Skip to main content

Window-based Query Processing

  • Reference work entry

Synonyms

Stream query processing

Definition

Data Streams are infinite in nature. As a result, a query that executes over data streams specifies a “window” of focus or the part of the data stream that is of interest to the query. When new data items arrive into the data stream, the window may either expand or slide to allow the query to process these new data items. Hence, queries over data streams are continuous in nature, i.e., the query is continuously re-evaluated each time the query window slides. Window-based query processing on data streams refers to the various ways and techniques for processing and evaluating continuous queries over windows of data stream items.

Historical Background

Windows over relational tables have already been introduced into Standard SQL (SQL:1999) in order to support data analysis, decision support, and more generally, OLAP-type operations.

However, the motivation for having windows in data stream management systems is quite different. Since data streams...

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   2,500.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Recommended Reading

  1. Abadi D., Ahmad Y., Balazinska M., Cetintemel U., Cherniack M., Hwang J-H., Lindner W., Maskey A.S., Rasin A., Ryvkina E., Tatbul N., Xing Y., and Zdonik S. The design of the Borealis stream processing engine. In Proc. 2nd Biennial Conf. on Innovative Data Systems Research, pp. 277–289.2005,

    Google Scholar 

  2. Abadi D., Carney D., Cetintemel U., Cherniack M., Convey C., Lee S., Stonebraker M., Tatbul N., and Zdonik S. Aurora: a new model and architecture for data stream management. VLDB J., 12(2):120–139, 2003.

    Article  Google Scholar 

  3. Bai Y., Thakkar H., Luo C., Wang H., and Zaniolo C. A data stream language and system designed for power and extensibility. In Proc. Int. Conf. on Information and Knowledge Management, pp. 337–346.2006,

    Google Scholar 

  4. Chandrasekaran S. and Franklin M.J. Streaming queries over streaming data. In Proc. 28th Int. Conf. on Very Large Data Bases, pp. 203–214.2002,

    Google Scholar 

  5. Ghanem T.M. Supporting Views in Data Stream Management Systems. Ph.D. Dissertation. Department of Computer Science, Purdue University, 2007.

    Google Scholar 

  6. Ghanem T.M., Hammad M.A., Mokbel M.F., Aref W.G., and Elmagarmid A.K. Incremental evaluation of sliding-window queries over data streams. IEEE Trans. Knowl. Data Eng., 19(1): 57–72, 2007.

    Article  Google Scholar 

  7. Hammad M.A., Franklin M.J., Aref W.G., and Elmagarmid A.K. Scheduling for shared window joins over data streams. In Proc. 29th Int. Conf. on Very Large Data Bases, pp. 297–308.2003,

    Google Scholar 

  8. Hammad M.A., Mokbel M.F., Ali M.H., Aref W.G., Catlin A.C., Elmagarmid A.K., Eltabakh M., Elfeky M.G., Ghanem T., Gwadera R., Ilyas I.F., Marzouk M., and Xiong X. Nile: a query processing engine for data streams. In Proc. 20th Int. Conf. on Data Engineering, p. 851.2004,

    Google Scholar 

  9. Jianjun C., DeWitt D.J., Feng T., and Yuan W. NiagaraCQ: a scalable continuous query system for internet databases. In Proc. ACM SIGMOD Int. Conf. on Management of Data, pp. 379–390.2000,

    Google Scholar 

  10. Jianjun C., DeWitt D.J., and Naughton J.F. Design and evaluation of alternative selection placement strategies in optimizing continuous queries. In Proc. 18th Int. Conf. on Data Engineering, pp. 345–356.2002,

    Google Scholar 

  11. Johnson T., Muthukrishnan S., Shkapenyuk V., and Spatscheck O. A heartbeat mechanism and its application in gigascope. In Proc. 31st Int. Conf. on Very Large Data Bases, pp. 1079–1088.2005,

    Google Scholar 

  12. Madden S., Shah M.A., Hellerstein J.M., and Raman V. Continuously adaptive continuous queries over streams. In Proc. ACM SIGMOD Int. Conf. on Management of Data, pp. 49–60.2002,

    Google Scholar 

  13. Ryvkina E., Maskey A.S., Cherniack M., and Zdonik S. Revision processing in a stream processing engine: a high-level design. In Proc. 22nd Int. Conf. on Data Engineering, 2006.

    Google Scholar 

  14. Srivastava U. and Widom J. Flexible time management in data stream systems. In Proc. 23rd ACM SIGACT-SIGMOD-SIGART Symp. on Principles of Database Systems, pp. 263–274.2004,

    Google Scholar 

  15. Stonebraker M., Cetintemel U., and Zdonik S. The 8 requirements of real-time stream processing. ACM SIGMOD Rec., 34(4):42–47, 2005.

    Article  Google Scholar 

  16. The STREAM Group. STREAM: the Stanford stream data manager. IEEE Data Eng. Bull., 26(1):19–26, 2003.

    Google Scholar 

  17. Tucker P.A., Maier D., Sheard T., and Fegaras L. Exploiting punctuation semantics in continuous data streams. IEEE Trans. Knowl. Data Eng., 15(3):555–568, 2003.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer Science+Business Media, LLC

About this entry

Cite this entry

Aref, W.G. (2009). Window-based Query Processing. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_468

Download citation

Publish with us

Policies and ethics