Learning queries for relational, semi-structured, and graph databases

Ciucanu, Radu

doi:10.1145/2483574.2483576

Cited by 2 publications

(3 citation statements)

References 45 publications

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“…In general, an MMQ q is a mapping that spans a collection of multi-model data D = {d 1 , … , d k } and maps it to the query result q(D). The evaluation of q is a challenging task and we say it as cross-model query processing [2,12,77] when an MMQ spans multiple data models. Roughly, we can identify two feasible approaches for cross-model query processing: (1) the mediator-wrapper fashion in Polystore systems, and (2) a holistic evaluation in MMDB systems.…”

Section: Cross-model Query Processingmentioning

confidence: 99%

“…Cross-model query processing via schema mapping Cross-model query processing in both polystores and MMDBs may incur expensive data exchange [77,88]. Figure 2 gives a visual representation of the MMQ Query 1, in which data fragments from the property graph, JSON document, and key-value pairs should be assembled into an intact query result with a graph-JSON join and a JSON-KV join.…”

Section: Cross-model Query Processingmentioning

confidence: 99%

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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: Cross-model Query Processingmentioning

confidence: 99%

Section: Cross-model Query Processingmentioning

confidence: 99%

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

Guo

Zhang

et al. 2023

Distrib Parallel Databases

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“…Moreover, the problem of learning schema mappings from simple user interactions has been studied in [33], but only in the relation-to-relational case. Interactive query learning on heterogeneous data models is a fundamental step towards interactive learning of cross-model schema mappings [15]. We believe that designing efficient algorithms for mapping big data instances to any other model that is easier to manipulate by the non-expert user (e.g., a common schema that the user feels confident to query) can bring the most value of the big data to the users.…”

Section: Challenges and Perspectivesmentioning

confidence: 99%

A Paradigm for Learning Queries on Big Data

Bonifati

Ciucanu

Lemay

et al. 2014

Proceedings of the First International Workshop on Bringing the Value of "Big Data" to Users (Data4U 2014)

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International audienceSpecifying a database query using a formal query language is typically a challenging task for non-expert users. In the context of big data, this problem becomes even harder as it requires the users to deal with database instances of big sizes and hence difficult to visualize. Such instances usually lack a schema to help the users specify their queries, or have an incomplete schema as they come from disparate data sources. In this paper, we propose a novel paradigm for interactive learning of queries on big data, without assuming any knowledge of the database schema. The paradigm can be applied to different database models and a class of queries adequate to the database model. In particular, in this paper we present two instantiations that validated the proposed paradigm for learning relational join queries and for learning path queries on graph databases. Finally, we discuss the challenges of employing the paradigm for further data models and for learning cross-model schema mappings

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Learning queries for relational, semi-structured, and graph databases

Cited by 2 publications

References 45 publications

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

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

A Paradigm for Learning Queries on Big Data

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