多数据流上共享窗口连接查询的降载策略

数据流模型作为一种新型的模型，在许多应用中扮演着重要的角色．基于数据流模型的查询处理技术也得到了广泛的研究．为了提高查询系统的性能，现有的研究成果主要可以划分为两类：调度优化和降低负载方法．调度优化方法通过改变元组执行次序来提高查询性能．降低负载方法在负载超出系统处理能力时，通过减少输入流量来提高吞吐率．然而，同时运用这两种方法来提高查询性能的研究工作还很少．结合共享滑动窗口查询操作的调度优化方法和降低负载方法，提出了两种在burst环境下提高查询吞吐率的策略：均匀降载策略和小窗口准确降载策略．理论分析和实验结果均证明这两种策略能显著提高系统的性能．

Load Shedding for Shared Window Joins over Data Streams

Recently, in many applications, especially in pervasive computing and sensor network environments, data streams play a central role The query processing technology over data streams has been widely studied Recent researches on improving query efficiency can be divided into two groups: scheduling and load shedding Scheduling is aimed at improving query throughput by changing the execution order of the input tuples Load shedding is to increase the throughput of the system by discarding some fraction of the unprocessed data when system is overloaded However, very few studies have been done by making use of these two types of technology simultaneously In this paper, two different algorithms are presented based on the two techniques to improve the system throughput Theoretical analysis and experiment results show that the algorithms are of high performance