TY - GEN
T1 - CliqueSquare
T2 - 2015 31st IEEE International Conference on Data Engineering, ICDE 2015
AU - Goasdoué, François
AU - Kaoudi, Zoi
AU - Manolescu, Ioana
AU - Quiané-Ruiz, Jorge Arnulfo
AU - Zampetakis, Stamatis
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/5/26
Y1 - 2015/5/26
N2 - As increasing volumes of RDF data are being produced and analyzed, many massively distributed architectures have been proposed for storing and querying this data. These architectures are characterized first, by their RDF partitioning and storage method, and second, by their approach for distributed query optimization, i.e., determining which operations to execute on each node in order to compute the query answers. We present CliqueSquare, a novel optimization approach for evaluating conjunctive RDF queries in a massively parallel environment. We focus on reducing query response time, and thus seek to build flat plans, where the number of joins encountered on a root-to-leaf path in the plan is minimized. We present a family of optimization algorithms, relying on n-ary (star) equality joins to build flat plans, and compare their ability to find the flattest possibles. We have deployed our algorithms in a MapReduce-based RDF platform and demonstrate experimentally the interest of the flat plans built by our best algorithms.
AB - As increasing volumes of RDF data are being produced and analyzed, many massively distributed architectures have been proposed for storing and querying this data. These architectures are characterized first, by their RDF partitioning and storage method, and second, by their approach for distributed query optimization, i.e., determining which operations to execute on each node in order to compute the query answers. We present CliqueSquare, a novel optimization approach for evaluating conjunctive RDF queries in a massively parallel environment. We focus on reducing query response time, and thus seek to build flat plans, where the number of joins encountered on a root-to-leaf path in the plan is minimized. We present a family of optimization algorithms, relying on n-ary (star) equality joins to build flat plans, and compare their ability to find the flattest possibles. We have deployed our algorithms in a MapReduce-based RDF platform and demonstrate experimentally the interest of the flat plans built by our best algorithms.
U2 - 10.1109/ICDE.2015.7113332
DO - 10.1109/ICDE.2015.7113332
M3 - Conference contribution
AN - SCOPUS:84940876019
T3 - Proceedings - International Conference on Data Engineering
SP - 771
EP - 782
BT - 2015 IEEE 31st International Conference on Data Engineering, ICDE 2015
PB - IEEE Computer Society
Y2 - 13 April 2015 through 17 April 2015
ER -