Implementing error detection in fast counting Bloom filters

2014 ◽  
Vol 50 (22) ◽  
pp. 1602-1604 ◽  
Author(s):  
P. Reviriego ◽  
J.A. Maestro
Keyword(s):  
Error Detection ◽  
Bloom Filters ◽  
Counting Bloom Filters

Accelerating packet classification with counting bloom filters for virtual OpenFlow switching

2018 ◽  
Vol 15 (10) ◽  
pp. 117-128 ◽  
Author(s):  
Jinyuan Zhao ◽  
Zhigang Hu ◽  
Bing Xiong ◽  
Keqin Li
Keyword(s):  
Packet Classification ◽  
Bloom Filters ◽  
Counting Bloom Filters

Algebra Operations on Counting Bloom Filters

2012 ◽  
Vol 35 (12) ◽  
pp. 2598
Author(s):  
Xiao-Mei TIAN ◽  
Da-Fang ZHANG ◽  
Kun XIE ◽  
Chang-Qiong SHI ◽  
Xiao-Bo YANG
Keyword(s):  
Bloom Filters ◽  
Counting Bloom Filters

scholarly journals Accurate Counting Bloom Filters for Large-Scale Data Processing

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Wei Li ◽  
Kun Huang ◽  
Dafang Zhang ◽  
Zheng Qin
Keyword(s):  
Data Processing ◽  
False Positive ◽  
Large Scale ◽  
Bloom Filters ◽  
Positive Probability ◽  
Large Scale Data ◽  
Large Scale Data Processing ◽  
False Positive Probability ◽  
Scale Data ◽  
Counting Bloom Filters

Bloom filters are space-efficient randomized data structures for fast membership queries, allowing false positives. Counting Bloom Filters (CBFs) perform the same operations on dynamic sets that can be updated via insertions and deletions. CBFs have been extensively used in MapReduce to accelerate large-scale data processing on large clusters by reducing the volume of datasets. The false positive probability of CBF should be made as low as possible for filtering out more redundant datasets. In this paper, we propose a multilevel optimization approach to building an Accurate Counting Bloom Filter (ACBF) for reducing the false positive probability. ACBF is constructed by partitioning the counter vector into multiple levels. We propose an optimized ACBF by maximizing the first level size, in order to minimize the false positive probability while maintaining the same functionality as CBF. Simulation results show that the optimized ACBF reduces the false positive probability by up to 98.4% at the same memory consumption compared to CBF. We also implement ACBFs in MapReduce to speed up the reduce-side join. Experiments on realistic datasets show that ACBF reduces the false positive probability by 72.3% as well as the map outputs by 33.9% and improves the join execution times by 20% compared to CBF.

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