Improving query processing performance using optimization among CPEL factors

Abstract

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.