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Optimization for iterative queries on MapReduce

Summary: OptIQ optimizes iterative MapReduce queries by partitioning into invariant and variant views and materializing the invariant part. It reuses the invariant and incrementally evaluates the variant view, skipping converged tuples for up to 5x speedups on PageRank and k-means. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11090
Venue
VLDB
Year
2014
Pagerank
5.6841364e-05
Overall Rank
7,168 | 50.83%
DOI
10.14778/2732279.2732286

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{onizuka_vldb14,
        title = {{Optimization for iterative queries on MapReduce}},
        author = {Onizuka, Makoto and Kato, Hiroyuki and Hidaka, Soichiro and Nakano, Keisuke and Hu, Zhenjiang},
        journal = {PVLDB},
        series = {{VLDB} '14},
        volume = {7},
        number = {4},
        pages = {241--252},
        doi = {10.14778/2732279.2732286},
        url = {https://doi.org/10.14778/2732279.2732286},
        year = {2014}
}

Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank Citing Paper Year Venue Pagerank
3,008 Scalable Big Graph Processing in MapReduce 2014 SIGMOD 7.8578871e-05
4,078 Asynchronous and Fault-Tolerant Recursive Datalog Evaluation in Shared-Nothing Engines 2015 VLDB 6.9209348e-05
5,991 Iterative Query Processing based on Unified Optimization Techniques 2019 SIGMOD 6.0171458e-05
10,690 Dynamic Pruning for Recursive Joins 2025 SIGMOD 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 12 of 12 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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