In this work, we study the problem of maintaining basic aggregate statistics over a sliding-window data stream under the constraint of limited memory. As in IoT scenarios the available memory is typically much less than the window size, queries are answered from compact synopses that are maintained in an online fashion. For the efficient construction of such synopses, we propose wavelet-based algorithms that provide deterministic guarantees and produce near exact results for a variety of data distributions. Furthermore, we show how accuracy can be further improved when workload information is known. For this purpose, we propose a workload-aware streaming system that trade-offs accuracy with synopsis’ construction throughput. The conducted experiments indicate that with only a 15%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$15\%$$\end{document} penalty in throughput, the proposed system produces fairly accurate results even for the most adversarial distributions.

中文翻译：

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用于滑动窗口聚合的工作负载感知小波概要

在这项工作中，我们研究了在有限内存的约束下维护滑动窗口数据流的基本聚合统计数据的问题。由于在 IoT 场景中，可用内存通常远小于窗口大小，因此从以在线方式维护的紧凑提要回答查询。为了有效构建此类概要，我们提出了基于小波的算法，该算法提供确定性保证并为各种数据分布产生接近精确的结果。此外，我们展示了当工作负载信息已知时如何进一步提高准确性。为此，我们提出了一种工作负载感知流系统，该系统在准确性与概要的构建吞吐量之间进行权衡。