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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 items", with many configurations of different result accuracies and probabilities, are deeply studied. Both lower and upper bounds for the space and time complexity for deterministic and probabilistic environments are analyzed with respect to possible improvements due to additional information. The general solution search problem is compared to the decision problem where a solution hypothesis has to be satisfied.

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Raffael Buff, born in 1990, holds a BSc and a MSc in Computer Science from the Swiss Federal Institute of Technology/ETH Zürich. His studies focused on topics such as Machine Learning, Big Data, Approximation Algorithms, Software Engineering and Distributed Systems.
While studying, the author served as a project manager at ETH juniors - a student run consulting agency at ETH Zürich - from 2010 to 2012 and was head of the department IT at ETH juniors from 2011 to 2012. He holds the certificates ITIL Foundation and HERMES Advanced. On a sidenote, he also was Swiss Champion in Online Sudoku in 2006.
Currently, the author works as a Business Consultant for an ICT company in Zürich, Switzerland.

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