MONITORING THE LABOR MARKET USING BIG DATA ANALYTICS TECHNOLOGIES

Abstract

The object of this study is the monitoring data of the professional and qualification sphere in the context of the database of vacancies in the areas of professional activity «food industry». The purpose of performing the whole range of works: monitoring the professional and qualification sphere in order to analyze data on vacancies and resumes collected from open sources (Work in Russia, HeadHunter, SuperJob) to study the dynamics and structure of their distribution by areas of professional activity «food industry», as well as an analysis of the requirements of employers for the positions of employees in the labor market using Big Data analytics technologies. Due to the significant volume and complexity of the initial data, the entire scope of work was carried out using the Big Data analysis infrastructure deployed on a computing cluster. Apache Spark, Apache Flume were used as the main software packages for creating the infrastructure. To update the information with the reference book of professions, machine learning methods were used, including models prepared on the general and specialized corpora of texts in Russian, using the vector representation of words and expressions. Thus, the study analyzed the dynamics and structure of vacancies and resumes in the field of professional activity «food industry» for 55 professions in accordance with the Directory of Professions (regional section): change in the number of vacancies and resumes, maximum and minimum wages; current trends in the number of jobs are presented; a comparative analysis of changes in the average monthly nominal accrued wages of those working in the economy; the need for workers to fill vacancies in accordance with the All-Russian classifier of occupations was studied (OKZ).