Deep Learning-Based Bloom Filter for Efficient Multi-key Membership Testing

Haitian Chen

doi:10.1007/s41019-023-00224-9

Deep Learning-Based Bloom Filter for Efficient Multi-key Membership Testing

2023-09-02

0

OA

AI

DOI：10.1007/s41019-023-00224-9

Original

Original request for help

Save

Abstract

En 中文

Multi-key membership testing plays a crucial role in computing systems and networking applications, encompassing web search, mail systems, distributed databases, firewalls, and network routing. Traditional approaches, such as the Bloom filter, encounter limitations within this specific context. Addressing these challenges, we propose the Multi-key Learned Bloom Filter (MLBF), a hybrid method that combines machine learning techniques with the Bloom filter. The MLBF introduces a value-interaction-based multi-key classifier and a multi-key Bloom filter. Furthermore, we introduce an Interval-based MLBF approach, which categorizes keys into specific intervals based on data distribution to minimize the False Positive Rate (FPR). Additionally, MLBF incorporates an out-of-distribution (OOD) detection component to identify data shifts. Through extensive experimental evaluations on three authentic datasets, we demonstrate the superiority of the proposed MLBF in terms of FPR and query efficiency.

Keywords:

Bloom filter

Learned structures

Learned bloom filter

Membership testing

Deep Learning-Based Bloom Filter for Efficient Multi-key Membership Testing

Abstract

Journal

Organization