Chapter 4: Impact of the hash functions in Bloom Filter design
Abstract
The performance of the Bloom Filter does not solely depend on the hash function; it also depends on the false positive probability and memory footprint. A good hash function is required for Bloom Filter to reduce the false positive probability and memory footprint. Also, a fast hash function significantly enhances the Bloom Filter performance. Therefore, we experimentally demonstrate the importance of the hash function in developing a new Bloom Filter. We compare various hash functions with robustBF to unearth the weakness of robustBF. Furthermore, we use various test cases to reveal the weakness of the hash functions. Alternatively, we design diverse datasets in the query ...
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