Research On Self-adaptive Query Processing Technology

The research of this paper is based on the adaptability of Query Processing, focusing on the Self-adaptive Query Processing. An improved arithmetic called self-adaptive GA is proposed. The main contributions of this thesis are as follows:First, the current approaches of query processing are summarized. We make an overview on the mediator/wrapper integration systems. Then a new query optimizer is designed based on the analysis the query processes and optimization in the DI, this optimizer can support dynamic query optimization and enhance the performance of systems effectively.Second, an improved method called self-adaptive GA to solve Multi-join query optimization based on GA is proposed in this thesis. According to multi-join query expressions, encode method crossover operator and mutation operator are introduced. In Data Integration system, many data sources have limited query capacity which can be expressed by Bind Pattern. So our method is partitioned into two Phases, in the first phase the search space of GA can be reduced by the bind pattern of data sources and in the second phase, GA exploits the result of phase 1 as heuristics and seeks the optimal query execution plan .This method can be used not only for search space of left-deep but also for hybrid search spaces. The results show that our algorithm is more efficient while there are many data sources integrated.All the details of the self-adaptive GA are described in this paper and promising results are given. We also indicated the future work at the end of this paper.