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A Bloom filter is a probabilistic data structure used to test whether an element is a member of a set. It is highly space-efficient and allows for quick insertions and membership tests. However, it has a small probability of false positives but no false negatives
A novel, space-efficient, probabilistic data structure for hashing a large number of values. It is similar to a hash table, but smaller because it does not need to store the entire value like HashTable and has better asymptotic performance, at the cost of possible false-positives, a likelihood that is configurable. False negatives are not possib…
This Bloom Filter implementation offers probabilistic data structure for fast set membership testing. Efficiently handling large datasets with minimal space, it ensures quick and accurate element existence checks, limits memory usage and retains speed.