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Machine Learning for Cyber Physical Systems pp 9–15Cite as

Semantic Stream Processing in Dynamic Environments Using Dynamic Stream Selection

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Part of the book series: Technologien für die intelligente Automation ((TIA))

Abstract

Cyber-physical systems (CPS) require a new level of dynamics in information processing. Databases and query approaches need to be extended towards dynamic stream aggregation and analysis systems. In this paper, we designed ECQELS, a semantic stream processing engine, to support CPS applications by adding essential features like dynamic sensor selection. We present a feature complete first implementation and show competitive performance results.

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References

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Author information

Authors and Affiliations

  1. Karlsruhe, Germany

    Michael Jacoby & Till Riedel

Authors
  1. Michael Jacoby
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  2. Till Riedel
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Corresponding author

Correspondence to Michael Jacoby .

Editor information

Editors and Affiliations

  1. Institut für Optronik, Systemtechnik und Bildauswertung, Fraunhofer, Karlsruhe, Germany

    Jürgen Beyerer

  2. inIT - Institut für industrielle Informationstechnik, Hochschule Ostwestfalen-Lippe inIT, Lemgo, Germany

    Oliver Niggemann

  3. MRD, Fraunhofer IOSB MRD, Karlsruhe, Germany

    Christian Kühnert

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Jacoby, M., Riedel, T. (2017). Semantic Stream Processing in Dynamic Environments Using Dynamic Stream Selection. In: Beyerer, J., Niggemann, O., Kühnert, C. (eds) Machine Learning for Cyber Physical Systems. Technologien für die intelligente Automation. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-53806-7_2

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  • DOI: https://doi.org/10.1007/978-3-662-53806-7_2

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  • Publisher Name: Springer Vieweg, Berlin, Heidelberg

  • Print ISBN: 978-3-662-53805-0

  • Online ISBN: 978-3-662-53806-7

  • eBook Packages: EngineeringEngineering (R0)

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