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Reservoir sampling

Reservoir sampling is a family of randomized algorithms for randomly choosing a sample of k items from a list S containing n items, where n is either… 
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Papers overview

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2019
2019
Database systems need to be able to convert queries to efficient execution plans. As recent research has shown, correctly… 
2017
2017
We present Silverback+, a scalable probabilistic framework for accurate association rule and frequent item-set mining of large… 
2016
2016
Bayesian network (BN) learning from big datasets is potentially more valuable than learning from conventional small datasets as… 
Review
2012
Review
2012
Reservoir sampling is an interesting statistical sampling technique, developed almost 40 years ago in order to enable analysis of… 
2007
2007
In stream join processing with limited memory, uniform random sampling is useful for approximate query evaluation. In this paper… 
2006
2006
We present a simple algorithm that allows sampling from a stream of data items without knowing the number of items in advance and… 
2005
2005
Clustering is a task of grouping data based on similarity. A popular k-means algorithm groups data by firstly assigning all data…