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arXiv · 1510.03019

A Shifting Bloom Filter Framework for Set Queries

Tong Yang·Alex X. Liu·Muhammad Shahzad·Yuankun Zhong·Qiaobin Fu·Zi Li·Gaogang Xie·Xiaoming Li
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Abstract

Set queries are fundamental operations in computer systems and applications.This paper addresses the fundamental problem of designing a probabilistic data structure that can quickly process set queries using a small amount of memory. We propose a Shifting Bloom Filter (ShBF) framework for representing and querying sets. We demonstrate the effectiveness of ShBF using three types of popular set queries: membership, association, and multiplicity queries. The key novelty of ShBF is on encoding the auxiliary information of a set element in a location offset. In contrast, prior BF based set data structures allocate additional memory to store auxiliary information. To evaluate ShBF in comparison with prior art, we conducted experiments using real-world network traces. Results show that ShBF significantly advances the state-of-the-art on all three types of set queries.

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Tong Yang, Alex X. Liu, Muhammad Shahzad, Yuankun Zhong, Qiaobin Fu, Zi Li, Gaogang Xie, Xiaoming Li. 2016-03-22. A Shifting Bloom Filter Framework for Set Queries. https://arxiv.org/abs/1510.03019

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