Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Stream Models

  • Lukasz Golab
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_370

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. 1.
    Arasu A, Babu S, Widom J. The CQL continuous query language: semantic foundations and query execution. VLDB J. 2006;15(2):121–42.CrossRefGoogle Scholar
  2. 2.
    Ghanem T, Hammad M, Mokbel M, Aref W, Elmagarmid A. Incremental evaluation of sliding-window queries over data streams. IEEE Trans Knowl Data Eng. 2007;19(1):57–72.CrossRefGoogle Scholar
  3. 3.
    Gilbert A, Kotidis Y, Muthukrishnan S, Strauss M. Surfing wavelets on streams: one-pass summaries for approximate aggregate queries. In: Proceedings of the 27th International Conference on Very Large Data Bases; 2001. p. 79–88.Google Scholar
  4. 4.
    Girod L, Mei Y, Newton R, Rost S, Thiagarajan A, Balakrishnan H, Madden S. The case for a signal-oriented data stream management system. In: Proceedings of the 3rd Biennial Conference on Innovative Data Systems Research; 2007. p. 397–406.Google Scholar
  5. 5.
    Golab L, Özsu MT. Update-pattern aware modeling and processing of continuous queries. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2005. p. 658–69.Google Scholar
  6. 6.
    Henzinger M, Raghavan P, Rajagopalan S. Computing on data streams. DIMACS Ser Discret Math Theor Comput Sci. 1999;50:107–18.MathSciNetzbMATHCrossRefGoogle Scholar
  7. 7.
    Hoffmann M, Muthukrishnan S, Raman R. Streaming algorithms for data in motion. ESCAPE. Berlin: Springer; 2007. p. 294–304.zbMATHGoogle Scholar
  8. 8.
    Muthukrishnan S. Data streams: algorithms and applications. Found Trends Theor Comput Sci. 2005;1(2):1–67.MathSciNetzbMATHCrossRefGoogle Scholar
  9. 9.
    Paxson V, Floyd S. Wide-area traffic: the failure of poison modeling. IEEE/ACM Trans Netw. 1995;3(3):226–44.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of WaterlooWaterlooCanada

Section editors and affiliations

  • Divesh Srivastava
    • 1
  1. 1.AT&T Labs-ResearchBedminsterUSA