A Review of Methods for Processing Unstructured Data in the Assessment of Mining Personnel

• Main Authors: Barbara Anna, Pimonov Alexander, Sluder Lyubov
• Format: Article
• Language: English
• Published: EDP Sciences 2020-01-01
• Series: E3S Web of Conferences
• Online Access: https://www.e3s-conferences.org/articles/e3sconf/pdf/2020/34/e3sconf_iims2020_04040.pdf

Skilled staff, as well as conditions for their development and motivation, are key conditions for the successful operation of the company. Creation of a high-quality Human Resources (HR) personnel management system would allow to solve this problem. For the achievement of the ultimate goal - the most effective formation and development of the personnel potential of the enterprise requires the creation of conditions for all employees allowing the maximum use and increase of labour potential, as well use of creative abilities and creative thinking. Labour competences and competence evaluation represent real challenges for companies. When modelling a high-quality HR management system, it is important to take into account features such as presence of uncertainty and a large number of unstructured data. When evaluating personnel, the cognitive abilities of the decision-maker are involved and the use of fuzzy cognitive modeling (FCM) seems to be the most promising. In addition, cognitive models allow us to present complex relationships between investigated parameters revealing influence on each other. This paper considers an expert performance evaluation system based on competency model and a fuzzy logic model. The FCM based management personnel system’s is proposed. There are many performance evaluation methods; however, none is universal and common to all companies. This work brings contributions to HR management solutions, finding new ways to apply artificial intelligence (AI) techniques to processes that typically were performed by humans.