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MISO: Souping Up Big Data Query Processing with a Multistore System

Summary: Tunes multistore physical design, optimizing data placement to minimize cross-store movement. Online, adaptive, and lightweight; leverages opportunistic views from query processing to boost ad-hoc performance with minimal overhead. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
4835
Venue
SIGMOD
Year
2014
Pagerank
7.1298683e-05
Overall Rank
3,783 | 74.05%
DOI
10.1145/2588555.2588568

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{lefevre_sigmod14,
        title = {{MISO: Souping Up Big Data Query Processing with a Multistore System}},
        author = {LeFevre, Jeff and Sankaranarayanan, Jagan and Hacigumus, Hakan and Tatemura, Junichi and Polyzotis, Neoklis and Carey, Michael J.},
        series = {{SIGMOD} '14},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/2588555.2588568},
        url = {https://dl.acm.org/doi/10.1145/2588555.2588568},
        year = {2014}
}

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