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METHODS OF LABOR MARKET ANALYSIS BASED ON BIG
DATA TECHNOLOGIES
Medetova Kunduz Muratovna,
Tashkent university of information technologies
named after Muhammad al-Khwarizmi
A
bstract. This article discusses some approaches to the analysis of
automated systems for monitoring the labor market using big data technologies.
Information technologies for the labor market are proposed. The approaches
and methods considered can be widely used in the analysis of the labor market.
Keywords: labor market, big data analysis, tracking employment, dynamic
system.
In recent decades, the nature and characteristics of the labor market in
developed and developing countries have undergone significant changes under
the influence of a number of forces and factors. The labor market is a complex
multidimensional dynamic system with a large number of feedbacks. In
addition, the amount of information about vacancies and job applicants is
constantly growing. All this leads to the fact that the analysis of the labor market
by traditional analytical means is becoming more complex and insufficiently
complete. The cardinal solution to the problem is seen in the application of
methods of working with big data, which, secondly, are able to work with this
task of a large amount of data, and secondly, perform new approaches to them
without involving a large number of analysts.
Labor market analysis is a management function designed to study,
systematize, generalize and evaluate the results achieved in the labor market [1].
Interested in the results of the labor market analysis: a) employers - would like
to know what the applicants are, what requirements they impose on the
employer; on the other hand, what competitors offer to applicants; b) applicants
- evaluate proposals, comparing them with their capabilities and requests; c)
education management systems and educational institutions - we would like to
understand how it is necessary to adjust the list of directions and specialties,
plans for the recruitment of applicants and curricula for the preparation of in-
demand graduates.
One of the promising areas of labor market analysis, in our opinion, is Big
Data technologies, since they imply working with information of a huge volume
and diverse composition, very often updated and located in different sources [2].
Big Data–or Big Data) is a set of approaches, methods and tools for working
with a full volume of heterogeneous, apparently unrelated data, in order to
extract benefits for business.
The task of Big data analysis is as follows: there is a large amount of data
and it is assumed that there is some "hidden knowledge" in this data. These
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"hidden knowledge" should be: a) not obvious - the patterns found should not be
detected by visual analysis of data, as well as using standard methods of
information processing; b) unknown - the patterns found should not confirm
already known information; c) practically useful – the data obtained have a
certain value and they can be used in practice; d) objective - the detected
patterns must fully correspond to reality; e) available for interpretation – the data
obtained must be logically explicable, they can be presented in a visual form and
easily explained in terms of the subject area.
Pic. 1. Model five "V" big data adapted for use in
labor market context
The use of Big Data technology for labor market analysis has certain
difficulties associated with the collection, processing and analysis of
information. Firstly, the information on employment websites is often
unstructured, and if structured, then these structures differ from site to site. The
complexity of analyzing such information lies in the fact that not all users
completely fill in the necessary information, and therefore there are certain
"holes" in the data that need to be minimized and smoothed out. There are also
sites that offer users to upload their resumes or vacancies for a position in a
simple text format, to which the built-in filters can no longer be applied. Such
data also needs to be collected and analyzed by resorting to semantic analysis.
Secondly, there is a lot of duplicate information on various sites. When
looking for an employee or a job, no serious employer or job seeker will stop at
one resource, because posting information on several sites increases the chance
of being noticed. In this regard, you should consider excluding copies of
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vacancies or resumes from the sample, because this can seriously affect the final
result of the analysis.
Thirdly, the dynamics of the labor market implies a greater dependence of
the result on the time and frequency of data collection. Monitoring of the labor
market implies the need for constant recalculation in order to exclude serious
obsolescence of the results. The situation in this area is constantly changing and
what was relevant today will fade into the background tomorrow[3].
Another significant difficulty in using the principles of Big Data is the
huge number of analysis methods based on tools borrowed from statistics and
computer science. As an example, such methods as Data Mining,
crowdsourcing, network analysis, predictive modeling, visualization, cluster
analysis, etc. At the same time, work is constantly continuing on improving
existing methods, as well as creating new methods.
Labor market analysis is a complex task due to the large number of
heterogeneous rapidly changing data, which makes it extremely difficult to use
traditional methods of analysis. The use of Big Data technologies not only copes
with this task, but also allows you to gain new, "hidden" knowledge about the
subject area.
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