Hybrid greedy and genetic algorithms for optimization of relational data warehouses

Authors:
Goran Velinov;Danilo Gligoroski;Margita Kon-Popovska
Affiliations:
Institute of Informatics, Faculty of Science and Mathematics, Ss. Cyril and Methodius University, Skopje, Macedonia;Centre for Quantifiable Quality of Service in Communication Systems, Norwegian University of Science and Technology, Trondheim, Norway;Institute of Informatics, Faculty of Science and Mathematics, Ss. Cyril and Methodius University, Skopje, Macedonia
Venue:
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
Year:
2007

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Abstract

In this paper we present two novel algorithms for generalized problem of selection of optimal set of views, their optimal vertical fragmentation and their optimal set of indexes. The algorithms are hybrid, i.e. they are combination of Greedy and Genetic Algorithm. We present our experimental results and show that our algorithms significantly improve the efficiency of the optimization process for different input parameters. The results show that those algorithms outperforms Stochastic Ranking evolutionary (Genetic) Algorithm - SRGA by 60% - 280% in the speed of finding optimal (or near optimal) solutions.