A Shifting Bloom Filter Framework for Set Queries
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.
Cite
@article{arxiv.1510.03019,
title = {A Shifting Bloom Filter Framework for Set Queries},
author = {Tong Yang and Alex X. Liu and Muhammad Shahzad and Yuankun Zhong and Qiaobin Fu and Zi Li and Gaogang Xie and Xiaoming Li},
journal= {arXiv preprint arXiv:1510.03019},
year = {2016}
}