Sampling from a moving window over streaming data
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
On biased reservoir sampling in the presence of stream evolution
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
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Reservoir sampling is an interesting statistical sampling technique, developed almost 40 years ago in order to enable analysis of large scale data (for that time) while utilizing limited computer memory resources. We present an overview of frequently used reservoir sampling techniques and discuss how they can be used for learning from data streams. While they are not perfect for all scenarios, they can easily be modified for many purpose, and also find place in surprisingly useful modern data analysis approaches.