Performance Analysis of Bloom Filter for Big Data Analytics

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%.