Accurately Identify Time-decaying Heavy Hitters by Decay-aware Cuckoo Filter along Kicking Path

Xiao, Qingjun; Wang, Haotian; Pan, Guannan

doi:10.1109/iwqos54832.2022.9812870

Cited by 5 publications

(1 citation statement)

References 26 publications

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“…Such data streams convey valuable information that can be useful to a range of applications, including business intelligence, anomaly detection [3,4], recommendation systems [5], etc. One important objectives in stream mining is the identification of heavy items, which spans heavy hitter detection [6][7][8][9], heavy changer detection [10][11][12], persistent item lookup [13][14][15], and significant item lookup [16,17]. Heavy hitters indicate items with large size or frequency.…”

Section: Introductionmentioning

confidence: 99%

Tight-Sketch: A High-Performance Sketch for Heavy Item-Oriented Data Stream Mining with Limited Memory Size

Li,

Patras

2023

Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

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Accurate and fast data stream mining is critical and fundamental to many tasks, including time series database handling, big data management and machine learning. Different heavy-based detection tasks, such as heavy hitter, heavy changer, persistent item and significant item detection, have drawn much attention from both the industry and academia. Unfortunately, due to the growing data stream speeds and limited memory (L1 cache) available for real-time processing, existing schemes face challenges in simultaneously achieving high detection accuracy, high memory efficiency, and fast update throughput, as we reveal. To tackle this conundrum, we propose a versatile and elegant sketch framework named Tight-Sketch, which supports a spectrum of heavybased detection tasks. Considering that most items are cold (nonheavy/persistent/significant) in practice, we employ different eviction treatments for different types of items to discard these potentially cold ones as soon as possible, and offer more protection to those that are hot (heavy/persistent/significant). In addition, we propose an eviction method that follows a stochastic decay strategy, enabling Tight-Sketch to only bear small one-sided errors (no overestimation). We present a theoretical analysis of the error bounds and conduct extensive experiments on diverse detection tasks to demonstrate that Tight-Sketch significantly outperforms existing methods in terms of accuracy and update speed. Lastly, we accelerate Tight-Sketch's update throughput by up to 36% with Single Instruction Multiple Data (SIMD) instructions. CCS CONCEPTS• Information systems → Data stream mining.

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