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

  • Petri Kannisto
  • David Hästbacka
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 914)

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.

Keywords

Distributed Knowledge Management Mobile machinery Cloud services Data preprocessing Machine learning 

Notes

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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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Tampere University of TechnologyTampereFinland

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