DocumentCode :
563155
Title :
A muti-join query optimization for the aerospace data
Author :
Wen, Qiao ; Yi, Shengwei ; Tian, Jing ; Li, Qianqian
Author_Institution :
State Key Lab. of Software Dev. Environ., Beihang Univ., Beijing, China
fYear :
2011
fDate :
Nov. 29 2011-Dec. 1 2011
Firstpage :
1
Lastpage :
7
Abstract :
There are massive data in the aerospace field, and the characteristics of the aerospace data evoke the difficulty to query from the data. The multi-join queries can ease the difficulty. A BCOP_GA algorithm based on the Genetic Algorithm for multi-join query of the specific data with a new appropriate cost model for it is proposed. The algorithm takes the advantages of both Bee Colony Algorithm and Simulated Annealing Algorithm to resolve the problems in Genetic Algorithm such as the rapid convergence and the weakness of local search ability etc. It is finally proved to gain global convergence by the theoretical analysis.
Keywords :
aerospace computing; data handling; genetic algorithms; query processing; simulated annealing; BCOP-GA algorithm; aerospace data; bee colony algorithm; cost model; genetic algorithm; multijoin query; mutijoin query optimization; simulated annealing algorithm; Algorithm design and analysis; Convergence; Data models; Genetic algorithms; Optimization; Query processing; Bee Colony Algorithm; Cost Model; Genetic Algorithm; Multi-Join Query Optimization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering and Industries (ICEI), 2011 International Conference on
Conference_Location :
Jeju
Print_ISBN :
978-1-4577-1999-8
Type :
conf
Filename :
6218517
Link To Document :
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