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首页> 外文期刊>IEEE Transactions on Knowledge and Data Engineering >Optimizing Cost of Continuous Overlapping Queries over Data Streams by Filter Adaption
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Optimizing Cost of Continuous Overlapping Queries over Data Streams by Filter Adaption

机译：通过过滤器自适应优化数据流上连续重叠查询的成本

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摘要

The problem we aim to address is the optimization of cost management for executing multiple continuous queries on data streams, where each query is defined by several filters, each of which monitors certain status of the data stream. Specially, the filter can be shared by different queries and expensive to evaluate. The conventional objective for such a problem is to minimize the overall execution cost to solve all queries, by planning the order of filter evaluation in shared strategy. However, in the streaming scenario, the characteristics of data items may change in process, which can bring some uncertainty to the outcome of individual filter evaluation, and affect the plan of query execution as well as the overall execution cost. In our work, considering the influence of the uncertain variation of data characteristics, we propose a framework to deal with the dynamic adjustment of filter ordering for query execution on data stream, and focus on the issues of cost management. By incrementally monitoring and analyzing the results of filter evaluation, our proposed approach can be effectively adaptive to the varied stream behavior and adjust the optimal ordering of filter evaluation, so as to optimize the execution cost. In order to achieve satisfactory performance and efficiency, we also discuss the trade-off between the adaptivity of our framework and the overhead incurred by filter adaption. The experimental results on synthetic and two real data sets (traffic and multimedia) show that our framework can effectively reduce and balance the overall query execution cost and keep high adaptivity in streaming scenario.
机译：我们旨在解决的问题是对数据流执行多个连续查询的成本管理的优化，其中每个查询由几个过滤器定义，每个过滤器监视数据流的某些状态。特别是，过滤器可以由不同的查询共享，并且评估成本很高。此类问题的常规目标是通过计划共享策略中的过滤器评估顺序，将解决所有查询的总体执行成本降至最低。但是，在流传输方案中，数据项的特征可能会在过程中发生变化，这可能给单个过滤器评估的结果带来一些不确定性，并影响查询执行的计划以及总体执行成本。在我们的工作中，考虑到数据特征不确定性变化的影响，我们提出了一个框架来处理对数据流执行查询的过滤器顺序的动态调整，并着重于成本管理问题。通过逐步监测和分析过滤器评估的结果，我们提出的方法可以有效地适应变化的流行为并调整过滤器评估的最佳顺序，从而优化执行成本。为了获得令人满意的性能和效率，我们还讨论了框架的适应性与过滤器适应性产生的开销之间的折衷。综合和两个真实数据集（交通和多媒体）的实验结果表明，我们的框架可以有效地减少和平衡总体查询执行成本，并在流场景中保持较高的适应性。

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