Content-Based Approach for Improving Bloom Filter Efficiency

Bloom filters are a type of data structure that is used to test whether or not an element member set. They known for being space-efficient and commonly employed in various applications, such as network routers, web browsers, databases. These work by allowing fixed probability incorrectly identifying the set, false positive rate (FPR). However, traditional bloom suffer from high FPR extensive memory usage, which can lead incorrect query results slow performance. Thus, this study indicates content-based strategy could be practical solution these challenges. Specifically, our approach requires less filter storage, consequently decreasing positives. The effectiveness several hash functions on strategy’s performance was also evaluated. Experimental evaluations demonstrated proposed potentially decrease positives substantial margin up 79.83%. use size-based content bits significantly contributes number well. volume rises, impact time considerably noticeable. Moreover, evidence suggests application singular leads more than 50%