Efficient processing of distributed iceberg semi-joins

Mohammed Kasim Imthiyaz*, Dong Xiaoan, Panos Kalnis

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


The Iceberg SemiJoin (ISJ) of two datasets R and S returns the tuples in R which join with at least k tuples of 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 R and 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 S with the pruning of unmatched tuples in server 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.

Original languageEnglish (US)
Pages (from-to)634-643
Number of pages10
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
StatePublished - 2004
Externally publishedYes

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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