Name
Abstract | ||
|---|---|---|
In this paper we focus on improving the false positive rate of a bloom filter with a pre-filtering scheme. By applying this scheme on a bloom filter, we can quickly screen out lots of input before entering the bloom filter and hence improve the result of false positives. We demonstrate with experiments that this approach can yield at least 4 times and at most 45 times better results than a standard bloom filter implementation, meanwhile using the same amount of memory requirement as a standard one. The proposed approach, called the pre-filtered bloom filter (PFBF), outperforms existing approaches in most of the cases. Especially, our approach is attractive to those applications which have limited amount of memory with a lot of patterns to check, since, in this case we can get the most improvement out of it. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.3233/978-1-61499-484-8-32 | Frontiers in Artificial Intelligence and Applications |
Keywords | Field | DocType |
Bloom filter,pre-filtering,false positive | Bloom filter,Computer science,Parallel computing | Conference |
Volume | ISSN | Citations |
274 | 0922-6389 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ssu-Ting Liu | 1 | 0 | 0.68 |
| Sheng-De Wang | 2 | 720 | 68.13 |