We introduce the Bloomier filter, a data structure for compactly encoding a function with static support in order to support approximate evaluation queries. Our construction generalizes the classical Bloom filter, an ingenious hashing scheme heavily used in networks and databases, whose main attribute—space efficiency—is achieved at the expense of a tiny false-positive rate. Whereas Bloom filters can handle only set membership queries, our Bloomier filters can deal with arbitrary functions. We give several designs varying in simplicity and optimality, and we provide lower bounds to prove the (near) optimality of our constructions.
The Bloom Filter (BF), a space-and-time-efficient hashcoding method, is used as one of the fundament...
A Bloom filter is a simple randomized data structure that answers membership query with no false neg...
Bloom filters are efficient randomized data structures for membership queries on a set with a certai...
We introduce the Bloomier filter, a data structure for compactly encoding a function with static sup...
A Bloom filter is a very compact data structure that supports approximate membership queries on a se...
A Bloom Filter is an efficient randomized data structure for membership queries on a set with a cert...
Bloom filters are compact set representations that support set membership queries with small, one-...
This paper presents new alternatives to the well-known Bloom filter data structure. The Bloom filter...
Abstract-A Bloom Filter is a space-efficient data structure allowing membership queries over sets wi...
A Bloom Filter is an efficient randomized data structure for membership queries on a set with a cert...
This paper considers space-efficient data structures for storing an approximation S ′ to a set S suc...
A Bloom Filter is a simple space-efficient randomized data structure for representing a set in order...
A retrieval data structure for a static function f: S ? {0,1}^r supports queries that return f(x) fo...
We present a version of the Bloom filter data structure that supports not only the insertion, deleti...
Bloom Filter is a simple space-efficient randomized data structure for representing a set in order t...
The Bloom Filter (BF), a space-and-time-efficient hashcoding method, is used as one of the fundament...
A Bloom filter is a simple randomized data structure that answers membership query with no false neg...
Bloom filters are efficient randomized data structures for membership queries on a set with a certai...
We introduce the Bloomier filter, a data structure for compactly encoding a function with static sup...
A Bloom filter is a very compact data structure that supports approximate membership queries on a se...
A Bloom Filter is an efficient randomized data structure for membership queries on a set with a cert...
Bloom filters are compact set representations that support set membership queries with small, one-...
This paper presents new alternatives to the well-known Bloom filter data structure. The Bloom filter...
Abstract-A Bloom Filter is a space-efficient data structure allowing membership queries over sets wi...
A Bloom Filter is an efficient randomized data structure for membership queries on a set with a cert...
This paper considers space-efficient data structures for storing an approximation S ′ to a set S suc...
A Bloom Filter is a simple space-efficient randomized data structure for representing a set in order...
A retrieval data structure for a static function f: S ? {0,1}^r supports queries that return f(x) fo...
We present a version of the Bloom filter data structure that supports not only the insertion, deleti...
Bloom Filter is a simple space-efficient randomized data structure for representing a set in order t...
The Bloom Filter (BF), a space-and-time-efficient hashcoding method, is used as one of the fundament...
A Bloom filter is a simple randomized data structure that answers membership query with no false neg...
Bloom filters are efficient randomized data structures for membership queries on a set with a certai...