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Cloud-based management of machine learning generated knowledge for fleet data refinement

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publicationKnowledge Discovery, Knowledge Engineering and Knowledge Management
Subtitle of host publication8th International Joint Conference, IC3K 2016, Porto, Portugal, November 9–11, 2016, Revised Selected Papers
PublisherSpringer
Pages267-286
Number of pages20
ISBN (Electronic)978-3-319-99701-8
ISBN (Print)978-3-319-99700-1
DOIs
Publication statusPublished - 14 Nov 2018
Publication typeA4 Article in a conference publication
EventInternational Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Porto, Portugal
Duration: 9 Nov 201611 Nov 2016

Publication series

NameCommunications in Computer and Information Science
Volume914
ISSN (Print)1865-0929

Conference

ConferenceInternational Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management
CountryPortugal
CityPorto
Period9/11/1611/11/16

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.

ASJC Scopus subject areas

Keywords

  • Data preprocessing, Distributed knowledge management, Machine learning, Mobile machinery cloud services

Publication forum classification

Field of science, Statistics Finland