全对MapReduce问题的匹配边界

F. Afrati, J. Ullman
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引用次数: 9

摘要

全对问题是一种输入-输出关系,其中每个输出对应一对输入,而每对输入都有相应的输出。它对相似性连接进行建模,其中不可能简化对相似对的搜索,例如,对位置敏感的散列,并且必须将每个输入与每个其他输入进行比较,以确定那些“相似”的对。当由MapReduce算法实现时,在必要通信的下限与最知名算法所需的通信之间存在2倍的差距。在这篇简短的文章中,我们证明了下界基本上是可以满足的。
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Matching bounds for the all-pairs MapReduce problem
The all-pairs problem is an input-output relationship where each output corresponds to a pair of inputs, and each pair of inputs has a corresponding output. It models similarity joins where no simplification of the search for similar pairs, e.g., locality-sensitive hashing, is possible, and each input must be compared with every other input to determine those pairs that are "similar." When implemented by a MapReduce algorithm, there was a gap, a factor of 2, between the lower bound on necessary communication and the communication required by the best known algorithm. In this brief paper we show that the lower bound can essentially be met.
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