ArticleComputational intelligence and neuroscience2022
Performance Analysis of Bloom Filter for Big Data Analytics.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Characterization of microbial dark matter at scale with MetaSBT and taxonomy-aware Sequence Bloom Trees.Nature biotechnology · 2026Article
- Characterization of microbial dark matter at scale with MetaSBT and taxonomy-aware Sequence Bloom Trees.bioRxiv : the preprint server for biology · 2025Article
- EyeCNN: exploring the potential of convolutional neural networks for identification of multiple eye diseases through retinal imagery.International ophthalmology · 2023Article
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Authors and funding
4 authors.
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Abstract
The rapid rise of data value, such as social media and mobile applications, results in large volumes of data, which is what the term "big data" refers to. The increased rate of data growth makes handling big data very challenging. Despite a Bloom filter (BF) technique having previously been proposed as a space-and-time efficient probabilistic method, this proposal has not yet been evaluated in terms of big data. This study, thus, evaluates the BF technique by conducting an experimental study with a large amount of data. The results revealed that BF overcomes the efficiency not present in the space-and-time of indexing and examining big data. Moreover, to address the increase of false-positive rate in using BF with big data, a novel false-positive rate reduction approach is proposed in this paper. The initial experimental results of evaluating this method are very promising. The novel approach helped to reduce the false-positive rate by more than 70%.
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Registered trials
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