CloudMdsQL: querying heterogeneous cloud data stores with a common language

Kolev, Boyan; Valduriez, Patrick; Bondiombouy, Carlyna; Jiménez Peris, Ricardo; Pau Fernández, Raquel y 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.

Descripción

Título: CloudMdsQL: querying heterogeneous cloud data stores with a common language
Autor/es:
  • Kolev, Boyan
  • Valduriez, Patrick
  • Bondiombouy, Carlyna
  • Jiménez Peris, Ricardo
  • Pau Fernández, Raquel
  • Pereira, José
Tipo de Documento: Artículo
Título de Revista/Publicación: Distributed and Parallel Databases
Fecha: 2015
Volumen: 34
Materias:
Palabras Clave Informales: Cloud; Heterogeneous databases; SQL and NoSQL integration; Multistore query language
Escuela: E.T.S. de Ingenieros Informáticos (UPM)
Departamento: Lenguajes y Sistemas Informáticos e Ingeniería del Software
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

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.

Proyectos asociados

TipoCódigoAcrónimoResponsableTítulo
FP7FP7-611068CoherentPaaSUniversidasd Politécnica de MadridA Coherent and Rich PaaS with a Common Programming Model CoherentPaaS
Comunidad de MadridS2013/ICE-2894Sin especificarUniversidad Politécnica de MadridCloud4BigData: efficient cloud and BigData infrastructure
Gobierno de EspañaTIN2013-46883Sin especificarUniversidad Politécnica de MadridBigDataPaaS: una plataforma como servicio para BigData

Más información

ID de Registro: 40947
Identificador DC: http://oa.upm.es/40947/
Identificador OAI: oai:oa.upm.es:40947
Identificador DOI: 10.1007/s10619-015-7185-y
URL Oficial: http://link.springer.com/article/10.1007/s10619-015-7185-y
Depositado por: Memoria Investigacion
Depositado el: 27 Oct 2016 09:57
Ultima Modificación: 27 Oct 2016 09:57
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