In this paper, we consider the problem of tracking multiple quantiles of dynamically varying data stream distributions. The method is based on making incremental updates of the quantile estimates every time a new sample is received. The method is memory and computationally efficient since it only stores one value for each quantile estimate and only performs one operation per quantile estimate when a new sample is received from the data stream. The estimates are realistic in the sense that the monotone property of quantiles is satisfied in every iteration. Experiments show that the method efficiently tracks multiple quantiles and outperforms state-of-the-art methods.

中文翻译：

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跟踪动态变化的数据流中的多个分位数

在本文中，我们考虑了跟踪动态变化的数据流分布的多个分位数的问题。该方法基于每次接收到新样本时对分位数估计进行增量更新。该方法具有存储效率和计算效率，因为当从数据流接收到新样本时，它仅为每个分位数估计存储一个值，并且仅对每个分位数估计执行一个操作。在每次迭代都满足分位数的单调特性的意义上，估计是现实的。实验表明，该方法可以有效地跟踪多个分位数，并且性能优于最新方法。