Name
Abstract | ||
|---|---|---|
Bloom filter (BF) has been widely used to support membership query, i.e., to judge whether a given element
<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${x}$ </tex-math></inline-formula>
is a member of a given set
<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${S}$ </tex-math></inline-formula>
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/COMST.2018.2889329 | IEEE Communications Surveys and Tutorials |
Keywords | Field | DocType |
Optimization,Tutorials,Probabilistic logic,Market research,Proposals | Bloom filter,Discrete mathematics,Artificial intelligence,Philosophy of design,Mathematics,Machine learning,False positive paradox | Journal |
Volume | Issue | ISSN |
abs/1804.04777 | 2 | 1553-877X |
Citations | PageRank | References |
9 | 0.56 | 100 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lailong Luo | 1 | 18 | 6.50 |
| Deke Guo | 2 | 326 | 47.25 |
| Richard T. B. Ma | 3 | 620 | 51.15 |
| Ori Rottenstreich | 4 | 318 | 29.90 |
| Xueshan Luo | 5 | 9 | 0.56 |