Paper Info
Title
SAGA: a combination of genetic and simulated annealing algorithms for physical data warehouse design
Abstract
Data partitioning is one of the physical data warehouse design techniques that accelerates OLAP queries and facilitates the warehouse manageability. To partition a relational warehouse, the best way consists in fragmenting dimension tables and then using their fragmentation schemas to partition the fact table. This type of fragmentation may dramatically increase the number of fragments of the fact table and makes their maintenance very costly. However, the search space for selecting an optimal fragmentation schema in the data warehouse context may be exponentially large. In this paper, the horizontal fragmentation selection problem is formalised as an optimisation problem with a maintenance constraint representing the number of fragments that the data warehouse administrator may manage. To deal with this problem, we present, SAGA, a hybrid method combining a genetic and a simulated annealing algorithms. We conduct several experimental studies using the APB-1 release II benchmark in order to validate our proposed algorithms.
Year
DOI
Venue
2006
10.1007/11788911_18
BNCOD
Keywords
Field
DocType
horizontal fragmentation selection problem,data warehouse context,optimal fragmentation schema,fact table,simulated annealing algorithm,fragmentation schema,relational warehouse,data warehouse administrator,data partitioning,warehouse manageability,physical data warehouse design,data warehouse,search space,genetics
Data warehouse,Data mining,Warehouse,Computer science,Genetic algorithm,Distributed computing,Simulated annealing,Fact table,Algorithm,Dimensional modeling,Fragmentation (computing),Online analytical processing,Database
Conference
Volume
ISSN
ISBN
4042
0302-9743
3-540-35969-9
Citations 
PageRank 
References 
9
0.60
5
Authors
3
Name
Order
Citations
PageRank
Ladjel Bellatreche1817117.35
Kamel Boukhalfa213820.77
Hassan Ismail Abdalla3244.18