A Generalized Approach to Optimization of Relational Data Warehouses Using Hybrid Greedy and Genetic Algorithms
Journal
Scientific Annals of Computer Science
Date Issued
2009
Author(s)
Kon Popovska, Margita
Gligoroski, Danilo
Abstract
As far as we know, in the open scientific literature, there is no generalized framework for the optimization of relational data warehouses
which includes view and index selection and vertical view fragmentation. In this paper we are offering such a framework. We propose
a formalized multidimensional model, based on relational schemas,
which provides complete vertical view fragmentation and presents an
approach of the transformation of a fragmented snowflake schema to
a defragmented star schema through the process of denormalization.
We define the generalized system of relational data warehouses optimization by including vertical fragmentation of the implementation
schema (F), indexes (I) and view selection (S) for materialization. We
consider Genetic Algorithm as an optimization method and introduce
the technique of ”recessive bits” for handling the infeasible solutions
that are obtained by a Genetic Algorithm. We also present two novel
hybrid algorithms, i.e. they are combination of Greedy and Genetic
Algorithms.
Finally, we present our experimental results and show improvements of the performance and benefits of the generalized approach
(SFI) and show that our novel algorithms significantly improve the
efficiency of the optimization process for different input parameters.
which includes view and index selection and vertical view fragmentation. In this paper we are offering such a framework. We propose
a formalized multidimensional model, based on relational schemas,
which provides complete vertical view fragmentation and presents an
approach of the transformation of a fragmented snowflake schema to
a defragmented star schema through the process of denormalization.
We define the generalized system of relational data warehouses optimization by including vertical fragmentation of the implementation
schema (F), indexes (I) and view selection (S) for materialization. We
consider Genetic Algorithm as an optimization method and introduce
the technique of ”recessive bits” for handling the infeasible solutions
that are obtained by a Genetic Algorithm. We also present two novel
hybrid algorithms, i.e. they are combination of Greedy and Genetic
Algorithms.
Finally, we present our experimental results and show improvements of the performance and benefits of the generalized approach
(SFI) and show that our novel algorithms significantly improve the
efficiency of the optimization process for different input parameters.
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