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Efficient parallel set-similarity joins using MapReduce

Published: 06 June 2010 Publication History

Abstract

In this paper we study how to efficiently perform set-similarity joins in parallel using the popular MapReduce framework. We propose a 3-stage approach for end-to-end set-similarity joins. We take as input a set of records and output a set of joined records based on a set-similarity condition. We efficiently partition the data across nodes in order to balance the workload and minimize the need for replication. We study both self-join and R-S join cases, and show how to carefully control the amount of data kept in main memory on each node. We also propose solutions for the case where, even if we use the most fine-grained partitioning, the data still does not fit in the main memory of a node. We report results from extensive experiments on real datasets, synthetically increased in size, to evaluate the speedup and scaleup properties of the proposed algorithms using Hadoop.

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cover image ACM Conferences
SIGMOD '10: Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
June 2010
1286 pages
ISBN:9781450300322
DOI:10.1145/1807167
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 06 June 2010

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  1. mapreduce
  2. set-similarity join

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SIGMOD/PODS '10
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SIGMOD/PODS '10: International Conference on Management of Data
June 6 - 10, 2010
Indiana, Indianapolis, USA

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