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Performance Analysis of Bloom Filter for Big Data Analytics.

Suliman A Alsuhibany1, Mohammed Alsuhaibani1, Rehan Ullah Khan2

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This study evaluates Bloom filters (BF) for big data challenges. Results show BF improves space-and-time efficiency, and a new method significantly reduces false positives.

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Area of Science:

  • Computer Science
  • Data Science
  • Information Retrieval

Background:

  • The exponential growth of data from sources like social media and mobile apps defines "big data."
  • Handling big data presents significant space-and-time efficiency challenges.
  • Bloom filters (BF) are known for space-and-time efficiency but lack evaluation in big data contexts.

Purpose of the Study:

  • To experimentally evaluate the effectiveness of Bloom filters (BF) for big data indexing and examination.
  • To propose and evaluate a novel approach for reducing the false-positive rate associated with BF in big data scenarios.

Main Methods:

  • Conducted an experimental study using large datasets to assess Bloom filter performance.
  • Developed and tested a new method specifically designed to mitigate the false-positive rate of Bloom filters when applied to big data.

Main Results:

  • Bloom filters demonstrated superior space-and-time efficiency for indexing and examining big data compared to existing methods.
  • The novel false-positive rate reduction approach achieved a reduction of over 70%.

Conclusions:

  • Bloom filters are a viable and efficient technique for managing big data.
  • The proposed novel approach effectively addresses the challenge of high false-positive rates in big data applications of Bloom filters.