doi: 10.17586/2226-1494-2022-22-1-127-137


Intellectualization of personnel development management in high-tech service-oriented companies
 

A. D. Beresnev


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Article in Russian

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Beresnev A.D. Intellectualization of personnel development management in high-tech service-oriented companies. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2022, vol. 22, no. 1, pp. 127–137 (in Russian). doi: 10.17586/2226-1494-2022-22-1-127-137


Abstract
The paper proposes an approach to managing the personnel development in service-oriented IT companies, which is based on a parametric model of training highly qualified personnel and implemented using intelligent algorithms. The parameterization of the training model is carried out on the rough sets theory. The implementation of intelligent algorithms required the following technologies: thematic modeling with additive regularization (Additive Regularization Thematic Model), a special environment for the current development of the configuration of information systems and the analysis of formal concepts (Formal Concept Analysis). The approach was developed as a set of techniques and implemented as a software library. The efficiency assessment was carried out on a dataset that contains the results of processing for 2,948 service requests processed by employees of a service-oriented IT company in 4 months. The results of the experimental evaluation showed that the use of the developed set of methods and the library of software tools increased the efficiency of the work of service engineers in terms of key indicators from 31 to 54 %. Application of the developed approach will make it possible to quickly adapt personnel qualifications in service-oriented IT companies in the context of a rapid change in production tasks and work environment without interrupting the work process.

Keywords: personnel development, service model of service delivery, on-the-job training, rough sets, text processing, thematic modeling with additive regularization, formal concept analysis

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