Direkt zum Inhalt

Chillon, Alberto Hernandez ; Klettke, Meike ; Ruiz, Diego Sevilla ; Molina, Jesus Garcia

A Generic Schema Evolution Approach for NoSQL and Relational Databases

Article

Chillon, Alberto Hernandez, Klettke, Meike , Ruiz, Diego Sevilla and Molina, Jesus Garcia (2024) A Generic Schema Evolution Approach for NoSQL and Relational Databases. IEEE Trans. Knowl. Data Eng. 36 (7), pp. 2774-2789.

DOI to cite this document: 10.5283/epub.77266


Abstract

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 ...

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.



Involved Institutions


Details

Item typeArticle
Journal or Publication TitleIEEE Trans. Knowl. Data Eng.
PublisherIEEE Service Center
Open Access TypeCC-License
Volume36
Number of Issue or Book Chapter7
Page Rangepp. 2774-2789
Date5 February 2024
Date of publication13 Aug 2025 05:59
InstitutionsInformatics and Data Science > General computer science > Data Engineering (Prof. Dr.-Ing. Meike Klettke)
Identification Number
ValueType
10.1109/TKDE.2024.3362273DOI
Dewey Decimal Classification000 Computer science, information & general works > 004 Computer science
StatusPublished
RefereedYes, this version has been refereed
Created at the University of RegensburgPartially
URN of the UB Regensburgurn:nbn:de:bvb:355-epub-772666
Item ID77266

Export bibliographical data

Owner only: item control page

nach oben