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The Flajolet-Martin algorithm is a probabilistic algorithm designed to estimate the number of distinct elements in a large data stream efficiently. In the context of Big Data, where the data volume is massive and often streaming in real time, maintaining exact counts of unique elements can be computationally expensive and memory-intensive. Traditional methods like storing all elements or using hash sets become impractical due to their high memory consumption. Flajolet-Martin offers a space-efficient solution by using randomness and bit patterns to estimate cardinality with a high degree of accuracy while requiring minimal memory. The algorithm is particularly useful in domains like network traffic analysis, web analytics, database query optimization, and any scenario where the uniqueness of data items needs to be tracked in real time. The core principle behind the Flajolet-Martin algorithm is the observation that in a uniformly distributed random bit string, the position of the first 1 bit provides information about the likelihood of encountering new unique elements. Each element in the data stream is passed through a hash function, which converts the element into a pseudo-random integer. This integer is then represented in binary form, and the position of the least significant _______ __________ _______ _________ _____ _____.
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