论文标题
$ t $ digests的不对称量表功能
Asymmetric scale functions for $t$-digests
论文作者
论文摘要
$ t $数量是一种数据结构，可以查询大约分位数，其精度接近分布的最小值和最大值。我们开发了一种$ t $数量的变体，具有准确性不对称的中位数，从而在计算资源和准确性之间进行了替代权衡，这对于具有明显偏斜的分布可能特别感兴趣。在为$ t $数字建立了比例功能的一些理论属性之后，我们表明，熟悉的规模函数上的切线构造可以保留允许$ t $ digigests在线操作并合并的关键属性。我们以一项经验研究结论，证明了不对称变体在分布的一侧保持精度，并且记忆足迹较小。
The $t$-digest is a data structure that can be queried for approximate quantiles, with greater accuracy near the minimum and maximum of the distribution. We develop a $t$-digest variant with accuracy asymmetric about the median, thereby making possible alternative tradeoffs between computational resources and accuracy which may be of particular interest for distributions with significant skew. After establishing some theoretical properties of scale functions for $t$-digests, we show that a tangent line construction on the familiar scale functions preserves the crucial properties that allow $t$-digests to operate online and be mergeable. We conclude with an empirical study demonstrating the asymmetric variant preserves accuracy on one side of the distribution with a much smaller memory footprint.
