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Multi-model query languages: taming the variety of big data

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Publicado en:Distributed and Parallel Databases vol. 42, no. 1 (Mar 2024), p. 31
Autor principal: Guo, Qingsong
Otros Autores: Zhang, Chao, Zhang, Shuxun, Lu, Jiaheng
Publicado:
Springer Nature B.V.
Materias:
Data management
Databases
Data models
Datasets
Investigations
Big Data
Resource Description Framework-RDF
Time series
Queries
Extensible Markup Language
Query languages
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Resumen:A critical issue in Big Data management is to address the variety of data–data are produced by disparate sources, presented in various formats, and hence inherently involves multiple data models. Multi-Model DataBases (MMDBs) have emerged as a promising approach for dealing with this task as they are capable of accommodating multi-model data in a single system and querying across them with a unified query language. This article aims to offer a comprehensive survey of a wide range of multi-model query languages of MMDBs. In particular, we first present the SQL-based extensions toward multi-model data, including the standard SQL extensions such as SQL/XML, SQL/JSON, and GQL, and the non-standard SQL extensions such as SQL++ and SPASQL. We then study the manners in which document-based and graph-based query languages can be extended to support multi-model data. We also investigate the query languages that provide native support on multi-model data. Finally, this article provides insights into the open challenges and problems of multi-model query languages.
ISSN:0926-8782
1573-7578
DOI:10.1007/s10619-023-07433-1
Fuente:Advanced Technologies & Aerospace Database