NDLI: Noisy Bloom Filters for Multi-Set Membership Testing

Content Provider	ACM Digital Library
Author	Wang, Wei Zhong, Yuankun Liu, Alex X. Li, Meng Dai, Haipeng
Abstract	This paper is on designing a compact data structure for multi-set membership testing allowing fast set querying. Multi-set membership testing is a fundamental operation for computing systems and networking applications. Most existing schemes for multi-set membership testing are built upon Bloom filter, and fall short in either storage space cost or query speed. To address this issue, in this paper we propose Noisy Bloom Filter (NBF) and Error Corrected Noisy Bloom Filter (NBF-E) for multi-set membership testing. For theoretical analysis, we optimize their classification failure rate and false positive rate, and present criteria for selection between NBF and NBF-E. The key novelty of NBF and NBF-E is to store set ID information in a compact but noisy way that allows fast recording and querying, and use denoising method for querying. Especially, NBF-E incorporates asymmetric error-correcting coding technique into NBF to enhance the resilience of query results to noise by revealing and leveraging the asymmetric error nature of query results. To evaluate NBF and NBF-E in comparison with prior art, we conducted experiments using real-world network traces. The results show that NBF and NBF-E significantly advance the state-of-the-art on multi-set membership testing.
Starting Page	139
Ending Page	151
Page Count	13
File Format	PDF
ISSN	01635999
DOI	10.1145/2964791.2901451
Journal	ACM SIGMETRICS Performance Evaluation Review (PERV)
Volume Number	44
Issue Number	1
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-01-10
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Asymmetric error-correcting code Noise Multi-set membership testing Constant weight code. Bloom filter
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Hardware and Architecture Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Noisy Bloom Filters for Multi-Set Membership Testing

Fast Dynamic Multiple-Set Membership Testing Using Combinatorial Bloom Filters

Fast dynamic multiple-set membership testing using combinatorial bloom filters

Building high accuracy bloom filters using partitioned hashing

Fast dynamic multiset membership testing using combinatorial bloom filters.

Tree-structured Bloom Filters for Joint Optimization of False Positive Probability and Transmission Bandwidth

Theory of Unidirectional Error Correcting/Detecting Codes

Bloom filter-based scalable multicast: methodology, design and application

Fast statistical spam filter by approximate classifications

Noisy Bloom Filters for Multi-Set Membership Testing

Similar Documents

Noisy Bloom Filters for Multi-Set Membership Testing

Fast Dynamic Multiple-Set Membership Testing Using Combinatorial Bloom Filters

Fast dynamic multiple-set membership testing using combinatorial bloom filters

Building high accuracy bloom filters using partitioned hashing

Fast dynamic multiset membership testing using combinatorial bloom filters.

Tree-structured Bloom Filters for Joint Optimization of False Positive Probability and Transmission Bandwidth

Theory of Unidirectional Error Correcting/Detecting Codes

Bloom filter-based scalable multicast: methodology, design and application

Fast statistical spam filter by approximate classifications

Noisy Bloom Filters for Multi-Set Membership Testing