Before revision:  We initiate the study of classic online selection problems\dots in a streaming model where the data stream consists of two parts: historical data points that an algorithm can use to learn something about the input; and data points from which a selection can be made. Both types of data points are i.i.d. draws from an unknown distribution .
After revision:  We initiate the study of classic online selection problems\dots in a streaming model where the data available. We finally establish that the upper bound of 1/e for the case without samples is robust to additional information about the distribution, and applies also to sequences of i.i.d. draws from an unknown distribution .