The model of streaming algorithms is motivated by the increasingly common situation in which the sheer amount of available data limits the ways in which the data can be accessed. Streaming algorithms are typically allowed a single pass over the data and can only store a sublinear fraction of the data at any time. We initiate the study of classic online selection problems 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. We consider the two canonical objectives for online selection---maximizing the probability of selecting the maximum and maximizing the expected value of the selection---and provide the first performance guarantees for both these objectives in the streaming model.

中文翻译：

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在线选择问题的流算法

流算法模型受到越来越普遍的情况的启发，在这种情况下，可用数据的绝对数量限制了访问数据的方式。流算法通常只允许对数据进行一次传递，并且在任何时候都只能存储数据的亚线性部分。我们开始研究流模型中经典的在线选择问题，其中数据流由两部分组成：算法可以用来了解输入的历史数据点；以及可以从中进行选择的数据点。两种类型的数据点都是来自未知分布的 iid 绘制。