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Improving Query Processing Performance Using Optimization among CPEL Factors

机译:使用CPEL因子中的优化提高查询处理性能

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Query services in public servers are interesting factor due to its scalability and low cost. The owner of the data needs to check confidentiality and privacy before moving to server. The construction of cloud query services requires confidentiality, privacy, efficiency and low processing cost. In order to improve the efficiency of query processing, the system will have to compromise on computing cost parameter. So finding appropriate balance ratio among CPEL, is an optimization problem. The genetic algorithm can be the best technique to solve optimal balancing among CEPL (confidentiality, privacy,efficiency, and low cost). In this paper we propose a frame work to improve query processing performance with optimal confidentially and privacy. The fast KNN-R algorithm is designed to work with random space perturbation method to process range query and K-nearest neighbor queries. The simulation results show that the performance of fast-KNN-R algorithm is better than KNN-R algorithm.
机译:由于其可扩展性和低成本,公共服务器中的查询服务是有趣的因素。 数据的所有者需要在移动到服务器之前检查机密性和隐私。 云查询服务的构建需要机密性,隐私,效率和低处理成本。 为了提高查询处理的效率,系统将必须在计算成本参数上妥协。 所以在CPEL之间找到适当的平衡比,是一个优化问题。 遗传算法可以是解决CEPL(机密性,隐私,效率和低成本)中最佳平衡的最佳技术。 在本文中,我们提出了一个框架工作,以改善查询处理性能,以最佳的机密和隐私。 FAST KNN-R算法旨在使用随机空间扰动方法来处理范围查询和k最近邻查询。 仿真结果表明,快速KNN-R算法的性能优于KNN-R算法。

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