BLOOM FILTER BASED ON UNASSOCIATED HASH METHODS

Abstract

Blossom filters are basic randomized information structures that are greatly valuable practically speaking. Truth be told, they are useful to the point that any noteworthy lessening in the time required to play out a Bloom channel operation instantly means a considerable speedup for some functional applications. Sadly, Bloom filters are simple to the point that they don't leave much space for advancement. At the point when space is an issue, a Bloom channel might be an astounding contrasting option to keeping an express rundown. The disadvantage of utilizing a Bloom channel is that it permits false positives. Their impact must be painstakingly considered for every particular application to decide if the effect of false positives is adequate. In this research work it is shown that the false positive performance of a standard Bloom filter implementation strongly relies on the selection of hash functions, even if these hash functions are considered good. The three hashing methods implemented for the ideal bloom filter are compared on the grounds of their relative time consumption, false positive and number of collisions.