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Adaptive two-level optimization for selection predicates of multiple continuous queries

机译：多个连续查询的选择谓词的自适应两级优化

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摘要

A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. Query processing for such a data stream should also be continuous and rapid, which requires strict time and space constraints. In order to guarantee these constraints, we have proposed a new scheme called an Attribute Selection Construct (ASC) for an attribute of a data stream in our previous study (Lee and Lee, Information Sciences 178:2416-2432, 2008). As its optimization technique, this paper proposes the new strategy that determines the evaluation order of multiple ASC's for a given query set at two different levels-macro and micro levels. Based on the two levels, it also proposes two different strategies-macro- sequence and hybrid-sequence-that find the optimized full evaluation sequence of all the ASC's. In addition, it provides the adaptive strategy that periodically rearranges the evaluation sequence of multiple ASC's. The performance of the proposed technique is verified by a series of experiments.
机译：数据流是连续快速生成的大量无界数据元素序列。对此类数据流的查询处理也应该是连续且快速的，这需要严格的时间和空间约束。为了保证这些约束，我们在先前的研究中针对数据流的属性提出了一种称为属性选择构造（ASC）的新方案（Lee和Lee，信息科学178：2416-2432，2008）。作为其优化技术，本文提出了一种新策略，该策略确定给定查询集在两个不同级别（宏级别和微观级别）的多个ASC的评估顺序。基于这两个级别，它还提出了两种不同的策略-宏序列和混合序列-可以找到所有ASC的最佳完整评估序列。另外，它提供了可周期性重新排列多个ASC评估顺序的自适应策略。通过一系列实验验证了所提出技术的性能。

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