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Two-layer partitioned and deletable deep bloom filter for large-scale membership query

文献类型：期刊论文

作者Zeng, Meng4; Zou, Beiji4; Zhang, Wensheng1; Yang, Xuebing1; Kong, Guilan2,5; Kui, Xiaoyan4; Zhu, Chengzhang3
刊名INFORMATION SYSTEMS
出版日期2023-10-01
卷号119页码:13
关键词Learned bloom filter Membership query Deep learning K-means cluster Perfect hash function
ISSN号0306-4379
DOI10.1016/j.is.2023.102267
通讯作者Zou, Beiji(bjzou@csu.edu.cn)
英文摘要The recently proposed Learned Bloom Filter (LBF) provides a new perspective on large-scale membership queries by using machine learning to replace the traditional bloom filter. However, reducing the false positive rate (FPR) of the learned model with small memory usage, and supporting deletion efficiently become the new issues. In this paper, we propose a novel Two-layer Partitioned and Deletable Deep Bloom Filter (PDDBF) for large-scale membership query, which can reduce the FPR with small memory usage and support deletion efficiently. The proposed PDDBF consists of three main parts: (1) Data partition. To improve the classification accuracy of the learned model, the K-means cluster with the elbow method is used for the data partition. (2) Deep Bloom Filter. To reduce the FPR, deep learning models are used to construct multiple independent learning mechanisms, which correspond to the clusters obtained by part1. (3) Partitioned backup filter. To support deletion under the premise of ensuring low FPR and reducing query time consumption, combine the perfect hash (PH) table and counting bloom filters (CBFs) on the basis of the partition bloom filter. Experiments show that the proposed PDDBF reduces the FPR 87.13% with the same memory usage compared with the state-of-the-art PLBF on real-world URLs data set. Moreover, the PDDBF reduces the FPR 99.68% with the same memory usage and reduces the query time consumption to 2.61x that of the PLBF after data deletion, respectively.& COPY; 2023 Elsevier Ltd. All rights reserved.
资助项目National Key R&D Program of China[2018AAA0102100] ; Key Research and Development Program of Hunan Province[2022SK2054] ; 111 project, China[B18059] ; National Natural Science Foundation of China[62177047]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:001067587700001
出版者PERGAMON-ELSEVIER SCIENCE LTD
资助机构National Key R&D Program of China ; Key Research and Development Program of Hunan Province ; 111 project, China ; National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/53107]  
专题多模态人工智能系统全国重点实验室
通讯作者Zou, Beiji
作者单位1.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
2.Peking Univ, Adv Inst Informat Technol, Hangzhou, Peoples R China
3.Cent South Univ, Coll Literature & Journalism, Changsha, Peoples R China
4.Cent South Univ, Sch Comp Sci & Engn, Changsha, Peoples R China
5.Peking Univ, Natl Inst Hlth Data Sci, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Zeng, Meng,Zou, Beiji,Zhang, Wensheng,et al. Two-layer partitioned and deletable deep bloom filter for large-scale membership query[J]. INFORMATION SYSTEMS,2023,119:13.
APA Zeng, Meng.,Zou, Beiji.,Zhang, Wensheng.,Yang, Xuebing.,Kong, Guilan.,...&Zhu, Chengzhang.(2023).Two-layer partitioned and deletable deep bloom filter for large-scale membership query.INFORMATION SYSTEMS,119,13.
MLA Zeng, Meng,et al."Two-layer partitioned and deletable deep bloom filter for large-scale membership query".INFORMATION SYSTEMS 119(2023):13.

入库方式： OAI收割

来源：自动化研究所

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