Random sampling algorithms for sliding windows over data streams

There are growing interests in algorithms over data streams recently. This paper introduces the problem of sampling from sliding windows of recent data items from data streams and presents two random sampling algorithms for this problem. The first algorithm is a basic window-based sampling algorithm (BWRS Algorithm) for count-based sliding window. BWRS algorithm extends classic reservoir sampling to deal with the expiration of data elements from count-based sliding window, and can avoid drawbacks of classic reservoir sampling. The second algorithm is a stratified multistage sampling algorithm for time-based sliding window (SMS Algorithm). The SMS algorithm takes different sampling fraction in different strata from time-based sliding window, and works even when the number of data items in the sliding window varies dynamically over time. The theoretic analysis and experiments show that the algorithms are effective and efficient for continuous data streams processing.