Efficient Processing of Distributed Iceberg Semi-joins

Abstract

The Iceberg SemiJoin (ISJ) of two datasets $\mathcal R$ and $\mathcal S$ returns the tuples in $\mathcal R$ which join with at least k tuples of $\mathcal S$. The ISJ operator is essential in many practical applications including OLAP, Data Mining and Information Retrieval. In this paper we consider the distributed evaluation of Iceberg SemiJoins, where $\mathcal R$ and $\mathcal S$ reside on remote servers. We developed an efficient algorithm which employs Bloom filters. The novelty of our approach is that we interleave the evaluation of the Iceberg set in server $\mathcal S$ with the pruning of unmatched tuples in server $\mathcal R$. Therefore, we are able to (i) eliminate unnecessary tuples early, and (ii) extract accurate Bloom filters from the intermediate hash tables which are constructed during the generation of the Iceberg set. Compared to conventional two-phase approaches, our experiments demonstrate that our method transmits up to 80% less data through the network, while reducing the disk I/O cost.