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首页> 外文期刊>Journal of supercomputing >Window-based multiple continuous query algorithm for data streams
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Window-based multiple continuous query algorithm for data streams

机译：基于窗口的数据流多重连续查询算法

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摘要

As more data are being collected and analyzed in real time, stream processing is attracting greater attention. In traditional network, economic and financial analysis, or processing of sensors data in Internet of Things, efficient and timely methods of handling continuous data generation are required. Data are being produced at higher frequency, and the volume of data to be processed within a particular period of time is increasing rapidly. This is especially true for continuous window aggregation, which involves intensive computation. An increase in the number of query windows also generates scalability problems involving aggregate queries. Traditional query handling algorithms can perform many repeated operations. In this study, to enable window-based shared processing of continuous queries, a window reuse algorithm for a multi-query environment based on pace and results is proposed: the multiple continuous query algorithm (MCQA). The aggregation is simplified by gradually shrinking the set of multiple values so that the operation is reduced at each step, eventually achieving result sharing. The algorithm is implemented with the Storm stream processing framework. Experiments prove that the MCQA performance is more efficient and effectively reduces memory usage.
机译：随着越来越多的数据被实时收集和分析，流处理越来越受到关注。在传统的网络，经济和金融分析或物联网中的传感器数据处理中，需要有效且及时的方法来处理连续数据生成。数据的产生频率更高，并且在特定时间段内要处理的数据量正在迅速增加。对于涉及密集计算的连续窗口聚合尤其如此。查询窗口数量的增加还会产生涉及聚合查询的可伸缩性问题。传统的查询处理算法可以执行许多重复的操作。在这项研究中，为了实现基于窗口的连续查询共享处理，提出了一种基于进度和结果的多查询环境的窗口重用算法：多重连续查询算法（MCQA）。通过逐渐缩小多个值的集合来简化聚合，以便在每个步骤中减少操作，最终实现结果共享。该算法是通过Storm流处理框架实现的。实验证明，MCQA性能更有效，并且可以有效减少内存使用。

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