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

doi:10.1016/j.is.2023.102267

CORC > 自动化研究所 > 中国科学院自动化研究所 > 多模态人工智能系统全国重点实验室

	Two-layer partitioned and deletable deep bloom filter for large-scale membership query
	Zeng, Meng 3; Zou, Beiji 3; Zhang, Wensheng 4; Yang, Xuebing 4; Kong, Guilan 2,5; Kui, Xiaoyan 3; Zhu, Chengzhang 1
刊名	INFORMATION SYSTEMS
	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
DOI	10.1016/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
出版者	PERGAMON-ELSEVIER SCIENCE LTD
WOS记录号	WOS:001067587700001
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
内容类型	期刊论文
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.Cent South Univ, Coll Literature & Journalism, Changsha, Peoples R China 2.Peking Univ, Adv Inst Informat Technol, Hangzhou, Peoples R China 3.Cent South Univ, Sch Comp Sci & Engn, Changsha, Peoples R China 4.Chinese Acad Sci, Inst Automat, Beijing, 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.