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Data Streams Join Aggregate Algorithms Based on Compound Sliding Window

机译:数据流基于复合滑动窗口加入聚合算法

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In many applications of data stream, join aggregate queries based on sliding window are a sort of queries that are widely used. All the join aggregate query algorithms in existing research works are designed for immediate continuous queries. In this paper, a join aggregate query method based on compound sliding window for periodically executed continuous queries is presented. This method organizes the basic windows in a compound sliding window into hash tables, according to their join properties, the aggregate values are computed while the join processing, the join results of compound sliding window are not saved, so the memory used by query processing is greatly reduced. An algorithm that computes the N+1th join aggregate value increment by using the Nth one is presented. Theoretical analysis and experiment result both show good time and space complexity of this incremental algorithm.
机译:在许多数据流的应用中,基于滑动窗口加入聚合查询是一种广泛使用的查询。现有研究工作中的所有连接聚合查询算法均设计用于立即连续查询。本文介绍了基于复合滑动窗口的用于周期性执行的连续查询的连接聚合查询方法。此方法将复合滑动窗口中的基本窗口组织到散列表中,根据其连接属性,在连接处理时计算聚合值,未保存复合滑动窗口的连接结果,因此查询处理使用的内存是大大减少了。呈现了一种通过使用第n个计算n + 1连接聚合值递增的算法。理论分析和实验结果既显示出该增量算法的良好时空和空间复杂性。

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