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Precomputing Datalog Evaluation Plans in Large-Scale Scenarios

Published online by Cambridge University Press:  20 September 2019

ALESSIO FIORENTINO
Affiliation:
Department of Mathematics and Computer Science, University of Calabria, Rende, Italy (e-mail: [email protected]) - https://www.mat.unical.it
NICOLA LEONE
Affiliation:
Department of Mathematics and Computer Science, University of Calabria, Rende, Italy (e-mail: [email protected]) - https://www.mat.unical.it
MARCO MANNA
Affiliation:
Department of Mathematics and Computer Science, University of Calabria, Rende, Italy (e-mail: [email protected]) - https://www.mat.unical.it
SIMONA PERRI
Affiliation:
Department of Mathematics and Computer Science, University of Calabria, Rende, Italy (e-mail: [email protected]) - https://www.mat.unical.it
JESSICA ZANGARI
Affiliation:
Department of Mathematics and Computer Science, University of Calabria, Rende, Italy (e-mail: [email protected]) - https://www.mat.unical.it

Abstract

With the more and more growing demand for semantic Web services over large databases, an efficient evaluation of Datalog queries is arousing a renewed interest among researchers and industry experts. In this scenario, to reduce memory consumption and possibly optimize execution times, the paper proposes novel techniques to determine an optimal indexing schema for the underlying database together with suitable body-orderings for the Datalog rules. The new approach is compared with the standard execution plans implemented in DLV over widely used ontological benchmarks. The results confirm that the memory usage can be significantly reduced without paying any cost in efficiency.

Type
Original Article
Copyright
© Cambridge University Press 2019 

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