1. Cloud-Based Management of Machine Learning Generated Knowledge for Fleet Data Refinement
- Author
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David Hästbacka, Petri Kannisto, Tampere University, Automation and Hydraulic Engineering, and Pervasive Computing
- Subjects
Information management ,020203 distributed computing ,Computer science ,business.industry ,020208 electrical & electronic engineering ,Cloud computing ,02 engineering and technology ,113 Computer and information sciences ,Machine learning ,computer.software_genre ,Workflow ,Software deployment ,Component (UML) ,0202 electrical engineering, electronic engineering, information engineering ,Preprocessor ,Anomaly detection ,Data pre-processing ,Artificial intelligence ,business ,computer - 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. acceptedVersion
- Published
- 2018
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