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Stream Models

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Encyclopedia of Database Systems
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Definition

Conceptually, a data stream is a sequence of data items that collectively describe one or more underlying signals. For instance, a network traffic stream describes the type and volume of data transmitted among nodes in the network; one possible signal is a mapping between pairs of source and destination IP addresses to the number of bytes transmitted from the given source to the given destination. A stream model explains how to reconstruct the underlying signals from individual stream items. Thus, understanding the model is a prerequisite for stream processing and stream mining. In particular, the computational complexity of a data stream problem often depends on the complexity of the model that describes the input.

Historical Background

The stream models discussed in this article were introduced in [3] and extended in [7,8]. In addition to modeling a stream with respect to its underlying signal(s), there exist the following two related concepts. First, the stream...

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Recommended Reading

  1. Arasu A., Babu S., and Widom J. The CQL continuous query language: semantic foundations and query execution. VLDB J., 15(2):121–142, 2006.

    Google Scholar 

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

    Google Scholar 

  3. Gilbert A., Kotidis Y., Muthukrishnan S., and Strauss M. Surfing wavelets on streams: one-pass summaries for approximate aggregate queries. In Proc. 27th Int. Conf. on Very Large Data Bases, 2001, pp. 79–88.

    Google Scholar 

  4. Girod L., Mei Y., Newton R., Rost S., Thiagarajan A., Balakrishnan H. and Madden S. The case for a signal-oriented data stream management system. In Proc. 3rd Biennial Conf. on Innovative Data Systems Research, 2007, pp. 397–406.

    Google Scholar 

  5. Golab L. and Özsu M.T. Update-pattern aware modeling and processing of continuous queries. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 2005, pp. 658–669.

    Google Scholar 

  6. Henzinger M., Raghavan P., and Rajagopalan S. Computing on data streams. DIMACS Ser. Discrete Math. Theor. Comput. Sci., 50:107–118, 1999.

    MathSciNet  Google Scholar 

  7. Hoffmann M., Muthukrishnan S., and Raman R. Streaming algorithms for data in motion. ESCAPE. Springer, Berlin Hiedelberg New York, 2007, pp. 294–304.

    Google Scholar 

  8. Muthukrishnan S. Data streams: algorithms and applications. Found. Trends Theor. Comput. Sci., 1(2):1–67, 2005.

    MathSciNet  Google Scholar 

  9. Paxson V. and Floyd S. Wide-area traffic: the failure of Poisson modeling. IEEE/ACM Trans. Netw., 3(3):226–244, 1995.

    Google Scholar 

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Golab, L. (2009). Stream Models. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_370

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