3 research outputs found
Autoscaling Bloom Filter: Controlling Trade-off Between True and False Positives
A Bloom filter is a simple data structure supporting membership queries on a
set. The standard Bloom filter does not support the delete operation,
therefore, many applications use a counting Bloom filter to enable deletion.
This paper proposes a generalization of the counting Bloom filter approach,
called "autoscaling Bloom filters", which allows adjustment of its capacity
with probabilistic bounds on false positives and true positives. In essence,
the autoscaling Bloom filter is a binarized counting Bloom filter with an
adjustable binarization threshold. We present the mathematical analysis of the
performance as well as give a procedure for minimization of the false positive
rate.Comment: 13 pages, 3 figure
Shed More Light on Bloom Filter's Variants
Bloom Filter is a probabilistic membership data structure and it is
excessively used data structure for membership query. Bloom Filter becomes the
predominant data structure in approximate membership filtering. Bloom Filter
extremely enhances the query response time, and the response time is very fast.
Bloom filter (BF) is used to detect whether an element belongs to a given set
or not. The Bloom Filter returns True Positive (TP), False Positive (FP), or
True Negative (TN). The Bloom Filter is widely adapted in numerous areas to
enhance the performance of a system. In this paper, we present a) in-depth
insight on the Bloom Filter,and b) the prominent variants of the Bloom Filters.Comment: 8 pages, 5 Figures, 1 Table, Proceedings of the 2018 International
Conference on Information and Knowledge Engineering (IKE'18), pp. 14-2
Optimizing Bloom Filter: Challenges, Solutions, and Comparisons
Bloom filter (BF) has been widely used to support membership query, i.e., to
judge whether a given element x is a member of a given set S or not. Recent
years have seen a flourish design explosion of BF due to its characteristic of
space-efficiency and the functionality of constant-time membership query. The
existing reviews or surveys mainly focus on the applications of BF, but fall
short in covering the current trends, thereby lacking intrinsic understanding
of their design philosophy. To this end, this survey provides an overview of BF
and its variants, with an emphasis on the optimization techniques. Basically,
we survey the existing variants from two dimensions, i.e., performance and
generalization. To improve the performance, dozens of variants devote
themselves to reducing the false positives and implementation costs. Besides,
tens of variants generalize the BF framework in more scenarios by diversifying
the input sets and enriching the output functionalities. To summarize the
existing efforts, we conduct an in-depth study of the existing literature on BF
optimization, covering more than 60 variants. We unearth the design philosophy
of these variants and elaborate how the employed optimization techniques
improve BF. Furthermore, comprehensive analysis and qualitative comparison are
conducted from the perspectives of BF components. Lastly, we highlight the
future trends of designing BFs. This is, to the best of our knowledge, the
first survey that accomplishes such goals