Skip to main content

Load Shedding for Window Joins over Streams

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4016))

Abstract

We present a novel load shedding technique over sliding window joins. We first construct a dual window architectural model including join-windows and aux-windows. With the statistics built on aux-windows, an effective load shedding strategy is developed to produce maximum subset join outputs. For the streams with high arrival rates, we propose an approach incorporating front-shedding and rear-shedding, and then address the problem of how to cooperate these two shedding processes through a series of calculations. Based on extensive experimentation with synthetic data and real life data, we show that our load shedding strategy delivers superb join output performance, and dominates the existing strategies.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Kang, J., Naughton, J.F., Viglas, S.D.: Evaluating Window Joins over Unbounded Streams. In: Proc. 2003 Intl. Conf. on Data Engineering (March 2003)

    Google Scholar 

  2. The STREAM Group: STREAM: The Stanford Stream Data Manager. IEEE Data Engineering Bulletin 26(1), 19–26 (2003)

    Google Scholar 

  3. Ayad, A.M., Naughton, J.F.: Static Optimization of Conjunctive Queries with Sliding Windows Over Infinite Streams. In: Proc. ACM SIGMOD Conf. (June 2004)

    Google Scholar 

  4. Das, A., Gehrke, J., Riedewald, M.: Approximate Join Processing Over Data Streams. In: Proc. 2003 ACM SIGMOD Conf. (June 2003)

    Google Scholar 

  5. Srivastava, U., Widom, J.: Memory-Limited Execution of Windowed Stream Joins. In: Proc. 30th Int. Conf. on Very Large Data Bases (2004)

    Google Scholar 

  6. Abadi, D., Carney, D., et al.: Aurora: a new model and architecture for data stream management. VLDB Journal 12(2), 120–139 (2003)

    Article  Google Scholar 

  7. Chen, J., DeWitt, D.J., Tian, F., Wang, Y.: NiagaraCQ: A scalable continous query system for internet databasses. In: Proc. ACM SIGMOD Int. Conf. on Management of Data, pp. 379–390 (2000)

    Google Scholar 

  8. Fenwich, P.M.: A New Data Structure for Cumulative Frequency Tables. Software - Practice and Experience 24(3), 327–336 (1994)

    Article  Google Scholar 

  9. Hellerstein, J.M., Franklin, M.J., Chandrasekaran, S., et al.: Adaptive query processing: Technology in evolution. IEEE data Engineering Bulletin 23(2), 7–18 (2000)

    Google Scholar 

  10. Baldocchi, D., Wilson, K., et al.: Half-Hourly Measurements of CO2, Water Vapor, and Energy Exchange Using the Eddy Covariance Technique from Walker Branch Watershed, Tennessee (1995-1998), http://cdiac.esd.ornl.gov/ftp/ameriflux/data/us-sites/walker-branch/

  11. Babcock, B., Babu, S., Datar, M., Motwani, R., Widom, J.: Models and issues in data stream systems. In: Proc. Principles of Database Systems (PODS) (June 2002)

    Google Scholar 

  12. Golab, L., Ozmu, M.T.: Processing Sliding Window Multi-joins in Continuous Queries over Data Streams. In: Proc. Conf. on Very Large Databases (September 2003)

    Google Scholar 

  13. Viglas, S.D., Naughton, J.F., Burger, J.: Maximizing the Output Rate of Multi- Way Join Queries over Streaming Information Sources. In: Proc. Int. Conf. on Very Large Databases (VLDB) (September 2003)

    Google Scholar 

  14. Han, D., Zhou, R., Xiao, C.: Load shedding for Window Joins over Data Streams, Technical report, Northeastern University (June 2004), http://mitt.neu.edu.cn/publications/HZX05-Joins.pdf

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Han, D., Xiao, C., Zhou, R., Wang, G., Huo, H., Hui, X. (2006). Load Shedding for Window Joins over Streams. In: Yu, J.X., Kitsuregawa, M., Leong, H.V. (eds) Advances in Web-Age Information Management. WAIM 2006. Lecture Notes in Computer Science, vol 4016. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11775300_40

Download citation

  • DOI: https://doi.org/10.1007/11775300_40

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-35225-9

  • Online ISBN: 978-3-540-35226-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics