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(searched for: doi:10.22362/ijcert/2016/v3/i9/48900)
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Aniketh Anchalia, Ankit Paudel, R Sanjeetha, Anirudh Kakati
Abstract:
Use of NoSQL to maintain databases has gained popularity in recent times. However, traditional relational databases provide structured data which helps in quick response time to execute certain queries. Currently there exist many algorithms that convert relational to unstructured databases, but vice versa is not available extensively. In this paper we propose an algorithm to convert NoSQL databases to relational databases, i.e., conversion of MongoDB databases to MySQL databases. Five datasets taken from GitHub, namely mobile accessories, bookstore, university database, restaurants and ecommerce store, are used as case studies to show the performance when stored as MongoDB database and as MySQL database. The input is given in JSON format and the output is obtained as MySQL tables. The execution time of the proposed algorithm and the time taken to execute queries are tabulated. It is observed that the execution time of the group-by queries improves by 75.33% when the database is converted to SQL.
Nisreen I. Abo Dabowsa, Abdelsalam M. Maatuk, Salwa M. Elakeili, M. Akhtar Ali
Abstract:
In the Internet of Things (IoT) area, the increase of data leads to the "Big data'' problem. The traditional relational database (RDB) is not being able to deal with processing big data. In contrast, the Not only SQL (NoSQL) database was created to deal with big data problems. Therefore, most organizations need to convert their data stored in RDB systems into NoSQL using flexible models and processes. Most research into the conversion of RDBs into newer database systems has concentrated on schemata transformation, RDB data publishing, whereas other little work focuses on data migration. This paper proposes an automatic approach to convert a database implemented in MySQL database management system into an equivalent database for the target MongoDB system, which is a leading NoSQL database system. This method can handle a large amount of existing data in RDBs without any loss or changes in data semantics and data instances. The solution takes an existing RDB as input, extracts its schema, and then analyzes the schema in an array to be converted with data instances according to the NoSQL target database structure. A system of the proposed approach has been designed. An experimental study was performed to evaluate the proposed approach. The experimental results show that the target database generated by the prototype and the target database generated by other methods were comparable and equivalent.
Aicha Aggoune, Mohamed Sofiane Namoune
Abstract:
Object-relational databases have emerged to improve relational ones by adding properties of object-oriented approach such as references, polymorphism, inheritance, etc. However, these extended relational databases have become a huge amount of data and the database management systems (DBMS) cannot handle them. Due to the emergence of NoSQL databases for ensuring the storage and the processing of large data scale, it is necessary to propose a method for transforming object-relational to NoSQL databases. This paper presents a new method for transforming the object-relational database to one of the popular NoSQL data stores so-called document-oriented database. The proposed method is based on a set of matching between the schemata of object-relational and document-oriented databases. The method is terminated by the generation of a set of JSON files which represent collections of semi-structured documents. These files can be imported and represented by BSON format that will be managed by document-oriented DBMS such as MongoDB.
Published: 3 December 2018
by MDPI
Journal: Symmetry
Abstract:
Handling complexity in the data of information systems has emerged into a serious challenge in recent times. The typical relational databases have limited ability to manage the discrete and heterogenous nature of modern data. Additionally, the complexity of data in relational databases is so high that the efficient retrieval of information has become a bottleneck in traditional information systems. On the side, Big Data has emerged into a decent solution for heterogenous and complex data (structured, semi-structured and unstructured data) by providing architectural support to handle complex data and by providing a tool-kit for efficient analysis of complex data. For the organizations that are sticking to relational databases and are facing the challenge of handling complex data, they need to migrate their data to a Big Data solution to get benefits such as horizontal scalability, real-time interaction, handling high volume data, etc. However, such migration from relational databases to Big Data is in itself a challenge due to the complexity of data. In this paper, we introduce a novel approach that handles complexity of automatic transformation of existing relational database (MySQL) into a Big data solution (Oracle NoSQL). The used approach supports a bi-fold transformation (schema-to-schema and data-to-data) to minimize the complexity of data and to allow improved analysis of data. A software prototype for this transformation is also developed as a proof of concept. The results of the experiments show the correctness of our transformations that outperform the other similar approaches.
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