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We now have a second generation of algorithmic tools for analyzing data streams, that go beyond the initial tools for summarizing a single stream in small space. The new tools deal with distributed data, stochastic models, dynamic graph and matrix objects and others; they optimize communication, number of parallel rounds and privacy among other things. I will provide an overview of these tools and explore their application to Machine Learning problems.

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