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Reservoir Sampling

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Reservoir Sampling is a crucial algorithmic technique for selecting a random sample of 'k' items from a large or infinite list. Developed by Jeffery Vitter in 1985, it's ideal for big data and stream processing, ensuring each item has an equal chance of selection. This method is widely used in network analysis, big data analytics, and more, offering unbiased sampling and memory efficiency.

Introduction to Reservoir Sampling

Reservoir Sampling is an algorithmic technique in computer science that is essential for selecting a random sample of 'k' items from a sizeable or potentially infinite list 'S' of 'n' items, where 'n' may be unknown or impractically large to process in a traditional manner. Developed by Jeffery Vitter in 1985, this algorithm is particularly beneficial for big data and stream processing, as it enables the efficient processing of datasets that exceed the capacity of available memory. Its significance is highlighted by its ability to improve algorithmic efficiency, thereby enhancing the capability to solve problems that involve large-scale data.
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The Principles of Reservoir Sampling

Reservoir Sampling is based on a straightforward principle. It starts by initializing a 'reservoir'—a data structure such as an array—with the first 'k' items from the data source. For each new item encountered, the algorithm generates a random integer 'j' within the range of 0 to the index of the current item. If 'j' falls within the range of the reservoir size 'k', the 'j'-th item in the reservoir is replaced with the new item. This procedure guarantees that every item in the data source has an equal probability of being included in the sample, thus ensuring a representative sample of the entire dataset.

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Reservoir Sampling: Algorithm Year of Development

Developed in 1985 by Jeffery Vitter.

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Reservoir Sampling: Applicability to Data Types

Ideal for big data and stream processing.

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Reservoir Sampling: Memory Efficiency

Enables processing of datasets larger than available memory.

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