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- W2963940951 abstract "Similarity joins are a fundamental database operation. Given data sets S and R, the goal of a similarity join is to find all points x ∈ S and y ∈ R with distance at most r. Recent research has investigated how locality-sensitive hashing (LSH) can be used for similarity join, and in particular two recent lines of work have made exciting progress on LSH-based join performance. Hu, Tao, and Yi (PODS 17) investigated joins in a massively parallel setting, showing strong results that adapt to the size of the output. Meanwhile, Ahle, Aumüller, and Pagh (SODA 17) showed a sequential algorithm that adapts to the structure of the data, matching classic bounds in the worst case but improving them significantly on more structured data." @default.
- W2963940951 created "2019-07-30" @default.
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- W2963940951 date "2018-06-15" @default.
- W2963940951 modified "2023-09-27" @default.
- W2963940951 title "Adaptive MapReduce Similarity Joins" @default.
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- W2963940951 doi "https://doi.org/10.1145/3206333.3206340" @default.
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