Transformation of Schema from Relational Database (RDB) to NoSQL Databases

Alotaibi, Obaid; Pardede, Eric

doi:10.3390/data4040148

Cited by 23 publications

(26 citation statements)

References 16 publications

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“…A large number of authors devote their research papers to data migration methods between different data models [7][8][9][10][11][12][13][14][15]; most of these sources assume not only the physical migration of data between different data stores, but also the data schema re-designing procedure. These methods will be discussed and analyzed in more detail in the following parts.…”

Section: Literature Reviewmentioning

confidence: 99%

Research of Methods to Support Data Migration Between Relational and Document Data Storage Models

Peretiatko

Shirokopetleva

Lesna

2022

ITSSI

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The subject matter of the article is heterogeneous model-inhomogeneous data migration between relational and document-oriented data storage models, existing strategies and methods to support such migrations, the use of relational algebra and set theory in the context of databases in building a new data migration algorithm. The goal of the work is to consider the features and procedure of data migration, explore methods to support data migration between relational and documentary data models, build a mathematical model and algorithm for data migration. The following methods were used: analysis and comparison of existing approaches to data migration, choice of strategy for further use in compiling the migration algorithm, mathematical modeling of the algorithm of heterogeneous model-inhomogeneous data migration, formalization of the data migration algorithm. The following tasks were solved in the article: consideration of the concept and types of data migration, justification for choosing a document-oriented data model as a target for data migration, analysis of existing literature sources on methods and strategies of heterogeneous model data migration from relational to document-oriented data model, highlighting advantages and disadvantages existing methods, choosing an approach to the formation of the data migration algorithm, compiling and describing a mathematical model of data migration using relational algebra and set theory, presentation of the data migration algorithm, which is based on the focus on data queries. The following results were obtained: the possibilities of relational algebra and set theory in the context of data models and queries are used, as well as in model redesign, the strategy of migration of data models is chosen, which provides relational and document-oriented data models, the algorithm of application of this method is described. Conclusions: because of the work, the main methods of migration support for different data storage models are analyzed, with the help of relational algebra, set theory a mathematical model is built, and an algorithm for transforming a relational data model into a document-oriented data model is taken into account. The obtained algorithm is suitable for use in real examples, and is the subject of further research and possible improvements, analysis of efficiency in comparison with other methods.

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Section: Literature Reviewmentioning

confidence: 99%

Research of Methods to Support Data Migration Between Relational and Document Data Storage Models

Peretiatko

Shirokopetleva

Lesna

2022

ITSSI

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“…( the row key of the column family is the primary key in the RDB table and the primary key of the one-side becomes a column on the many-side. [38]. (b) Rule 13, creates a column family for each participating table in the RDB, where each column family contains all fields of the two tables [37].…”

Section: Algorithm 1: Transformation and Migration From An Rdb To A D...mentioning

confidence: 99%

“…Rule 14 (creating one table with a single-column family in the column store database): Merge all fields of the two tables in the RDB to a single-column family [37]. The row key of the super family is the concatenation between the relationship name and the primary key of one column that is frequently used in the query [38].…”

Section: Algorithm 1: Transformation and Migration From An Rdb To A D...mentioning

confidence: 99%

A Comprehensive Spark-Based Layer for Converting Relational Databases to NoSQL

Abdel-Fattah

Mohamed

AbdelGaber

2022

BDCC

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Currently, the continuous massive growth in the size, variety, and velocity of data is defined as big data. Relational databases have a limited ability to work with big data. Consequently, not only structured query language (NoSQL) databases were utilized to handle big data because NoSQL represents data in diverse models and uses a variety of query languages, unlike traditional relational databases. Therefore, using NoSQL has become essential, and many studies have attempted to propose different layers to convert relational databases to NoSQL; however, most of them targeted only one or two models of NoSQL, and evaluated their layers on a single node, not in a distributed environment. This study proposes a Spark-based layer for mapping relational databases to NoSQL models, focusing on the document, column, and key–value databases of NoSQL models. The proposed Spark-based layer comprises of two parts. The first part is concerned with converting relational databases to document, column, and key–value databases, and encompasses two phases: a metadata analyzer of relational databases and Spark-based transformation and migration. The second part focuses on executing a structured query language (SQL) on the NoSQL. The suggested layer was applied and compared with Unity, as it has similar components and features and supports sub-queries and join operations in a single-node environment. The experimental results show that the proposed layer outperformed Unity in terms of the query execution time by a factor of three. In addition, the proposed layer was applied to multi-node clusters using different scenarios, and the results show that the integration between the Spark cluster and NoSQL databases on multi-node clusters provided better performance in reading and writing while increasing the dataset size than using a single node.

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“…Given the multiple schemas, the first step is to convert the given schema into a knowledge graph. Firstly, each table is converted to a node [24], table attributes are added as the properties of node and relation as an edge for the graph and a complete knowledge graph is built. Table attributes are converted to properties of nodes so that it is easier to keep the properties of the original schema intact.…”

Section: Schema To Knowledge Graphmentioning

confidence: 99%

Automated dynamic schema generation using knowledge graph

Singh¹,

Rehman1²,

Darshan³

et al. 2022

IJ-AI

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<span>On the internet where the number of database developers is increasing with the availability of huge data to be stored and queried. Establishing relations between various schemas and helping the developers by filtering, prioritizing, and suggesting relevant schema is a requirement. Recommendation system plays an important role in searching through a large volume of dynamically generated schemas to provide database developers with personalized schemas and services. Although many methods are already available to solve problems using machine learning, they require more time and data to learn. These problems can be solved using knowledge graphs (KG). This paper investigates building knowledge graphs to recommend schemas. </span>

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Transformation of Schema from Relational Database (RDB) to NoSQL Databases

Cited by 23 publications

References 16 publications

Research of Methods to Support Data Migration Between Relational and Document Data Storage Models

Research of Methods to Support Data Migration Between Relational and Document Data Storage Models

A Comprehensive Spark-Based Layer for Converting Relational Databases to NoSQL

Automated dynamic schema generation using knowledge graph

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