  In this work, we present a comprehensive treatment of weighted random
sampling (WRS) over data streams. More precisely, we examine two natural
interpretations of the item weights, describe an existing algorithm for each
case ([2, 4]), discuss sampling with and without replacement and show
adaptations of the algorithms for several WRS problems and evolving data
streams.
