Reef: Efficient and Accurate Recent Heavy Hitter Detection

Detecting recent heavy hitters, objects that frequently appear in the recent past, is fundamental yet challenging for high-speed and time-varying data streams. Approximation offers memory-efficient detection, albeit with accuracy degradation. To optimize the trade-off, we propose Reef, an efficient and accurate framework that leverages real-world stream patterns for recent heavy hitter detection. Reef models item arrivals to prioritize recent occurrences and dynamically filters out irrelevant objects to lower the detection complexity. We conduct trace-driven evaluation against 12 state-of-the-art studies. Results show that Reef improves the precision of baselines by up to 59.6× and reduces the ARE by up to 99.8% under limited memory.