Online Detection of Outstanding Quantiles with QuantileFilter

Yuhan Wu, Aomufei Yuan, Zhouran Shi, Yuanpeng Li, Yikai Zhao, Peiqing Chen, Tong Yang, Bin Cui

Abstract: In quantile estimation within a stream of key-value pairs, recent work has made significant progress in query flexibility, supporting quantile estimation for any key using a unified statistical structure. However, despite this flexibility, their query speed falls behind, unable to match the high speed of online data insertion. This “offline query + online insertion” model is not ideal for online quantile estimation. Our goal is to online detect keys whose quantiles exceed a user-queried threshold in real-time, such as identifying the user whose 95 % latency exceeds 200ms in network data. These keys, termed “Quantile-Outstanding Keys,” are vital for anomaly detection in streaming data. In this paper, we propose QuantileFilter, the first approximate algorithm specifically designed for detecting quantile-outstanding keys. QuantileFilter overcomes existing limitations by 1) enabling fast online computation, capable of handling streaming data in real-time with a constant processing time for each data item, accelerating the state-of-the-art (SOTA) by 10 ~ 100 times, and 2) maintaining high space efficiency, saving 50 ~ 500 times storage space compared to the SOTA while maintaining the same accuracy. All associated code is available on GitHub.