The Integration Technology for Collective Expert Knowledge in the Tasks of Developing Scenarios for Vocational Guidance and Employees’ Rehabilitation
Andrey Vitalievich Gorokhov 1 *, Igor Valerievich Petukhov 2, Lyudmila Aleksandrovna Steshina 2
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1 Faculty of Information Technologies and Computer Engineering, Volga State University of Technology, Yoshkar-Ola, RUSSIA
2 Design and Production of Computing Systems Department, Volga State University of Technology, Yoshkar-Ola, RUSSIA
* Corresponding Author

Abstract

This study aims at developing information technology to support the synthesis of scenarios for vocational guidance and workers’ rehabilitation based on collective expert knowledge integration. Collective expert knowledge is formalized by the conceptual modeling method. The functionally targeted approach enables the integration of collective expert knowledge at the stage of creating a conceptual model. The authors’ technologies for the synthesis of control algorithms based on the conceptual model are applied to synthesize and select scenarios for vocational guidance and workers’ rehabilitation. The application of the functionally targeted approach at the synthesis stage ensures the adequacy of relevant synthesized scenarios. Tree structures used in the functionally targeted approach are convenient for experts and allow them to build a hierarchical description of the main objects, processes, and relationships of the studied system in terms of the subject domain. This approach allows for rationally justified synthesis, and choice of psycho-physiological rehabilitation scenarios from the perspective of vocational rehabilitation goals.

License

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Article Type: Research Article

https://doi.org/10.12973/ejmste/80012

EURASIA J Math Sci Tech Ed, 2017 - Volume 13 Issue 11, pp. 7517-7526

Publication date: 08 Nov 2017

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