We study the problem of extracting a small subset of representative items from a large data stream. In many data mining and machine learning applications, such as social network analysis and recommender systems, this problem is formulated as maximizing a monotone submodular function subject to a cardinality constraint $k$. In this work, we consider a setting where data items in the stream belong to one of several disjoint groups and investigate the optimization problem with an additional emph{fairness constraint} that limits the selection to a given number of items from each group. We propose efficient algorithms for this fairness-aware variant of the streaming submodular maximization problem. In particular, we first give a $ (frac{1}{2}-varepsilon) $-approximation algorithm that requires $ O(frac{1}{varepsilon} cdot log

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