skip to main content
10.1145/2505515.2505736acmconferencesArticle/Chapter ViewAbstractPublication PagescikmConference Proceedingsconference-collections
research-article

Query execution timing: taming real-time anytime queries on multicore processors

Published: 27 October 2013 Publication History

Abstract

Answering real-time queries, especially over probabilistic data, is becoming increasingly important for service providers. We study anytime query processing algorithms, and extend the traditional query execution plan with a timing component. Our focus is how to determine this timing component, given the queries' deadline constraints. We consider the common multicore processors. Specifically, we propose two query optimization modes: offline periodic optimization and online optimization. We devise efficient algorithms for both offline and online cases followed by a competitive analysis to show the power of our online optimization. Finally, we perform a systematic experimental evaluation using real-world datasets to verify our approaches.

References

[1]
S. Albers, et al. Online Algorithms. ACM Comp. Surveys, 1999.
[2]
Aydin, H., Melhem, R., Mosse, D., Mejfa-Alvarez, P. Optimal reward-based scheduling of periodic real-time tasks. In RTSS, 1999.
[3]
A. Bar-Noy, et al. A unified approach to approximating resource allocation and scheduling. In STOC 2000.
[4]
O. Benjelloun et al. Databases with uncertainty & lineage. VLDB'08.
[5]
R. Caflisch. Monte Carlo and quasi-Monte Carlo methods, Acta Numerica vol. 7, Cambridge University Press, 1998.
[6]
N. Dalvi, D. Suciu. Efficient query evaluation on probabilistic databases. In VLDB, 2004.
[7]
H. Fujiwara, K. Iwama. Average-Case Competitive Analysis for Ski-Rental Problems. In ISAAC, 2002.
[8]
T. Ge, S. Zdonik. Handling Uncertain Data in Array Database Systems. In ICDE, 2008.
[9]
P. Haas, et al. Online Query Processing: A Tutorial. SIGMOD, 2001.
[10]
I-Hong Hou, P. R. Kumar. Scheduling Periodic Real-Time Tasks with Heterogeneous Reward Requirements. In RTSS, 2011.
[11]
R. Jampani, F. Xu, M. Wu, L. Perez, C. Jermaine, P. Haas. MCDB: a Monte Carlo approach to managing uncertain data. SIGMOD, 2008.
[12]
R. Karp, M. Luby. Monte-Carlo Algorithms for Enumeration and Reliability Problems. In SFCS, 1983.
[13]
E. Koutsoupias, et al. Beyond Competitive Analysis. In FOCS, 1994.
[14]
T. Tran, L. Peng, B. Li, Y. Diao, A. Liu. PODS: A New Model and Processing Algorithms for Uncertain Data Streams. SIGMOD, 2010.
[15]
S. Zilberstein. Using Anytime Algorithms in Intelligent Systems. In AI Magazine, 17(3):73--83, 1996.
[16]
http://www.vacommunity.org/VAST+Challenge+2012
[17]
http://www.nws.noaa.gov/ndfd/anonymous_ftp.htm

Index Terms

  1. Query execution timing: taming real-time anytime queries on multicore processors

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management
    October 2013
    2612 pages
    ISBN:9781450322638
    DOI:10.1145/2505515
    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]

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 27 October 2013

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. multicore processor
    2. online algorithm
    3. real-time query

    Qualifiers

    • Research-article

    Conference

    CIKM'13
    Sponsor:
    CIKM'13: 22nd ACM International Conference on Information and Knowledge Management
    October 27 - November 1, 2013
    California, San Francisco, USA

    Acceptance Rates

    CIKM '13 Paper Acceptance Rate 143 of 848 submissions, 17%;
    Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

    Upcoming Conference

    CIKM '25

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 109
      Total Downloads
    • Downloads (Last 12 months)3
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 17 Feb 2025

    Other Metrics

    Citations

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media