Usage of Big Data for Information Support of the Labor Market

Author:

ROZBYTSKYI MYKHAILO

Abstract

The challenges of collecting current labor market data amidst global changes and geopolitical fluctuations increasingly necessitate new approaches and alternative methods of data collection and analysis. This highlights the relevance of research aimed at developing and adapting labor market information provision methods to contemporary challenges. A promising approach in this context is the use of Big Data for labor market assessment, which involves collecting information from sources such as online job search and vacancy portals. This method allows for a deeper analysis of market trends and provides a more accurate and timely assessment of labor market needs and opportunities. The aim of this article is to discuss approaches to developing labor market information systems using Big Data, particularly online data from job vacancy websites. It examines the use of Big Data in labor market analysis based on a database containing over four million job vacancies posted on Ukrainian job search portals over the last five years, provided by the European Training Foundation (ETF). The effectiveness of these approaches in facilitating job search for all interested parties is evaluated, particularly through providing insights into the dynamics of supply and demand in the labor market based on data from these portals. The opportunities and limitations of using Big Data in this context are analyzed, including their impact on employment policy development and labor market planning. The potential benefits of Big Data in providing deeper and more accurate market condition analyses are outlined, along with technical aspects and challenges associated with their processing and interpretation. The article examines methodological approaches to data collection and analytical processing in the context of accelerated transformations, volatility, and limited access to traditional information resources. The scientific novelty of the article lies in the substantiation of the feasibility and appropriateness of using open data from online job portals for labor market information provision under current conditions. In conducting the research, methods of analysis, synthesis, and generalization were applied to identify the main contemporary issues of labor market information provision in Ukraine. The effectiveness of data collection methods based on web scraping and parsing techniques was evaluated, as well as the use of the integrated Snowflake platform to identify key trends and patterns in the labor market. The conclusions summarize the main points and substantiate directions for further research, highlighting the significance of Big Data in developing employment strategies and optimizing the labor market.

Publisher

National Academy of Sciences of Ukraine (Co. LTD Ukrinformnauka) (Publications)

Reference13 articles.

1. Mashey, J. R. (1999). Big data and the next wave of {InfraStress} problems, solutions, opportunities. 1999 USENIX annual technical conference (USENIX ATC 99). https://static. usenix.org/event/usenix99/invited_talks/mashey.pdf

2. Sarioglo, V., & Ogay, M. (2023). Approach to population estimation in Ukraine using mobile operators' data. Statistics in Transition new series, 24 (1), 131-144. https: // doi:10.59170/stattrans-2023-008

3. Chetty, R., & Hendren, N. (2015). The impacts of neighborhoods on intergenerational mobility: Childhood exposure effects and county-level estimates. Harvard University and NBER, 133 (3), 14-16.

4. Sarioglo, V., & Cymbal, O. (2020). Labour market landscaping Ukraine. Big Data for labour market intelligence, 29 p.

5. Vaccarino, A. (2020). Big Data For Labour Market Information (Lmi) In Ukraine. Methodological overview and analytics insights on Ukrainian Web Labour Market Working Paper, 6-33. https://www.etf.europa.eu/sites/default/files/2021-05/ukraine_ big_data_lmi_analysis_2020_web.pdf

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3