Add to ORKGAbstract. A standard technique from the hashing literature is to use two hash functions h1(x)andh2(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.
Abstract—Hash tables are used in many networking applica-tions, such as lookup and packet classifica...
In a partitioned Bloom Filter the $m$ bit vector is split into $k$ disjoint $m/k$ sized parts, one p...
This paper considers space-efficient data structures for storing an approximation S ′ to a set S suc...
A standard technique from the hashing literature is to use two hash functions h1(x) and h2(x) to sim...
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...
Abstract. A counting Bloom filter (CBF) generalizes a Bloom filter data structure so as to allow mem...
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...
The Bloom Filter (BF), a space-and-time-efficient hashcoding method, is used as one of the fundament...
Bloom Filters are a technique to reduce the effects of conflicts/ interference in hash table-like st...
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...
efficient hash-coding method, is used as one of the fundamen-tal modules in several network processi...
Abstract—Hash tables are used in many networking applica-tions, such as lookup and packet classifica...
In a partitioned Bloom Filter the $m$ bit vector is split into $k$ disjoint $m/k$ sized parts, one p...
This paper considers space-efficient data structures for storing an approximation S ′ to a set S suc...
A standard technique from the hashing literature is to use two hash functions h1(x) and h2(x) to sim...
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...
Abstract. A counting Bloom filter (CBF) generalizes a Bloom filter data structure so as to allow mem...
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...
The Bloom Filter (BF), a space-and-time-efficient hashcoding method, is used as one of the fundament...
Bloom Filters are a technique to reduce the effects of conflicts/ interference in hash table-like st...
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...
efficient hash-coding method, is used as one of the fundamen-tal modules in several network processi...
Abstract—Hash tables are used in many networking applica-tions, such as lookup and packet classifica...
In a partitioned Bloom Filter the $m$ bit vector is split into $k$ disjoint $m/k$ sized parts, one p...
This paper considers space-efficient data structures for storing an approximation S ′ to a set S suc...