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Streaming big data with self-adjusting computation
2013
Proceedings of the 2013 workshop on Data driven functional programming - DDFP '13
Many big data computations involve processing data that changes incrementally or dynamically over time. Using existing techniques, such computations quickly become impractical. For example, computing the frequency of words in the first ten thousand paragraphs of a publicly available Wikipedia data set in a streaming fashion using MapReduce can take as much as a full day. In this paper, we propose an approach based on self-adjusting computation that can dramatically improve the efficiency of
doi:10.1145/2429376.2429382
dblp:conf/popl/AcarC12
fatcat:bfufhf354zchjaalfnfoym6wk4