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Dealing with Velocity and Variety in the Acquisition of Heterogeneous Sensor Data

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Book cover Knowledge Engineering and Knowledge Management (EKAW 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10180))

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Abstract

ETL (Extraction-Transform-Load) tools, traditionally developed to operate offline, need to be enhanced to deal with various, fast, big and fresh data and be executed on the edge of the network during the acquisition process. In this dissertation we wish to develop facilities that from one side make easy, scalable and controllable the development of data acquisition plans that can be executed on the edge of the network during loading and transmission. From the other side, we wish to deal with the variety of the data and verify when the developed data acquisition plans adhere to the common semantics adopted in the Domain Ontology. These facilities are included in StreamLoader, a web application tailored for the specification and monitoring of sensor data acquisition plans.

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Notes

  1. 1.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Milano, Milan, Italy

    Luca Ferrari

Authors
  1. Luca Ferrari
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Corresponding author

Correspondence to Luca Ferrari .

Editor information

Editors and Affiliations

  1. University of Bologna , Bologna, Italy

    Paolo Ciancarini

  2. University of Bologna , Bologna, Italy

    Francesco Poggi

  3. Stanford University , Palo Alto, California, USA

    Matthew Horridge

  4. Lancaster University , Lancaster, United Kingdom

    Jun Zhao

  5. Garvan Institute of Medical Research , Darlinghurst, New South Wales, Australia

    Tudor Groza

  6. Universidad Politecnica de Madrid , Boadilla del Monte, Spain

    Mari Carmen Suarez-Figueroa

  7. The Open University , Milton Keynes, United Kingdom

    Mathieu d'Aquin

  8. STLab ISTC-CNR , Rome, Italy

    Valentina Presutti

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Ferrari, L. (2017). Dealing with Velocity and Variety in the Acquisition of Heterogeneous Sensor Data. In: Ciancarini, P., et al. Knowledge Engineering and Knowledge Management. EKAW 2016. Lecture Notes in Computer Science(), vol 10180. Springer, Cham. https://doi.org/10.1007/978-3-319-58694-6_35

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  • DOI: https://doi.org/10.1007/978-3-319-58694-6_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-58693-9

  • Online ISBN: 978-3-319-58694-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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