Skip to main content
SpringerLink
Log in

Definition of Data Streams

  • Living reference work entry
  • First Online:
Encyclopedia of Big Data Technologies

Synonyms

Event streams; Information flows

Definitions

A data stream is a countably infinite sequence of elements. Different models of data streams exist that take different approaches with respect to the mutability of the stream and to the structure of stream elements. Stream processing refers to analyzing data streams on-the-fly to produce new results as new input data becomes available. Time is a central concept in stream processing: in almost all models of streams, each stream element is associated with one or more timestamps from a given time domain that might indicate, for instance, when the element was generated, the validity of its content, or when it became available for processing.

Overview

A data stream is a countably infinite sequence of elements and is used to represent data elements that are made available over time. Examples are readings from sensors in an environmental monitoring application, stock quotes in financial applications, or network data in computer monitoring...

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

References

  • Affetti L, Margara A, Cugola G (2017) Flowdb: integrating stream processing and consistent state management. In: Proceedings of the international conference on distributed and event-based systems, DEBS’17. ACM, pp 134–145. https://doi.org/10.1145/3093742.3093929

  • Akidau T (2015) The world beyond batch: streaming 101

    Google Scholar 

  • Akidau T, Bradshaw R, Chambers C, Chernyak S, Fernández-Moctezuma RJ, Lax R, McVeety S, Mills D, Perry F, Schmidt E, Whittle S (2015) The dataflow model: a practical approach to balancing correctness, latency, and cost in massive-scale, unbounded, out-of-order data processing. VLDB 8(12):1792–1803. https://doi.org/10.14778/2824032.2824076

    Google Scholar 

  • Arasu A, Babu S, Widom J (2006) The CQL continuous query language: semantic foundations and query execution. VLDB 15(2):121–142. https://doi.org/10.1007/s00778-004-0147-z

    Article  Google Scholar 

  • Babcock B, Babu S, Datar M, Motwani R, Widom J (2002) Models and issues in data stream systems. In: Proceedings of the symposium on principles of database systems, PODS’02. ACM, pp 1–16. https://doi.org/10.1145/543613.543615

  • Botan I, Derakhshan R, Dindar N, Haas L, Miller RJ, Tatbul N (2010) Secret: a model for analysis of the execution semantics of stream processing systems. VLDB 3(1–2):232–243. https://doi.org/10.14778/1920841.1920874

    Google Scholar 

  • Carbone P, Katsifodimos A, Ewen S, Markl V, Haridi S, Tzoumas K (2015) Apache flink: stream and batch processing in a single engine. Bull IEEE Comput Soc Tech Comm Data Eng 36(4):28–38.

    Google Scholar 

  • Carbone P, Ewen S, Fóra G, Haridi S, Richter S, Tzoumas K (2017) State management in Apache flinkⓇ: consistent stateful distributed stream processing. Proc VLDB 10(12):1718–1729. https://doi.org/10.14778/3137765.3137777

    Article  Google Scholar 

  • Cugola G, Margara A (2012) Processing flows of information: from data stream to complex event processing. ACM Comput Surv 44(3):15:1–15:62. https://doi.org/10.1145/2187671.2187677

    Article  Google Scholar 

  • Doblander C, Rabl T, Jacobsen HA (2014) Processing big events with showers and streams. In: Rabl T, Poess M, Baru C, Jacobsen HA (eds) Specifying big data benchmarks. Springer, Berlin/Heidelberg, pp 60–71

    Chapter  Google Scholar 

  • Etzion O, Niblett P (2010) Event processing in action. Manning Publications, Greenwich

    Google Scholar 

  • Luckham DC (2001) The power of events: an introduction to complex event processing in distributed enterprise systems. Addison-Wesley, Boston

    Google Scholar 

  • Marz N, Warren J (2015) Big data: principles and best practices of scalable realtime data systems. Manning Publications, Greenwich

    Google Scholar 

  • Stonebraker M, Çetintemel U, Zdonik S (2005) The 8 requirements of real-time stream processing. SIGMOD Rec 34(4):42–47. https://doi.org/10.1145/1107499.1107504

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Politecnico di Milano, Milano, Italy

    Alessandro Margara

  2. Database Systems and Information Management Group, Technische Universität Berlin, Berlin, Germany

    Tilmann Rabl

Authors
  1. Alessandro Margara
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tilmann Rabl
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Alessandro Margara or Tilmann Rabl .

Editor information

Editors and Affiliations

  1. Institute of Computer Science, University of Tartu, Tartu, Estonia

    Sherif Sakr

  2. Sch of Info Techno, Building J12, University of Sydney Sch of Info Techno, Building J12, Sydney, Australia

    Albert Zomaya

Section Editor information

  1. Politecnico di Milano http://home.deib.polimi.it/margara/

    Alessandro Margara

  2. Database Systems and Information Management Group, Technische Universität Berlin, Berlin, Germany

    Tilmann Rabl

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Margara, A., Rabl, T. (2018). Definition of Data Streams. In: Sakr, S., Zomaya, A. (eds) Encyclopedia of Big Data Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-63962-8_188-1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-63962-8_188-1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63962-8

  • Online ISBN: 978-3-319-63962-8

  • eBook Packages: Springer Reference MathematicsReference Module Computer Science and Engineering

Publish with us

Policies and ethics

Search

Navigation