1. Multi-model query languages: taming the variety of big data.
- Author
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Guo, Qingsong, Zhang, Chao, Zhang, Shuxun, and Lu, Jiaheng
- Subjects
BIG data ,QUERY languages (Computer science) ,DATA management ,SQL ,LANGUAGE & languages ,DATA modeling - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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