Hash Adaptive Bloom Filter
Rongbiao Xie, Meng Li, Zheyu Miao, Rong Gu, He Huang, Haipeng Dai,, Guihai Chen

TL;DR
The paper introduces Hash Adaptive Bloom Filter (HABF), a novel probabilistic data structure that customizes hash functions for improved accuracy and efficiency in membership testing, addressing limitations of traditional Bloom filters.
Contribution
HABF allows hash function customization for positive keys, enhancing performance and flexibility over standard Bloom filters, with theoretical analysis and extensive experimental validation.
Findings
HABF reduces false positive rates compared to standard Bloom filters.
HABF improves construction and query times.
HABF uses less memory while maintaining accuracy.
Abstract
Bloom filter is a compact memory-efficient probabilistic data structure supporting membership testing, i.e., to check whether an element is in a given set. However, as Bloom filter maps each element with uniformly random hash functions, few flexibilities are provided even if the information of negative keys (elements are not in the set) are available. The problem gets worse when the misidentification of negative keys brings different costs. To address the above problems, we propose a new Hash Adaptive Bloom Filter (HABF) that supports the customization of hash functions for keys. The key idea of HABF is to customize the hash functions for positive keys (elements are in the set) to avoid negative keys with high cost, and pack customized hash functions into a lightweight data structure named HashExpressor. Then, given an element at query time, HABF follows a two-round pattern to check…
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Taxonomy
TopicsCaching and Content Delivery · Covalent Organic Framework Applications · Carbon and Quantum Dots Applications
