Online Adaptive Approximate Stream Processing With Customized Error Control
Online Adaptive Approximate Stream Processing With Customized Error Control
复制标题
具有定制错误控制的在线自适应近似流处理
DOI：
10.1109/access.2019.2899825
复制
发表时间：
2019-01-01
期刊：
影响因子：
3.9
通讯作者：
Chen, Lei
中科院分区：
文献类型：
--
作者：
Wei, Xiaohui;Liu, Yuanyuan;Chen, Lei
在流数据的近似处理中，大多数工作集中在如何近似在线到达数据。然而，逼近的效率需要考虑多个方面。通常，客户提交的请求具有特定的质量要求（例如最大误差）。这就提出了一个关键问题，即需要在线质量控制来满足所需的服务质量。由于连续到达的数据可能不会被完全存储并需要立即处理，这给在线获取知识带来了困难，严重影响了结果的质量。为了解决这些问题，我们提出了一种在线自适应近似处理框架，将数据学习、采样和质量控制巧妙地结合在一起。我们首先设计一个针对流数据的在线数据学习策略。根据实时学习结果，我们提出了一种动态采样策略，根据负载的变化切换不同的采样方法。最后，我们提出了一种双重检查错误控制策略来监视和纠正大错误。各个操作模块通过在线学习和反馈相互关联。对合成数据集和真实数据集的实验表明，所提出的近似框架不仅适用于不同的数据分布，而且还提供了定制的误差控制。
In approximate processing on stream data, most works focus on how to approximate online arrival data. However, the efficiency of approximation needs to consider multiple aspects. Generally, customers submit their requests with specific quality requirements (e.g., maximum error). This raises a critical problem that online quality control is required to meet the desired quality of service. Since the continuous arriving data may not be entirely stored and needs to be processed immediately, it brings the difficulty of acquiring knowledge online which significantly affects the quality of results. To address these problems, we present an online adaptive approximate processing framework with a delicate combination of data learning, sampling, and quality control. We first design an online data learning strategy for stream data. With the real-time learning results, we propose a dynamic sampling strategy that switches to different sampling methods based on the change of the load. Finally, we present a double-check error control strategy to monitor and correct large errors. Each operation module is correlated through online learning and feedback. The experiments with both synthetic and real-world datasets show that the proposed approximate framework is not only applicable to different data distributions but also provides a customized error control.