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Streaming problems are algorithmic problems that are mainly characterized by their massive input streams. Because of these data streams, the algorithms for these problems are forced to be space-efficient, as the input stream length generally exceeds the available storage. The goal of this study is to analyze the impact of additional information (more specifically, a hypothesis of the solution) on the algorithmic space complexities of several streaming problems. To this end, different streaming problems are analyzed and compared. The two problems "most frequent item" and "number of distinct...