网格环境下基于流水线的多重相似查询优化

Pipeline-Based Multi-Query Optimization for Similarity Queries in Grid Environment

提出一种网格环境下基于流水线技术的分布式多重相似查询的优化算法(pipeline-based distributed similarity query processing,简称pGMSQ).首先,当用户提交若干个查询请求时,采用基于代价的动态层次聚类策略(dynamic query clustering,简称DQC)对其进行合并.然后在数据结点层,采用索引支持的向量集缩减方法快速过滤无关向量.最后,在执行结点层对候选向量执行求精操作返回结果向量.由于本查询采用了流水线技术,实验结果表明,该方法在提高查询性能的同时也提高了系统的吞吐量.

This paper proposes a multi-query optimization algorithm for pipeline-based distributed similarity query processing （pGMSQ） in grid environment. First, when a number of query requests are simultaneously submitted by users, a cost-based dynamic query clustering （DQC） is invoked to quickly and effectively identify the correlation among the query spheres （requests）. Then, index-support vector set reduction is performed at data node level in parallel. Finally, refinement of the candidate vectors is conducted to get the answer set at the execution node level. By adopting pipeline-based technique, this algorithm is experimentally proved to be efficient and effective in minimizing the response time by decreasing network transfer cost and increasing the throughput.