Knowledge Commons of Institute of Automation,CAS
Two-layer partitioned and deletable deep bloom filter for large-scale membership query | |
Zeng, Meng1; Zou, Beiji1; Zhang, Wensheng2; Yang, Xuebing2; Kong, Guilan3,4; Kui, Xiaoyan1; Zhu, Chengzhang5 | |
发表期刊 | INFORMATION SYSTEMS |
ISSN | 0306-4379 |
2023-10-01 | |
卷号 | 119页码:13 |
通讯作者 | Zou, Beiji(bjzou@csu.edu.cn) |
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. |
关键词 | Learned bloom filter Membership query Deep learning K-means cluster Perfect hash function |
DOI | 10.1016/j.is.2023.102267 |
收录类别 | SCI |
语种 | 英语 |
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] |
项目资助者 | National Key R&D Program of China ; Key Research and Development Program of Hunan Province ; 111 project, China ; National Natural Science Foundation of China |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Information Systems |
WOS记录号 | WOS:001067587700001 |
出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/53108 |
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, Sch Comp Sci & Engn, Changsha, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China 3.Peking Univ, Natl Inst Hlth Data Sci, Beijing, Peoples R China 4.Peking Univ, Adv Inst Informat Technol, Hangzhou, Peoples R China 5.Cent South Univ, Coll Literature & Journalism, Changsha, 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. |
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