We describe a control loop over a sequential online algorithm. The control loop either computes or uses the sequential algorithm to estimate the temporal percentiles of any univariate input data sequence. It transforms an input sequence of numbers into output sequence of histograms. This is performed without storing or sorting all of the observations. The algorithm continuously tests whether the input data is stationary, and reacts to events that do not appear to be stationary. It also indicates how to interpret the histograms since their information content varies according to whether a histogram was computed or estimated. It works with parameter-defined, fixed-size small memory and limited CPU power. It can be used for a statistical reduction of a numerical data stream and, therefore, applied to various Internet of Things applications, edge analytics or fog computing. The algorithm has a built-in feasibility metric called applicability. The applicability metric indicates whether the use of the algorithm is justified for a data source: it works for an arbitrary univariate numerical input, but it is statistically feasible only under some requirements, which are explicitly stated here. We also show the results of a performance study, which was executed on the algorithm with synthetic data.

我们描述了顺序在线算法的控制循环。控制循环计算或使用顺序算法来估计任何单变量输入数据序列的时间百分位数。它将输入的数字序列转换为直方图的输出序列。这是在不存储或排序所有观察结果的情况下执行的。该算法不断测试输入数据是否稳定，并对看似不稳定的事件做出反应。它还指示如何解释直方图，因为直方图的信息内容根据直方图是计算还是估计而变化。它适用于参数定义的、固定大小的小内存和有限的 CPU 能力。它可用于数值数据流的统计缩减，因此适用于各种物联网应用，边缘分析或雾计算。该算法有一个内置的可行性指标，称为适用性。适用性指标表明该算法的使用对于数据源是否合理：它适用于任意单变量数值输入，但仅在某些要求下才在统计上可行，这些要求在此处明确说明。我们还展示了性能研究的结果，该研究是在使用合成数据的算法上执行的。