Window Update Patterns in Stream Operators

  • Kostas Patroumpas
  • Timos Sellis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5739)


Continuous queries applied over nonterminating data streams usually specify windows in order to obtain an evolving –yet restricted– set of tuples and thus provide timely results. Among other typical variants, sliding windows are mostly employed in stream processing engines and several advanced techniques have been suggested for their incremental evaluation. In this paper, we set out to study the existence of monotonic-related semantics in windowing constructs towards a more efficient maintenance of their changing contents. We investigate update patterns observed in common window variants as well as their impact on windowed adaptations of typical operators (like selection, join or aggregation), offering more insight towards design and implementation of stream processing mechanisms. Finally, to demonstrate its significance, this framework is validated for several windowed operations against streaming datasets with simulations at diverse arrival rates and window sizes.


Query Plan Continuous Query Expiration Time Window State Window Type 
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  1. 1.
    Abadi, D.J., Carney, D., Çetintemel, U., Cherniack, M., Convey, C., Lee, S., Stonebraker, M., Tatbul, N., Zdonik, S.: Aurora: a New Model and Architecture for Data Stream Management. VLDB Journal 12(2), 120–139 (2003)CrossRefGoogle Scholar
  2. 2.
    Abadi, D.J., Ahmad, Y., Balazinska, M., Çetintemel, U., Cherniack, M., Hwang, J.-H., Lindner, W., Maskey, A.S., Rasin, A., Ryvkina, E., Tatbul, N., Xing, Y., Zdonik, S.: The Design of the Borealis Stream Processing Engine. In: CIDR (January 2005)Google Scholar
  3. 3.
    Arasu, A., Babu, S., Widom, J.: The CQL Continuous Query Language: Semantic Foundations and Query Execution. VLDB Journal 15(2), 121–142 (2006)CrossRefGoogle Scholar
  4. 4.
    Arasu, A., Cherniack, M., Galvez, E., Maier, D., Maskey, A., Ryvkina, E., Stonebraker, M., Tibbetts, R.: Linear Road: A Stream Data Management Benchmark. In: VLDB, September 2004, pp. 480–491 (2004)Google Scholar
  5. 5.
    Arasu, A., Widom, J.: Resource Sharing in Continuous Sliding-Window Aggregates. In: VLDB, September 2004, pp. 336–347 (2004)Google Scholar
  6. 6.
    Babcock, B., Babu, S., Datar, M., Motwani, R., Widom, J.: Models and Issues in Data Stream Systems. In: ACM PODS, May 2002, pp. 1–16 (2002)Google Scholar
  7. 7.
    Chandrasekaran, S., Cooper, O., Deshpande, A., Franklin, M.J., Hellerstein, J.M., Hong, W., Krishnamurthy, S., Madden, S.R., Raman, V., Reiss, F., Shah, M.A.: TelegraphCQ: Continuous Dataflow Processing for an Uncertain World. In: CIDR (January 2003)Google Scholar
  8. 8.
    Coral8 Inc. Continuous Computation Language (CCL) Reference. Documentation (2008),
  9. 9.
    Ghanem, T., Hammad, M., Mokbel, M., Aref, W., Elmagarmid, A.: Incremental Evaluation of Sliding-Window Queries over Data Streams. IEEE Transactions on Knowledge and Data Engineering 19(1), 57–72 (2007)CrossRefGoogle Scholar
  10. 10.
    Golab, L., Tamer Özsu, M.: Update-Pattern-Aware Modeling and Processing of Continuous Queries. In: ACM SIGMOD, June 2005, pp. 658–669 (2005)Google Scholar
  11. 11.
    Jain, N., Mishra, S., Srinivasan, A., Gehrke, J., Widom, J., Balakrishnan, H., Çetintemel, U., Cherniack, M., Tibbetts, R., Zdonik, S.: Towards a Streaming SQL Standard. In: VLDB, August 2008, pp. 1379–1390 (2008)Google Scholar
  12. 12.
    Johnson, T., Muthukrishnan, S., Shkapenyuk, V., Spatscheck, O.: A Heartbeat Mechanism and its Application in Gigascope. In: VLDB, September 2005, pp. 1079–1088 (2005)Google Scholar
  13. 13.
    Krämer, J., Seeger, B.: A Temporal Foundation for Continuous Queries over Data Streams. In: COMAD, January 2005, pp. 70–82 (2005)Google Scholar
  14. 14.
    Li, J., Maier, D., Tufte, K., Papadimos, V., Tucker, P.: Semantics and Evaluation Techniques for Window Aggregates in Data Streams. In: ACM SIGMOD, June 2005, pp. 311–322 (2005)Google Scholar
  15. 15.
    Oracle Inc. Complex Event Processing in the Real World. White paper (September 2007),
  16. 16.
    Patroumpas, K., Sellis, T.: Window Specification over Data Streams. In: Grust, T., Höpfner, H., Illarramendi, A., Jablonski, S., Mesiti, M., Müller, S., Patranjan, P.-L., Sattler, K.-U., Spiliopoulou, M., Wijsen, J. (eds.) EDBT 2006. LNCS, vol. 4254, pp. 445–464. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  17. 17.
    Stonebraker, M., Çetintemel, U., Zdonik, S.: The 8 Requirements of Real-Time Stream Processing. ACM SIGMOD Record 34(4), 42–47 (2005)CrossRefGoogle Scholar
  18. 18.
    StreamBase Systems. StreamSQL Guide. Documentation (2009),
  19. 19.
    Tucker, P., Maier, D., Sheard, T., Fegaras, L.: Exploiting Punctuation Semantics in Continuous Data Streams. IEEE Transactions on Knowledge and Data Engineering 15(3), 555–568 (2003)CrossRefGoogle Scholar
  20. 20.
    Tucker, P., Maier, D., Sheard, T., Stephens, P.: Using Punctuation Schemes to Characterize Strategies for Querying over Data Streams. IEEE Transactions on Knowledge and Data Engineering 19(9), 1227–1240 (2007)CrossRefGoogle Scholar

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© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Kostas Patroumpas
    • 1
  • Timos Sellis
    • 1
    • 2
  1. 1.School of Electrical and Computer EngineeringNational Technical University of AthensHellas
  2. 2.Institute for the Management of Information SystemsR.C. ”Athena”Hellas

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