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The program execution initializes the data queue, according to the time attribute of the message to its hash calculation, and then compares the data size and write threshold on the basis of the hash value sent to the corresponding data queue.
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This occurs, in part, because we are able to reuse the hash calculated to probe the Bloom filter to address the hash table and, as a consequence, hash calculations are saved.
On the other hand, a very low FPR requires a larger number of hash calculations and a high memory utilization.
These algorithms are more compute-intensive and may require many hash calculations during each lookup/store operation.
A small FPR will perform less hash calculations and use smaller data structures, whereas a high FPR will perform more hash computations and employ larger data structures.
This technique results in faster hash calculations and can be effectively applied in the BFs and HTs without affecting the asymptotic false positive probabilities [25].
The trade-off between increasing the hash calculations and the application memory footprint in order to avoid the extra cost of a false positive is complex.
The false positive ratio (FPR or f) is a key aspect for the effectiveness of a Bloom filter because it affects the memory requirements and the number of hash calculations per lookup.
The ILP optimization focused on the hashing calculations because it is the most compute intensive stage of the algorithm.
We have also proposed the reuse of one of the hashes calculated to search or store a key in the BF to address its associated HT, which avoids the calculation of another hash whenever a HT is visited.
The lack of certainty about the future makes a hash of merely prudential calculation.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com