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

P. Kannisto and D. Hästbacka, "Cloud-based Management of Machine Learning Generated Knowledge for Fleet Data Refinement", in A. Fred, J. Dietz, D. Aveiro, K. Liu, J. Bernardino, and J. Filipe, Eds., Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2016; Communications in Computer and Information Science, vol 914) (CCIS), 2019, pp. 267-286. doi:10.1007/978-3-319-99701-8_13

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Research project(s): D2I


Distributed Knowledge Management; Mobile machinery; Cloud services; Data preprocessing; Machine learning


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.