CloudMdsQL: querying heterogeneous cloud data stores with a common language

Kolev, Boyan and Valduriez, Patrick and Bondiombouy, Carlyna and Jiménez Peris, Ricardo and Pau Fernández, Raquel and Pereira, José (2015). CloudMdsQL: querying heterogeneous cloud data stores with a common language. "Distributed and Parallel Databases", v. 34 (n. 4); pp. 463-503. ISSN 0926-8782. https://doi.org/10.1007/s10619-015-7185-y.

Description

Title: CloudMdsQL: querying heterogeneous cloud data stores with a common language
Author/s:
  • Kolev, Boyan
  • Valduriez, Patrick
  • Bondiombouy, Carlyna
  • Jiménez Peris, Ricardo
  • Pau Fernández, Raquel
  • Pereira, José
Item Type: Article
Título de Revista/Publicación: Distributed and Parallel Databases
Date: 2015
ISSN: 0926-8782
Volume: 34
Subjects:
Freetext Keywords: Cloud; Heterogeneous databases; SQL and NoSQL integration; Multistore query language
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Lenguajes y Sistemas Informáticos e Ingeniería del Software
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

The blooming of different cloud data management infrastructures, specialized for different kinds of data and tasks, has led to a wide diversification of DBMS interfaces and the loss of a common programming paradigm. In this paper, we present the design of a cloud multidatastore query language (CloudMdsQL), and its query engine. CloudMdsQL is a functional SQL-like language, capable of querying multiple heterogeneous data stores (relational and NoSQL) within a single query that may contain embedded invocations to each data store?s native query interface. The query engine has a fully distributed architecture, which provides important opportunities for optimization. The major innovation is that a CloudMdsQL query can exploit the full power of local data stores, by simply allowing some local data store native queries (e.g. a breadth-first search query against a graph database) to be called as functions, and at the same time be optimized, e.g. by pushing down select predicates, using bind join, performing join ordering, or planning intermediate data shipping. Our experimental validation, with three data stores (graph, document and relational) and representative queries, shows that CloudMdsQL satisfies the five important requirements for a cloud multidatastore query language.

Funding Projects

TypeCodeAcronymLeaderTitle
FP7FP7-611068CoherentPaaSUniversidasd Politécnica de MadridA Coherent and Rich PaaS with a Common Programming Model CoherentPaaS
Madrid Regional GovernmentS2013/ICE-2894UnspecifiedUniversidad Politécnica de MadridCloud4BigData: efficient cloud and BigData infrastructure
Government of SpainTIN2013-46883UnspecifiedUniversidad Politécnica de MadridBigDataPaaS: una plataforma como servicio para BigData

More information

Item ID: 40947
DC Identifier: http://oa.upm.es/40947/
OAI Identifier: oai:oa.upm.es:40947
DOI: 10.1007/s10619-015-7185-y
Official URL: http://link.springer.com/article/10.1007/s10619-015-7185-y
Deposited by: Memoria Investigacion
Deposited on: 27 Oct 2016 09:57
Last Modified: 27 Oct 2016 09:57
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