Max-cover in map-reduce

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FastText.zip: Compressing text classification models (2016, background): “Standard greedy approaches require the storing of an inverted index or to do multiple passes over the dataset, which is prohibitive on very large dataset (Chierichetti et al., 2010).”
Lazier Than Lazy Greedy (2014, background): “In particular, Chierichetti, Kumar, and Tomkins (2010) and Blelloch, Peng, and Tangwongsan (2011) addressed a particular instance of submodular functions, namely, maximum coverage and provided a distributed method with a constant factor approximation to the centralized algorithm.”
Scalable K-Means++ (2012, methodology): “To aid in the formal analysis of MapReduce algorithms, Karloﬀ et al. [24] introduced a model of computation for MapReduce, which has since been used to reason about algorithms for set cover [11], graph problems [27], and other clustering formulations [15].”
Streaming submodular maximization: massive data summarization on the fly (2014, methodology): “In particular, Chierichetti et al. [6] and later Blelloch et al. [5] addressed the MAX-COVER problem and provided a constant approximation to the centralized algorithm.”