• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

Computer Engineering & Science ›› 2021, Vol. 43 ›› Issue (12): 2272-2280.

Previous Articles    

Connection query based on improved ant colony genetic algorithm

ZHANG Yi-feng1,TONG Guo-xiang1,LIU Jun2 ,QU Ya-ning2   

  1. (1.School of Optical-Electrical and Computer Engineering,

    University of Shanghai for Science and Technology,Shanghai 200093;

    2.Shandong Hoteam Software Co.,LTD.,Jinan 250000,China)

  • Received:2020-07-23 Revised:2020-10-24 Accepted:2021-12-25 Online:2021-12-25 Published:2021-12-31

Abstract: Connection query optimization technique is very important to improve database performance. This paper proposes an improved connection query algorithm, which combines the Wander Join query algorithm and the ant colony genetic hybrid algorithm to optimize the connection order. After executing the new connection plan, pruning strategy is used to decrease the complexity of sample connection, thus achieving the purpose of reducing storage cost. Theoretical analysis and comparative experiment on TPC-H data set and TPC-DS data set are carried out. Experimental results prove that, under the condition that the sample confidence interval of multi-table connection is greater than or equal to 95%, the connection query algorithm combing the ant colony genetic hybrid algorithm and pruning strategy can reduce the relative error rate by 20% to 70% in comparison to the Wander Join query algorithm.


Key words: data management, database, query optimization, connected graph, mixed algorithm