Add to ORKGA standard technique from the hashing literature is to use two hash functions h1(x) and h2(x) to simulate additional hash functions of the form gi(x) = h1(x) + ih2(x). We demonstrate that this technique can be usefully applied to Bloom filters and related data structures. Specifically, only two hash functions are necessary to effectively implement a Bloom filter without any loss in the asymptotic false positive probability. This leads to less computation and potentially less need for randomness in practice.
In a partitioned Bloom Filter the $m$ bit vector is split into $k$ disjoint $m/k$ sized parts, one p...
Abstract—Hash tables are used in many networking applica-tions, such as lookup and packet classifica...
This paper considers space-efficient data structures for storing an approximation S ′ to a set S suc...
Abstract. A standard technique from the hashing literature is to use two hash functions h1(x)andh2(x...
A technique from the hashing literature is to use two hash functions h1(x) and h2(x) to simulate add...
A Bloom Filter is an efficient randomized data structure for membership queries on a set with a cert...
A Bloom filter is a very compact data structure that supports approximate membership queries on a se...
This paper presents new alternatives to the well-known Bloom filter data structure. The Bloom filter...
A Bloom filter is a compact data structure that supports membership queries on a set, allowing false...
Abstract. A counting Bloom filter (CBF) generalizes a Bloom filter data structure so as to allow mem...
Bloom Filters are a technique to reduce the effects of conflicts/ interference in hash table-like st...
The Bloom Filter (BF), a space-and-time-efficient hashcoding method, is used as one of the fundament...
efficient hash-coding method, is used as one of the fundamen-tal modules in several network processi...
Abstract — In this paper, we propose the Generalized Bloom Filter (GBF), a space-efficient data stru...
Bloom filters are hash-based data structures for membership queries without false negatives widely u...
In a partitioned Bloom Filter the $m$ bit vector is split into $k$ disjoint $m/k$ sized parts, one p...
Abstract—Hash tables are used in many networking applica-tions, such as lookup and packet classifica...
This paper considers space-efficient data structures for storing an approximation S ′ to a set S suc...
Abstract. A standard technique from the hashing literature is to use two hash functions h1(x)andh2(x...
A technique from the hashing literature is to use two hash functions h1(x) and h2(x) to simulate add...
A Bloom Filter is an efficient randomized data structure for membership queries on a set with a cert...
A Bloom filter is a very compact data structure that supports approximate membership queries on a se...
This paper presents new alternatives to the well-known Bloom filter data structure. The Bloom filter...
A Bloom filter is a compact data structure that supports membership queries on a set, allowing false...
Abstract. A counting Bloom filter (CBF) generalizes a Bloom filter data structure so as to allow mem...
Bloom Filters are a technique to reduce the effects of conflicts/ interference in hash table-like st...
The Bloom Filter (BF), a space-and-time-efficient hashcoding method, is used as one of the fundament...
efficient hash-coding method, is used as one of the fundamen-tal modules in several network processi...
Abstract — In this paper, we propose the Generalized Bloom Filter (GBF), a space-efficient data stru...
Bloom filters are hash-based data structures for membership queries without false negatives widely u...
In a partitioned Bloom Filter the $m$ bit vector is split into $k$ disjoint $m/k$ sized parts, one p...
Abstract—Hash tables are used in many networking applica-tions, such as lookup and packet classifica...
This paper considers space-efficient data structures for storing an approximation S ′ to a set S suc...