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Applications involving telecommunication call data records, web pages, onlinetransactions, medical records, stock markets, climate warning systems, etc.,necessitate efficient management and processing of such massively exponentialamount of data from diverse sources. De-duplication or Intelligent Compressionin streaming scenarios for approximate identification and elimination ofduplicates from such unbounded data stream is a greater challenge given thereal-time nature of data arrival. Stable Bloom Filters (SBF) addresses thisproblem to a certain extent. .  In this work, we present several novel algorithms for the problem ofapproximate detection of duplicates in data streams. We propose the ReservoirSampling based Bloom Filter (RSBF) combining the working principle of reservoirsampling and Bloom Filters. We also present variants of the novel BiasedSampling based Bloom Filter (BSBF) based on biased sampling concepts. We alsopropose a randomized load balanced variant of the sampling Bloom Filterapproach to efficiently tackle the duplicate detection. In this work, we thusprovide a generic framework for de-duplication using Bloom Filters. Usingdetailed theoretical analysis we prove analytical bounds on the false positiverate, false negative rate and convergence rate of the proposed structures. Weexhibit that our models clearly outperform the existing methods. We alsodemonstrate empirical analysis of the structures using real-world datasets (3million records) and also with synthetic datasets (1 billion records) capturingvarious input distributions.

Advanced Bloom Filter Based Algorithms for Efficient Approximate Data De-Duplication in Streams