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Distributed Knowledge Management Architecture and Rule Based Reasoning for Mobile Machine Operator Performance Assessment

P. Kannisto, D. Hästbacka, L. Palmroth, and S. Kuikka, "Distributed Knowledge Management Architecture and Rule Based Reasoning for Mobile Machine Operator Performance Assessment", in Proceedings of the 16th International Conference on Enterprise Information Systems (ICEIS), 2014, pp. 440-449. DOI: 10.5220/0004870004400449

ISBN978-989-758-027-7
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ConferenceICEIS , Lisbon, Portugal - http://www.iceis.org
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APA

Kannisto, P., Hästbacka, D., Palmroth, L., & Kuikka, S. (2014). Distributed Knowledge Management Architecture and Rule Based Reasoning for Mobile Machine Operator Performance Assessment. Proceedings of the 16th International Conference on Enterprise Information Systems – ICEIS (pp. 440-449). SCITEPRESS. https://doi.org/10.5220/0004870004400449
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Bibtex

@inproceedings{kannisto_et_al2014distributed,
    year = {2014},
    author = {Kannisto, Petri and Hästbacka, David and Palmroth, Lauri and Kuikka, Seppo},
    title = {Distributed Knowledge Management Architecture and Rule Based Reasoning for Mobile Machine Operator Performance Assessment},
    pages = {440-449},
    booktitle = {Proceedings of the 16th International Conference on Enterprise Information Systems (ICEIS)},
    isbn = {978-989-758-027-7},
    publisher = {SCITEPRESS},
    doi = {10.5220/0004870004400449},
    url = {https://doi.org/10.5220/0004870004400449}
}
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IEEEtran

P. Kannisto, D. Hästbacka, L. Palmroth, and S. Kuikka, "Distributed Knowledge Management Architecture and Rule Based Reasoning for Mobile Machine Operator Performance Assessment", in Proceedings of the 16th International Conference on Enterprise Information Systems (ICEIS), 2014, pp. 440-449. DOI: 10.5220/0004870004400449
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Research project(s): D2I

Keywords

Distributed Knowledge Management; Rule Based Reasoning; Operator Performance Assessment; Mobile Machines

Abstract

The performance of mobile machine operators has a great impact on productivity that can be translated to, for example, wasted time or environmental concerns such as fuel consumption. In this paper, solutions for improving the assessment of mobile machine are studied. Usage data is gathered from machines and utilized to provide feedback for operators. The feedback is generated with rules that define in what way different measures indicate performance. The study contributes to developing an architecture to manage both data collection and inference rules. A prototype is created: rule knowledge is managed with decision tables from which machine-readable rules are generated. The rules are then distributed to application instances executed in various locations. The results of the prototype promote several benefits. Rules can be maintained independent of the actual assessment application, and they can also be distributed from a centrally managed source. In addition, no IT expertise is required for rule maintenance so the rule administrator can be a pure domain expert. The results bring the architecture towards a scalable cloud service that combines the benefits of both centralized knowledge and distributed data management.