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Recurring job optimization in scope

Published: 20 May 2012 Publication History

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References

[1]
R. Chaiken, B. Jenkins, P.-Å. Larson, B. Ramsey, D. Shakib, S. Weaver, and J. Zhou. SCOPE: Easy and efficient parallel processing of massive data sets. In Proceedings of VLDB Conference, 2008.
[2]
J. Dean and S. Ghemawat. MapReduce: Simplified data processing on large clusters. In Proceedings of OSDI Conference, 2004.
[3]
J. Zhou, P.-Å. Larson, and R. Chaiken. Incorporating partitioning and parallel plans into the SCOPE optimizer. In Proceedings of ICDE Conference, 2010.

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    cover image ACM Conferences
    SIGMOD '12: Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
    May 2012
    886 pages
    ISBN:9781450312479
    DOI:10.1145/2213836

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 20 May 2012

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    Author Tags

    1. distributed computation
    2. query optimization
    3. recurring jobs
    4. scope
    5. statistics

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    SIGMOD '12 Paper Acceptance Rate 48 of 289 submissions, 17%;
    Overall Acceptance Rate 785 of 4,003 submissions, 20%

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    Cited By

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    • (2023)Runtime Variation in Big Data AnalyticsProceedings of the ACM on Management of Data10.1145/35889211:1(1-20)Online publication date: 30-May-2023
    • (2021)Procedural extensions of SQLProceedings of the VLDB Endowment10.14778/3457390.345740214:8(1378-1391)Online publication date: 21-Oct-2021
    • (2021)KEA: Tuning an Exabyte-Scale Data InfrastructureProceedings of the 2021 International Conference on Management of Data10.1145/3448016.3457569(2667-2680)Online publication date: 9-Jun-2021
    • (2020)Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our FindingsProceedings of the 2020 ACM SIGMOD International Conference on Management of Data10.1145/3318464.3380584(99-113)Online publication date: 11-Jun-2020
    • (2018)Selecting subexpressions to materialize at datacenter scaleProceedings of the VLDB Endowment10.14778/3192965.319297111:7(800-812)Online publication date: 1-Mar-2018
    • (2018)JanusJournal of Parallel and Distributed Computing10.1016/j.jpdc.2018.02.030120:C(196-210)Online publication date: 1-Oct-2018
    • (2018)Using machine learning to optimize parallelism in big data applicationsFuture Generation Computer Systems10.1016/j.future.2017.07.00386:C(1076-1092)Online publication date: 1-Sep-2018
    • (2016)Dynamic Reconfiguration of Data Parallel Programs2016 28th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD)10.1109/SBAC-PAD.2016.32(190-197)Online publication date: Oct-2016
    • (2016)Continuously Improving the Resource Utilization of Iterative Parallel Dataflows2016 IEEE 36th International Conference on Distributed Computing Systems Workshops (ICDCSW)10.1109/ICDCSW.2016.20(1-6)Online publication date: Jun-2016
    • (2015)PixidaProceedings of the VLDB Endowment10.14778/2850578.28505829:2(72-83)Online publication date: 1-Oct-2015
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