首页> 外国专利> VARIABLE-SIZE SAMPLING METHOD FOR SUPPORTING UNIFORMITY CONFIDENCE UNDER DATA-STREAMING ENVIRONMENT

VARIABLE-SIZE SAMPLING METHOD FOR SUPPORTING UNIFORMITY CONFIDENCE UNDER DATA-STREAMING ENVIRONMENT

机译:数据流环境下支持均匀性置信度的可变大小抽样方法

摘要

Disclosed is a variable-size sampling method under a data-streaming environment, including: calculating a maximum window size that satisfies a lower limitation of a predetermined uniformity confidence level at all times; inputting a data stream to be sampled; comparing a data stream length input until a current time point with the maximum window size; inspecting a sample size and a sampling fraction if the maximum window size is larger than the data stream length; performing sampling by generating a slot to increase the sample size if the current sample size is smaller than a predetermined percentage (P %) of the data stream; and directly performing sampling without generating a slot if the current sample size is equal to or larger than the predetermined percentage (P %) of the data stream. As a result, degradation of uniformity confidence during variable-size sampling under a real-time streaming environment can be prevented to improve sampling performance.
机译:公开了一种在数据流环境下的可变大小采样方法,包括:计算最大窗口大小,该最大窗口大小始终满足预定均匀性置信度水平的下限;输入要采样的数据流;将直到当前时间点为止输入的数据流长度与最大窗口大小进行比较;如果最大窗口大小大于数据流长度,则检查样本大小和抽样分数;如果当前样本大小小于数据流的预定百分比(P%),则通过生成时隙来增加样本大小来执行采样;如果当前样本大小等于或大于数据流的预定百分比(P%),则直接进行采样而不产生时隙。结果,可以防止在实时流环境下在可变大小采样期间均匀性置信度的下降,从而提高了采样性能。

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