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中文摘要：
我们在滑动窗口上流技术的地址几负担加入。我们首先包括 aux 窗户和连接, 并集,' 或 ' 操作 ', 汇合指令窗户构造一个双窗户体系结构模型，并且在 aux 窗户上造统计。与统计，我们开发流生产最大的子集 join 产量的策略的有效负担。以便加速流过程的负担，二进制索引的树被利用了在流评估上减少费用。当溪流有高到达率时，我们建议流前面、后面流的合并的一条途径，并且发现在他们之间的一宗最佳的交易。至于可变速度比率的情形，我们开发重新分配中央处理器资源并且动态地缩放窗口的一个计划。另外，我们证明流的那负担没在重新分配的过程期间被影响。合成、真实的数据在我们的实验被使用，并且结果显示出我们的策略的诺言。电子增补材料电子增补材料为在 http://dx.doi.org/10.1007/s11390-007-9024-8 的这篇文章是可得到的并且为授权的用户可存取。
英文摘要：
We address several load shedding techniques over sliding window joins. We first construct a dual window architectural model including aux-windows and join-windows, and build statistics on aux-windows. With the statistics, we develop an effective load shedding strategy producing maximum subset join outputs. In order to accelerate the load shedding process, binary indexed trees have been utilized to reduce the cost on shedding evaluation. When streams have high arrival rates, we propose an approach incorporating front-shedding and rear-shedding, and find an optimal trade-off between them. As for the scenarios of variable speed ratio, we develop a plan reallocating CPU resources and dynamically resizing the windows. In addition, we prove that load shedding is not affected during the process of reallocation. Both synthetic and real data are used in our experiments, and the results show the promise of our strategies.
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