A Constraint Optimization Method for Large-Scale Distributed View Selection

Imene Mami 1 Zohra Bellahsene 2 Remi Coletta 3
2 FADO - Fuzziness, Alignments, Data & Ontologies
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
3 COCONUT - Agents, Apprentissage, Contraintes
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : View materialization is a commonly used technique in many data-intensive systems to improve the query performance. Increasing need for large-scale data processing has led to investigating the view selection problem in distributed complex scenarios where a set of cooperating computer nodes may share data and issue numerous queries. In our work, the view selection and data placement problem is studied given a limited amount of resources e.g. storage space capacity per computer node and maximum view maintenance cost. We also consider the IO and CPU costs for each computer node as well as the network bandwidth. To address this problem, we have proposed a constraint programming approach which is based on constraint reasoning to tackle problems that aim to satisfy a set of constraints. Then, we have designed a set of efficient heuristics that result in a drastic reduction in the solution space so that the problem becomes solvable for complex scenarios consisting of realistically large numbers of sites, queries and views. Our experimental study shows that our approach performs consistently better compared to a practical approach designed for large-scale distributed environments which uses a genetic algorithm to compute which view has to be materialized at what computer node.
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Journal articles
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-01411295
Contributor : Joël Quinqueton <>
Submitted on : Wednesday, December 7, 2016 - 12:27:41 PM
Last modification on : Friday, March 8, 2019 - 1:20:45 AM

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Imene Mami, Zohra Bellahsene, Remi Coletta. A Constraint Optimization Method for Large-Scale Distributed View Selection. Transactions on Large-Scale Data- and Knowledge-Centered Systems, Springer Berlin / Heidelberg, 2016, LNCS (9620), pp.71-108. ⟨10.1007/978-3-662-49534-6_3⟩. ⟨lirmm-01411295⟩

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