Title:
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ASSESSING JOB MARKET DYNAMICS USING ELK
STACK |
Author(s):
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Gabriel Silva, Mário Rodrigues, Marlene Amorim, Angélica Souza, Marta Dias and Armando Pinho |
ISBN:
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978-989-8704-23-8 |
Editors:
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Pedro Isaías |
Year:
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2020 |
Edition:
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Single |
Keywords:
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Natural Language Processing, Ontology, Data Overview, ESCO, ELK Stack, Job Market |
Type:
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Full |
First Page:
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91 |
Last Page:
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98 |
Language:
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English |
Cover:
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Full Contents:
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click to dowload
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Paper Abstract:
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The adoption of digital technologies promises to accelerate the transformation and the agility of processes, work activities
and revenue models. Yet, the promised gains come together with dramatic needs for qualified professionals who can
effectively leverage the technology potential. Job contexts are being reshaped as new models for the interaction and
integration of humans and technologies take shape.
To increase the readiness of the job market in this fast-changing context it is important that all stakeholders - companies,
professionals, policy makers - are aware of the job market dynamics and needs. This can be observed from the collection
of job announcements, but its high volume requires effective tools for analyzing and simplifying it in order to draw
timely and correct conclusions.
ELK stack was used for dealing with the high volume of job announcements. ELK is a stable platform that can manage
large quantities of data and the Kibana layer enables to rapidly explore data and create visualization dashboards. As job
announcements have distinct formulations for similar roles, depending on the hiring company, this raises the necessity of
establishing a common ground for comparing the job descriptions. In this work were mapped job descriptions to ESCO
occupations. ESCO is an ontology published by the European Union and its occupations are job positions.
Results show that the ELK stack is a suitable tool for providing a visual interpretation on the job market dynamics.
Moreover, the first experiments using natural language processing techniques and machine learning algorithms revealed
an accuracy over 0.9 in mapping job descriptions to ESCO occupations. This result is very promising and shows that
ESCO a good candidate as common ground to enable comparison of job market dynamics for distinct environments. |
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