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Window-Based Query Processing

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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 reevaluated 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...

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Recommended Reading

  1. Srivastava U, Widom J. Flexible time management in data stream systems. In: Proceedings of the 23rd ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems; 2004. p. 263–74.

    Google Scholar 

  2. Abadi D, Ahmad Y, Balazinska M, Cetintemel U, Cherniack M, Hwang J-H, Lindner W, Maskey AS, Rasin A, Ryvkina E, Tatbul N, Xing Y, Zdonik S. The design of the Borealis stream processing engine. In: Proceedings of the 2nd Biennial Conference on Innovative Data Systems Research; 2005. p. 277–89.

    Google Scholar 

  3. Ryvkina E, Maskey AS, Cherniack M, Zdonik S. Revision processing in a stream processing engine: a high-level design. In: Proceedings of the 22nd International Conference on Data Engineering; 2006.

    Google Scholar 

  4. Ghanem TM, Hammad MA, Mokbel MF, Aref WG, Elmagarmid AK. Incremental evaluation of sliding-window queries over data streams. IEEE Trans Knowl Data Eng. 2007;19(1):57–72.

    Article  Google Scholar 

  5. Tucker PA, Maier D, Sheard T, Fegaras L. Exploiting punctuation semantics in continuous data streams. IEEE Trans Knowl Data Eng. 2003;15(3):555–68.

    Article  Google Scholar 

  6. Chandrasekaran S, Franklin MJ. Streaming queries over streaming data. In: Proceedings of the 28th International Conference on Very Large Data Bases; 2002. p. 203–14.

    Chapter  Google Scholar 

  7. Hammad MA, Franklin MJ, Aref WG, Elmagarmid AK Scheduling for shared window joins over data streams. In: Proceedings of the 29th International Conference on Very Large Data Bases; 2003. p. 297–308.

    Chapter  Google Scholar 

  8. Jianjun C, DeWitt DJ, Feng T, Yuan W. NiagaraCQ: a scalable continuous query system for internet databases. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2000. p. 379–90.

    Google Scholar 

  9. Jianjun C, DeWitt DJ, Naughton JF. Design and evaluation of alternative selection placement strategies in optimizing continuous queries. In: Proceedings of the 18th International Conference on Data Engineering; 2002. p. 345–56.

    Google Scholar 

  10. Madden S, Shah MA, Hellerstein JM, Raman V. Continuously adaptive continuous queries over streams. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2002. p. 49–60.

    Google Scholar 

  11. The STREAM Group. STREAM: the stanford stream data manager. IEEE Data Eng Bull. 2003;26(1):19–26.

    Google Scholar 

  12. Hammad MA, Mokbel MF, Ali MH, Aref WG, Catlin AC, Elmagarmid AK, Eltabakh M, Elfeky MG, Ghanem T, Gwadera R, Ilyas IF, Marzouk M, Xiong X. Nile: a query processing engine for data streams. In: Proceedings of the 20th International Conference on Data Engineering; 2004. p. 851.

    Google Scholar 

  13. Ghanem TM, Elmagarmid AK, Larson PA, Aref WG. Supporting views in data stream management systems. ACM Transactions on Database Systems. 2010; 35(1), 1:1–1:47.

    Article  Google Scholar 

  14. Abadi D, Carney D, Cetintemel U, Cherniack M, Convey C, Lee S, Stonebraker M, Tatbul N, Zdonik S. Aurora: a new model and architecture for data stream management. VLDB J. 2003;12(2):120–39.

    Article  Google Scholar 

  15. Bai Y, Thakkar H, Luo C, Wang H, Zaniolo C. A data stream language and system designed for power and extensibility. In: Proceedings of the 15th ACM International Conference on Information and Knowledge Management; 2006. p. 337–46.

    Google Scholar 

  16. Johnson T, Muthukrishnan S, Shkapenyuk V, Spatscheck O A heartbeat mechanism and its application in gigascope. In: Proceedings of the 31st International Conference on Very Large Data Bases; 2005. p. 1079–88.

    Google Scholar 

  17. Stonebraker M, Cetintemel U, Zdonik S. The 8 requirements of real-time stream processing. ACM SIGMOD Rec. 2005;34(4):42–7.

    Article  Google Scholar 

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Correspondence to Walid G. Aref .

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Aref, W.G. (2018). Window-Based Query Processing. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_468

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