Description:
<jats:p>In this paper, we propose a novel method for distributing the distance computations of record pairs generated by a blocking mechanism to the reduce tasks of a Map/Reduce system. The proposed solutions in the literature analyze the blocks and then construct a profile, which contains the number of record pairs in each block. However, this deterministic process, including all its variants, might incur considerable overhead given massive data sets. In contrast, our method utilizes two Map/Reduce jobs where the first job formulates the record pairs while the second job distributes these pairs to the reduce tasks, which perform the distance computations, using repetitive allocation rounds. In each such round, we utilize all the available reduce tasks on a random basis by generating permutations of their indexes. A series of experiments demonstrate an almost-equal distribution of the record pairs, or equivalently of the distance computations, to the reduce tasks, which makes our method a simple, yet efficient, solution for applying a blocking mechanism given massive data sets.</jats:p>