Efficient Parallel Set-Similarity Joins Using MapReduce
Summary: 3-stage end-to-end parallel set-similarity joins on MapReduce. Balanced partitioning with minimal replication, memory-aware processing for self- and R-S joins, and out-of-core strategies when data exceed node memory, with Hadoop-based speedup/scaleup results. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Rares Vernica (University of California Irvine)
- 2. Michael J. Carey (University of California Irvine)
- 3. Chen Li (University of California Irvine)
BibTeX Citation
@inproceedings{vernica_sigmod10,
title = {{Efficient Parallel Set-Similarity Joins Using MapReduce}},
author = {Vernica, Rares and Carey, Michael J. and Li, Chen},
series = {{SIGMOD} '10},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/1807167.1807222},
url = {https://dl.acm.org/doi/10.1145/1807167.1807222},
year = {2010}
}
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