CliqueSquare in Action: Flat Plans for Massively Parallel RDF Queries

Benjamin Djahandideh 1 François Goasdoué 2, 1 Zoi Kaoudi 3, 1 Ioana Manolescu 4, 1 Jorge-Arnulfo Quiané-Ruiz 5 Stamatis Zampetakis 1, 4, *
* Auteur correspondant
1 OAK - Database optimizations and architectures for complex large data
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
2 SHAMAN - Symbolic and Human-centric view of dAta MANagement
IRISA-D7 - GESTION DES DONNÉES ET DE LA CONNAISSANCE
3 IMIS, Athena Research Center, Greece
4 UP11 - Université Paris-Sud - Paris 11
5 QCRI - Qatar Computing Research Institute
Abstract : RDF is an increasingly popular data model for many practical applications, leading to large volumes of RDF data; efficient RDF data management methods are crucial to allow applications to scale. We propose to demonstrate CliqueSquare, an RDF data management system built on top of a MapReduce-like infrastructure. The main technical novelty of CliqueSquare resides in its logical query optimization algorithm, guaranteed to find a logical plan as flat as possible for a given query, meaning: a plan having the smallest possible number of join operators on top of each other. CliqueSquare's ability to build flat plans allows it to take advantage of a parallel processing framework in order to shorten response times. We demonstrate loading and querying the data, with a particular focus on query optimization, and on the performance benefits of CliqueSquare's flat plans.
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Communication dans un congrès
International Conference on Data Engineering, Apr 2015, Seoul, South Korea
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Contributeur : Stamatis Zampetakis <>
Soumis le : vendredi 23 janvier 2015 - 12:22:18
Dernière modification le : vendredi 17 février 2017 - 16:11:44
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Benjamin Djahandideh, François Goasdoué, Zoi Kaoudi, Ioana Manolescu, Jorge-Arnulfo Quiané-Ruiz, et al.. CliqueSquare in Action: Flat Plans for Massively Parallel RDF Queries. International Conference on Data Engineering, Apr 2015, Seoul, South Korea. <hal-01108710>

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