Let U be the set of integers {1,..., U}, and S of a subset of U with size n ( ≤ U). We want to preprocess S into a structure so that, given any integer q ∈ U, we can determine quickly whether q ∈ S. We will refer to this problem as the membership problem. We represent each integer in U with w = logU bits (all logarithms have base 2 by default). A simple solution is to build a hash table on S which answers a query in constant time (which can be made worst-case using perfect hashing). The space of the hash table is O(nw) bits. In this lecture, we ask the question: is it possible to solve the problem using a structure of o(nw) bits? The answer is negative if a precise answer is always desired – it is easy to show that any structure for this pu...
Abstract — In this paper, we propose the Generalized Bloom Filter (GBF), a space-efficient data stru...
Today we’re going to talk about: 1. linear probing (show with 5-wise independence) 2. approximate me...
Bloom filters are probabilistic data structures commonly used for approximate membership problems in...
A Bloom Filter is a simple space-efficient randomized data structure for representing a set in order...
An approximate membership data structure is a randomized data structure representing a set which sup...
Abstract—In this paper we consider the problem of designing a data structure that can perform fast m...
This paper considers space-efficient data structures for storing an approximation S ′ to a set S suc...
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-...
Bloom filters are hash-based data structures for membership queries without false negatives widely u...
Bloom filters [2] are compact data structures for probabilistic representation of a set in order to ...
A Bloom Filter is an efficient randomized data structure for membership queries on a set with a cert...
The membership problem asks to maintain a set S ? [u], supporting insertions and membership queries,...
A Bloom filter is a method for reducing the space (memory) required for representing a set by allowi...
Abstract — In this paper, we propose the Generalized Bloom Filter (GBF), a space-efficient data stru...
Today we’re going to talk about: 1. linear probing (show with 5-wise independence) 2. approximate me...
Bloom filters are probabilistic data structures commonly used for approximate membership problems in...
A Bloom Filter is a simple space-efficient randomized data structure for representing a set in order...
An approximate membership data structure is a randomized data structure representing a set which sup...
Abstract—In this paper we consider the problem of designing a data structure that can perform fast m...
This paper considers space-efficient data structures for storing an approximation S ′ to a set S suc...
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-...
Bloom filters are hash-based data structures for membership queries without false negatives widely u...
Bloom filters [2] are compact data structures for probabilistic representation of a set in order to ...
A Bloom Filter is an efficient randomized data structure for membership queries on a set with a cert...
The membership problem asks to maintain a set S ? [u], supporting insertions and membership queries,...
A Bloom filter is a method for reducing the space (memory) required for representing a set by allowi...
Abstract — In this paper, we propose the Generalized Bloom Filter (GBF), a space-efficient data stru...
Today we’re going to talk about: 1. linear probing (show with 5-wise independence) 2. approximate me...
Bloom filters are probabilistic data structures commonly used for approximate membership problems in...