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Probability Algorithms in Big Data: BloomFilter and Count-min Sketch Applications

The article explains how space‑efficient probabilistic structures such as BloomFilter and Count‑min Sketch enable large‑scale data deduplication, join pruning, real‑time idempotent filtering, and approximate top‑K analytics by trading modest accuracy loss for dramatically reduced storage and faster computation.

Big DataBloomFilterCount-Min Sketch
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Probability Algorithms in Big Data: BloomFilter and Count-min Sketch Applications