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Frequent elements on query defined ranges

机译:查询定义范围上的常见元素

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Heavy hitters and flow frequency measurements are fundamental in many networking applications. Further, some functionalities require insights about recent traffic, which is often captured by a sliding window. While existing works assume a single fixed size window, often multiple sizes are of interest and maintaining several instances is wasteful in terms of space and time complexity. Moreover, drill down analytics can also benefit from querying varying intervals. We propose efficient algorithms that support heavy hitters and frequency queries over an interval given at query time. Our methods asymptotically improve the naive approach of running multiple instances of fixed window size solutions. We evaluate our algorithm on a real Internet packet trace, showing that our methods are significantly more efficient than the naive approach and only pay a slight overhead compared to a (single) state-of-the-art fixed window algorithm.
机译:在许多网络应用中,沉重的击球者和流量频率测量是基础。此外,某些功能需要洞悉最近的流量,而这些流量通常是由滑动窗口捕获的。尽管现有作品假定使用单个固定大小的窗口,但通常需要多个大小,而维护多个实例在空间和时间复杂性方面都是浪费的。此外,深入分析还可以从查询不同的间隔中受益。我们提出了高效的算法,可以在查询时间给定的间隔内支持重击手和频率查询。我们的方法渐近地改进了运行固定窗口大小解决方案的多个实例的天真的方法。我们在真实的Internet数据包跟踪上评估了我们的算法,表明我们的方法比朴素的方法有效得多,并且与(单个)最新的固定窗口算法相比仅支付了少量开销。

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