgreSQL as relational databases to perform transformation tasks for different dataset sizes. We observed excellent performance, compared to the existing state-of-the-art methods, in transforming data from a NoSQL database into a relational database.
ISSN:1058-9244
1875-919X
DOI:10.1155/2020/8813350