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Cloud-Based Management of Machine Learning Generated Knowledge for Fleet Data Refinement

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Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 914))

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Abstract

The modern mobile machinery has advanced on-board computer systems. They may execute various types of applications observing machine operation based on sensor data (such as feedback generators for more efficient operation). Measurement data utilisation requires preprocessing before use (e.g. outlier detection or dataset categorisation). As more and more data is collected from machine operation, better data preprocessing knowledge may be generated with data analyses. To enable the repeated deployment of that knowledge to machines in operation, information management must be considered; this is particularly challenging in geographically distributed fleets. This study considers both data refinement management and the refinement workflow required for data utilisation. The role of machine learning in data refinement knowledge generation is also considered. A functional cloud-managed data refinement component prototype has been implemented, and an experiment has been made with forestry data. The results indicate that the concept has considerable business potential.

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Acknowledgments

This work was made as a part of the D2I (Data to Intelligence) project funded by Tekes (the Finnish Funding Agency for Innovation). The authors would like to express their sincere gratitude to the project partners and participant companies.

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Authors and Affiliations

  1. Tampere University of Technology, P.O. Box 692, FI-33101, Tampere, Finland

    Petri Kannisto & David Hästbacka

Authors
  1. Petri Kannisto
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  2. David Hästbacka
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Corresponding author

Correspondence to Petri Kannisto .

Editor information

Editors and Affiliations

  1. Instituto de Telecomunicações, Lisbon, Portugal

    Ana Fred

  2. Department of Software Technology, Delft University of Technology, Voorburg, Zuid-Holland, The Netherlands

    Jan Dietz

  3. Faculty of Exact Sciences and Engineering, University of Madeira, Funchal, Portugal

    David Aveiro

  4. Henley Business School, University of Reading, Reading, UK

    Kecheng Liu

  5. University of Coimbra, Coimbra, Portugal

    Jorge Bernardino

  6. Instituto Politecnico de Setúbal (IPS), Setúbal, Portugal

    Joaquim Filipe

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Kannisto, P., Hästbacka, D. (2019). Cloud-Based Management of Machine Learning Generated Knowledge for Fleet Data Refinement. In: Fred, A., Dietz, J., Aveiro, D., Liu, K., Bernardino, J., Filipe, J. (eds) Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2016. Communications in Computer and Information Science, vol 914. Springer, Cham. https://doi.org/10.1007/978-3-319-99701-8_13

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  • DOI: https://doi.org/10.1007/978-3-319-99701-8_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99700-1

  • Online ISBN: 978-3-319-99701-8

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