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基于蚁群算法的分布式数据库查询优化方法 被引量:2

Research of query optimization based on ant colony algorithm in distributed database
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摘要 在分布式数据库查询优化中,数据传输和多连接次序往往决定了查询执行速度,以通信代价最小为目标的代价模型一直是研究的重点。随着大数据时代的到来,如何提高数据库的查询效率成为我们所要面对的首要问题。为此,利用蚁群算法优化查询计划,以多元连接查询操作为例,进行了模型建立和算法实现。在Oracle数据库中进行了仿真实验,实验结果表明该算法有较好的寻优效果,并对分布式数据库的查询优化具有实际意义。 In the distributed database query optimization, the speed of query depends on the data transfer and join sequence. The price model minimizing communication cost is the emphasis of research. Since the era of big data is coming, the first important problem is how to enhance the speed of database query. Seeking the best query path by using the ant colony algorithm, and taking multiple connection query as an example, model building and algorithm implementation are carried on. The experimental results show that this algorithm has the better effect in selecting path and is practically meaningful for the query optimization of distribute database.
出处 《计算机时代》 2014年第5期47-49,共3页 Computer Era
关键词 分布式数据库 查询优化 多元连接 蚁群算法 distributed database query optimization multiple connection ant colony algorithm
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  • 1VIJAY KUMAR T V. Vikram Singh and Ajay Kumar Ver- ma. Distributed Query Processing Plans Generation Using Genetic Algorithm [ J ]. International Journal of Computer Theory and Engineering,2012,3( 1 ) :38-45. 被引量:1
  • 2ZHOU ZEHAI. Using Heuristics and Genetic Algorithms for Large-scale Database Query Optimization [ J ]. Journal of In- formation and Computing Science ,2010,2 (4) :261-280. 被引量:1
  • 3CHEN POHAN, Seyed Mohsen Shahandashti. Hybrid of Ge- netic Algorithm and Simulated Annealing for Multiple Pro- ject Scheduling with Multiple Resource Constraints[ J]. Au- tomation in Construction. 2012,18 (4) :434-443. 被引量:1
  • 4WEI L Y,ZHAO M. A Niche Hybrid Genetic Algorithm for Global Optimization of Continuous Muhimodal Functions [ J ]. Applied Mathematics and Computation, 2013,160 ( 3 ) : 649-661. 被引量:1
  • 5SITARZ, PIOTR, POWALKA, BARTOSZ. Modal Parameters Estimation Usingant Colony Optimization Algorithnm [ J ]. Mechanical Sysems and Signal Processing, 2016 ( 76/77 ) : 531-554. 被引量:1
  • 6GAO, SHANGCE, WANG, YIRUI, CHENG, JIUJUN. Ant Colony Optimization with Clustering for Solving the Dynamic Location Routing Problem[J]. Applied Mathemamatics and Computation,2016 ( 285 ) : 149-173. 被引量:1
  • 7DADANEH, BEHROUZ ZAMANI, MARKID, HOSSEIN YEGANEH,ZAKEROLHOSSEINI. Ali Unsupervised Proba- bilistic Feature Selection Using ant Colony Optimization [J]. Expert Systens with Application,2016(53) :27-42. 被引量:1
  • 8LIB H, LU M, SHAN Y G. Parallel Ant Colony Optimiza- tion for the Determination of a Point Heat Source Position in a 2-D Domain [ J ]. Applied Thermal Engineering, 2015 (91) :994-1002. 被引量:1
  • 9MI,NAN, HOU, JINGWEI, MI. Wenbao Optimal Spatial Land-use Allocation for Limited Development Ecological Zones Based on the Geographic Information System and a Genetic Ant Colony Algorithm [ J ]. International Journal of Geographical Information Science, 2015, 29 (12): 2174-2193. 被引量:1
  • 10XU BO, MIN. Huaqing Solving Minimum Constraint Remov- al (MCR) Problem Using a Social-force-model-based Ant Colonyalgorithm[J]. Applied Soft Computing,2016, (43) : 553-560. 被引量:1

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