Modeling Strategies for Storing Data in Distributed Heterogeneous NoSQL Databases

Hewasinghage, Moditha; Seghouani, Nacéra Bennacer; Bugiotti, Francesca

doi:10.1007/978-3-030-00847-5_35

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…NoSQL databases are°exible but their data design requires also signi¯cant modeling decisions that impact their performance in integrated architectures. 24 Particularly, these approaches are in°uenced by the fact that Big Data and Big Analytics are considered. In practice, the key issues for building Big Data processing infrastructure are in decisions concerning NoSQL databases.…”

Section: Discussionmentioning

confidence: 99%

Integration of Relational and NoSQL Databases

Pokorný

2019

Vietnam J. Comp. Sci.

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The analysis of relational and NoSQL databases leads to the conclusion that these data processing systems are to some extent complementary. In the current Big Data applications, especially where extensive analyses (so-called Big Analytics) are needed, it turns out that it is nontrivial to design an infrastructure involving data and software of both types. Unfortunately, the complementarity negatively influences integration possibilities of these data stores both at the data model and data processing levels. In terms of performance, it may be beneficial to use a polyglot persistence, a multimodel approach or multilevel modeling, or even to transform the SQL database schema into NoSQL and to perform data migration between the relational and NoSQL databases. Another possibility is to integrate a NoSQL database and relational database with the help of a third data model. The aim of the paper is to show these possibilities and present some new methods of designing such integrated database architectures.

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Section: Discussionmentioning

confidence: 99%

Integration of Relational and NoSQL Databases

Pokorný

2019

Vietnam J. Comp. Sci.

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Data Modeling and NoSQL Databases - A Systematic Mapping Review

et al. 2021

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Modeling is one of the most important steps in developing a database. In traditional databases, the Entity Relationship (ER) and Unified Modeling Language (UML) models are widely used. But how are NoSQL databases being modeled? We performed a systematic mapping review to answer three research questions to identify and analyze the levels of representation, models used, and contexts where the modeling process occurred in the main categories of NoSQL databases. We found 54 primary studies where we identified that conceptual and logical levels received more attention than the physical level of representation. The UML, ER, and new notation based on ER and UML were adapted to model NoSQL databases, in the same way, formats such as JSON, XML, and XMI were used to generate schemas through the three levels of representation. New contexts such as benchmark, evaluations, migration, and schema generation were identified, as well as new features to be considered for modeling NoSQL databases, such as the number of records by entities, CRUD operations, and system requirements (availability, consistency, or scalability). Additionally, a coupling and co-citation analysis was carried out to identify relevant works and researchers.

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Modeling Strategies for Storing Data in Distributed Heterogeneous NoSQL Databases

Cited by 2 publications

References 11 publications

Integration of Relational and NoSQL Databases

Integration of Relational and NoSQL Databases

Data Modeling and NoSQL Databases - A Systematic Mapping Review

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