CoDEvo: Column family database evolution using model transformations

Suárez-Otero, Pablo; Mior, Michael J.; Suárez-Cabal, María José; Tuya, Javier

doi:10.1016/j.jss.2023.111743

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…Cassandra (columnar): Suárez-Otero et al [35] recently published a work addressing the schema evolution in Cassandra, focusing on maintaining consistency between conceptual and logical schemas. To achieve this, they have developed an MDE solution, CoDEvo, using model transformations to implement inter-schema consistency.…”

Section: Approaches For Specific Nosql Storesmentioning

confidence: 99%

A Generic Schema Evolution Approach for NoSQL and Relational Databases

Chillón,

Klettke,

Ruiz

et al. 2024

IEEE Trans. Knowl. Data Eng.

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In the same way as with relational systems, schema evolution is a crucial aspect of NoSQL systems. But providing approaches and tools to support NoSQL schema evolution is more challenging than for relational databases. Not only are most NoSQL systems schemaless, but different data models exist without a standard specification for them. Moreover, recent proposals fail to address some key aspects related to the kinds of relationships between entities, the definition of relationship types, and the support of structural variation. In this article, we present a generic schema evolution approach able to support the most popular NoSQL data models (columnar, document, key-value, and graph) and the relational model. The proposal is based on the Orion language that implements a schema change operation taxonomy defined for the U-Schema unified data model that integrates NoSQL and relational abstractions. The consistency of the taxonomy operations is formally evaluated with Alloy, and the Orion semantics is expressed by translating operations into native code to update data and schema. Several database systems are supported, and the engine built for each of them has been validated by testing each individual SCO and refactoring study cases. A study of relative execution time of operations is also shown.

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Section: Approaches For Specific Nosql Storesmentioning

confidence: 99%

A Generic Schema Evolution Approach for NoSQL and Relational Databases

Chillón,

Klettke,

Ruiz

et al. 2024

IEEE Trans. Knowl. Data Eng.

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A Literature Review on Schema Evolution in Databases

Brahmia,

Grandi,

Oliboni

2024

Comp. Open

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Changing a database schema is a fact of life in information systems, as a response to changes inside the enterprise (e.g., new users’ requirements, correction of errors in the current database schema) or outside it (e.g., new regulations, new partners’ requirements). In the database research field, a well-known technique has been proposed for managing schema changes, called schema evolution. It allows the database to survive schema changes by adapting existing data to conform to the new schema. A lot of research efforts addressed the topic of schema evolution, in both conventional (i.e., relational) and advanced (e.g., XML, stream, NoSQL) databases, providing a plethora of heterogeneous approaches and solutions making up a quite large literature. Since there is no research work that extensively deals with different proposals and compares them, the purpose of this paper is to fill this gap by reviewing the available schema evolution literature. For that, first we collected and summarized the contributions of research papers dealing with database schema evolution. Then we organized their presentation in a chronological order, also giving a historical perspective on the topic development. Finally, we defined a list of six comparison criteria (database model, implementation, schema change semantics, schema change propagation, integrity constraints, and software evolution) that have helped us to categorize and compare the different database schema evolution proposals. In sum, our paper (i) provides an overview of the state-of-the-art research approaches on database schema evolution, with tables that compare such approaches based on some proposed criteria, (ii) studies the support of schema evolution in commercial DataBase Management Systems (DBMSs), and (iii) points out some possible future research directions in this area.

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CoDEvo: Column family database evolution using model transformations

Cited by 2 publications

References 11 publications

A Generic Schema Evolution Approach for NoSQL and Relational Databases

A Generic Schema Evolution Approach for NoSQL and Relational Databases

A Literature Review on Schema Evolution in Databases

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