Mortadelo: Automatic generation of NoSQL stores from platform-independent data models

Vega, Alfonso de la; García‐Saiz, Diego; Blanco, Carlos; Zorrilla, Marta E.; Sánchez, Pablo

doi:10.1016/j.future.2019.11.032

Cited by 24 publications

(11 citation statements)

References 16 publications

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“…Additionally, two studies focused on a process for various NoSQL technologies, including the column-oriented model. Furthermore, in [37], a model-based process is proposed, which begins with the creation of a conceptual model; later, it is translated into a logical NoSQL model by applying a set of transformation rules. The obtained model is then used to generate a deployment script for the selected NoSQL technology.…”

Section: A Rq-1 What Are Nosql Database Modeling Procedures Based On ...mentioning

confidence: 99%

“…By automatically analyzing the structure of the database and the data using a domain ontology, the problems found are listed, and based on them, it proposes a model with improved security. In [37], the authors develop a It is then translated into a logical NoSQL model through the application of a set of transformation rules, and finally, the resulting model is used to generate a deployment script. In [49], an ontology-driven metamodel is proposed to conceptualize data representations, which provides an abstraction based on semantically enriched formal vocabularies for NoSQL and SQL databases.…”

Section: A Rq-1 What Are Nosql Database Modeling Procedures Based On ...mentioning

confidence: 99%

See 1 more Smart Citation

NoSQL Database Modeling and Management: A Systematic Literature Review

Aguilar Vera,

Naal Jácome,

Díaz Mendoza

et al. 2023

Rev. Fac. Ing.

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The NoSQL databases that emerged this century were created to solve the limitations of relational database systems due to the different types of data that have appeared for information processing. In this paper, we present the results of a secondary study carried out to find and synthesize the research made up to now on modeling processes, characteristics of the used types of data, and management tools for NoSQL Databases. Currently, four types are recognized and classified according to the data model they use: key-value, document-oriented, column-based, and graph-based. With this study, it was possible to identify that the most frequently type of NoSQL database model is that of documents because it offers greater flexibility and versatility compared to the other three models. Although it offers more complex search methods, in terms of data, column and document schemas are the ones that usually describe their characteristics. It was also possible to observe a trend in the use of the column-oriented model and the document-oriented model in the management tools, and, although they all comply with the basic functionalities, the differences lie in the way in which the information is stored and the way they can be accessed.

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Section: A Rq-1 What Are Nosql Database Modeling Procedures Based On ...mentioning

confidence: 99%

Section: A Rq-1 What Are Nosql Database Modeling Procedures Based On ...mentioning

confidence: 99%

NoSQL Database Modeling and Management: A Systematic Literature Review

Aguilar Vera,

Naal Jácome,

Díaz Mendoza

et al. 2023

Rev. Fac. Ing.

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“…Moratadelo [26] proposed model-driven transformation and migration of relational databases to document and column databases. This provided customization mapping.…”

Section: Related Workmentioning

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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“…Representing data relationships as entities and separating entities in the data structure provide this model with full horizontal scalability. In the design of NoSQL database [19], a model-based process, called Mortadelo, was planned to automate database implementation when switching between NoSQL models. A conceptual data model is required for the database to determine the entities and the relationships between them.…”

Section: Review Of the Selected Articlesmentioning

confidence: 99%

“…For instance, agility and flexibility were obtained at the expense of low compatibility of data storage and retrieval with the database in the data model [35]. The scalability was increased with the decrease of ACID properties in NoSQL databases [19]. However, the more coverage of these features, the more capable is the data model when facing the challenge of 3Vs.…”

Section: B Data Model Featuresmentioning

confidence: 99%

A Systematic Review of Data Models for the Big Data Problem

Mostajabi¹,

Safaei

Sahafi

2021

IEEE Access

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Nowadays, data are generated in a continuous streaming manner as the inputs of various applications. The sources of such generated data can be wired or wireless sensor networks commonly used in various fields of geographical, traffic, Internet of Things (IoT), financial tickers, Web2 and Web3, ecommerce, social networks, and online communities. The high volume, high variety, and high velocity of data have recently posed the challenge of 3Vs to this field, also known as the Big Data Problem. The 3Vs dimensions of complexities for the big data entails high-speed storage, scalability of database systems, suitable data models, real-time responsiveness and so on. Data model, as the representation schema of data is an essential issue since many others (e.g., DBMS systems' design, DB languages, etc.) rely on. So, the study of data models is a key and fundamental aspect in structuring, organizing, storing, and manipulating big data. It is also the essence in various areas of cloud migration, web-scale, and so forth. In this paper, we have systematically reviewed different types of data models, the rationale behind them, their applications and support capabilities, and the technologies to switch from one model to another. To address the user needs in various fields, a systematic review method is adopted to classify and present different types and characteristics of data models.

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Mortadelo: Automatic generation of NoSQL stores from platform-independent data models

Cited by 24 publications

References 16 publications

NoSQL Database Modeling and Management: A Systematic Literature Review

NoSQL Database Modeling and Management: A Systematic Literature Review

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

A Systematic Review of Data Models for the Big Data Problem

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