Bloom filter

A Bloom filter can be considered a kind of superimposed code.

The regular or local bloom filter indicates which services are offered by the node itself.

In a simple bloom filter, there is no way to distinguish between the two cases, but more advanced techniques can address this problem.

Hence Bloom filters use 44% more space than a hypothetical equivalent optimal data structure.

Other algorithms that use multiple hash functions include the Bloom filter.

Bloom filter is a probabilistic method to find a subset of a given set.

The proposed variants have however the drawback of using about 32% more space than classic Bloom filters.

Bloom filters are a way of compactly representing a set of items.

Bloom filters can be used for approximate data synchronization as in .

Bloom filters can be used to approximate the size of the intersection and union of two sets.

Bloom filters are commonly used to search large databases of chemicals (see chemical similarity).

Design and implementation of new approach for searching in encrypted data using Bloom Filter.

Removing an element from this simple Bloom filter is impossible because false negatives are not permitted.

In fact, regular Bloom filters can be considered as counting filters with a bucket size of one bit.

Cuckoo hashing can be used to implement a data structure equivalent to a Bloom filter.

However, the space that is strictly necessary for any data structure playing the same role as a Bloom filter is only per key .

Some kinds of superimposed code can be seen as a Bloom filter implemented with physical edge-notched cards.

Since the patterns don't match, we check the attenuated bloom filter in order to determine which node should be the next hop.

Decompressing the whole Bloom filter for each query would make this variant totally unusable.

Another alternative to classic Bloom filter is the one based on space efficient variants of cuckoo hashing.

In a hardware implementation, however, the Bloom filter shines because its k lookups are independent and can be parallelized.

Commonly, the tanimoto similarity is used to quantify the similarity between molecules' bloom filters.

Each molecule is represented with a bloom filter (called a fingerprint in this field) which stores substructures of the molecule.

(for the Bloom filter extension)

For such applications, compressed bit arrays, Judy arrays, tries, or even Bloom filters should be considered instead.