Optimizing Execution Plans in a Multistore

Forresi, Chiara; Francia, Matteo; Gallinucci, Enrico; Golfarelli, Matteo

doi:10.1007/978-3-030-82472-3_11

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…The super-model/meta-model approach can be used efficiently to facilitate schema mapping, but query processing under this approach still results in significant overheads. Recently, several approaches have been proposed to optimize query evaluation of MMQs, such as efficient enumeration of execution plan [176] and query augmentation that supports automatic enrichment of the answer [177,178]…”

Section: Open Challenges and Problemsmentioning

confidence: 99%

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

Guo

Zhang

et al. 2023

Distrib Parallel Databases

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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.

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Section: Open Challenges and Problemsmentioning

confidence: 99%

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

Guo

Zhang

et al. 2023

Distrib Parallel Databases

View full text Add to dashboard Cite

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Cost-based Optimization of Multistore Query Plans

et al. 2022

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Multistores are data management systems that enable query processing across different and heterogeneous databases; besides the distribution of data, complexity factors like schema heterogeneity and data replication must be resolved through integration and data fusion activities. Our multistore solution relies on a dataspace to provide the user with an integrated view of the available data and enables the formulation and execution of GPSJ queries. In this paper, we propose a technique to optimize the execution of GPSJ queries by formulating and evaluating different execution plans on the multistore. In particular, we outline different strategies to carry out joins and data fusion by relying on different schema representations; then, a self-learning black-box cost model is used to estimate execution times and select the most efficient plan. The experiments assess the effectiveness of the cost model in choosing the best execution plan for the given queries and exploit multiple multistore benchmarks to investigate the factors that influence the performance of different plans.

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Optimizing Execution Plans in a Multistore

Cited by 2 publications

References 21 publications

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

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

Cost-based Optimization of Multistore Query Plans

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