Dynamic Partition Bloom Filters: A Bounded False Positive Solution For Dynamic Set Membership (Extended Abstract)

01/19/2019
by   Sidharth Negi, et al.
0

Dynamic Bloom filters (DBF) were proposed by Guo et. al. in 2010 to tackle the situation where the size of the set to be stored compactly is not known in advance or can change during the course of the application. We propose a novel competitor to DBF with the following important property that DBF is not able to achieve: our structure is able to maintain a bound on the false positive rate for the set membership query across all possible sizes of sets that are stored in it. The new data structure we propose is a dynamic structure that we call Dynamic Partition Bloom filter (DPBF). DPBF is based on our novel concept of a Bloom partition tree which is a tree structure with standard Bloom filters at the leaves. DPBF is superior to standard Bloom filters because it can efficiently handle a large number of unions and intersections of sets of different sizes while controlling the false positive rate. This makes DPBF the first structure to do so to the best of our knowledge. We provide theoretical bounds comparing the false positive probability of DPBF to DBF.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
07/06/2021

Telescoping Filter: A Practical Adaptive Filter

Filters are fast, small and approximate set membership data structures. ...
research
05/22/2021

Support Optimality and Adaptive Cuckoo Filters

Filters (such as Bloom Filters) are data structures that speed up networ...
research
01/18/2018

NAE-SAT-based probabilistic membership filters

Probabilistic membership filters are a type of data structure designed t...
research
10/05/2021

Distcomp: Comparing distributions

The distcomp command is introduced and illustrated. The command assesses...
research
06/23/2021

A Bloom Filter Survey: Variants for Different Domain Applications

There is a plethora of data structures, algorithms, and frameworks deali...
research
05/30/2022

Daisy Bloom Filters

Weighted Bloom filters (Bruck, Gao and Jiang, ISIT 2006) are Bloom filte...
research
11/28/2022

A Critical Analysis of Classifier Selection in Learned Bloom Filters

Learned Bloom Filters, i.e., models induced from data via machine learni...

Please sign up or login with your details

Forgot password? Click here to reset