English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Bloom Filter is an important probabilistic data structure to reduce memoryconsumption for membership filters. It is applied in diverse domains such asComputer Networking, Network Security and Privacy, IoT, Edge Computing, CloudComputing, Big Data, and Biometrics. But Bloom Filter has an issue of the falsepositive probability. To address this issue, we propose a novel robust BloomFilter, robustBF for short. robustBF is a 2D Bloom Filter, capable of filteringmillions of data with high accuracy without compromising the performance. Ourproposed system is presented in two-fold. Firstly, we modify the murmur hashfunction, and test all modified hash functions for improvements and select thebest-modified hash function experimentally. Secondly, we embed the modifiedhash functions in 2D Bloom Filter. Our experimental results show that robustBFis better than standard Bloom Filter and counting Bloom Filter in every aspect.robustBF exhibits nearly zero false positive probability with more than$10\times$ and $44\times$ lower memory consumption than standard Bloom filterand counting Bloom Filter, respectively.

RobustBF: A High Accuracy and Memory Efficient 2D Bloom Filter