scholarly journals Certifying Certainty and Uncertainty in Approximate Membership Query Structures

2020 ◽  
pp. 279-303
Author(s):  
Kiran Gopinathan ◽  
Ilya Sergey
Keyword(s):  
Membership Query

Membership Query

2009 ◽  
pp. 1707-1707
Author(s):  
Mirella M. Moro
Keyword(s):  
Membership Query

Membership Query

2018 ◽  
pp. 2222-2222
Author(s):  
Mirella M. Moro
Keyword(s):  
Membership Query

Locality-Sensitive Bloom Filter for Approximate Membership Query

2012 ◽  
Vol 61 (6) ◽  
pp. 817-830 ◽  
Author(s):  
Yu Hua ◽  
Bin Xiao ◽  
Bharadwaj Veeravalli ◽  
Dan Feng
Keyword(s):  
Bloom Filter ◽  
Membership Query

scholarly journals findere: Fast and Precise Approximate Membership Query

2021 ◽  
pp. 151-163
Author(s):  
Lucas Robidou ◽  
Pierre Peterlongo
Keyword(s):  
Membership Query

Efficient Update Control of Bloom Filter Replicas in Large Scale Distributed Systems

2010 ◽  
pp. 785-807 ◽  
Author(s):  
Yifeng Zhu ◽  
Hong Jiang
Keyword(s):  
Distributed Systems ◽  
Large Scale ◽  
Control Mechanism ◽  
False Negative ◽  
Bloom Filter ◽  
Analytical Models ◽  
Bloom Filters ◽  
Distributed Environment ◽  
Membership Query ◽  
Efficient Data

This chapter discusses the false rates of Bloom filters in a distributed environment. A Bloom filter (BF) is a space-efficient data structure to support probabilistic membership query. In distributed systems, a Bloom filter is often used to summarize local services or objects and this Bloom filter is replicated to remote hosts. This allows remote hosts to perform fast membership query without contacting the original host. However, when the services or objects are changed, the remote Bloom replica may become stale. This chapter analyzes the impact of staleness on the false positive and false negative for membership queries on a Bloom filter replica. An efficient update control mechanism is then proposed based on the analytical results to minimize the updating overhead. This chapter validates the analytical models and the update control mechanism through simulation experiments.

rDBF: A r-Dimensional Bloom Filter for massive scale membership query

2019 ◽  
Vol 136 ◽  
pp. 100-113 ◽  
Author(s):  
Ripon Patgiri ◽  
Sabuzima Nayak ◽  
Samir Kumar Borgohain
Keyword(s):  
Bloom Filter ◽  
Membership Query ◽  
Massive Scale
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