Real-time Labour Market Informationon Skill Requirements

Extraction and analysis of Online Job Vacancies data

Access to data

Alessandro Vaccarino – Matteo Fontana – Mauro Pelucchi

EssNET Workshop11 June 2019, Thessaloniki, Greece

1. Dashboards and tools for different stakeholders2. The DataLab

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Topics

1. Dashboards and tools for different stakeholders

2. The DataLab

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Topics

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Interactive session with Tableau

Take a look on

1. Dashboards and tools for different stakeholders

2. The DataLab

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Topics

• Denormalized tables• Columnar Store

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DataLab – Model & DatastoreKeywords

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DataLab Model2 Tables

FT Document FT Skill Analysis

1 Row for each:- General_ID à Key- OJV- Source- Place

Why? Because, for each OJV, we candetect a multi-place Vacancye.g. «Software Developer in London / Liverpool»

1 Row for each:- General_ID à Key- OJV- Source- Place- Skill

Why? Because, for each OJV, we canobviously detect multiple skills

e.g. «Software Developer in London / Liverpool, with customer orientation culture, that speaks english and tolerates stress»

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DataLab Model

So, to have the number of job vacanciesyou always have to compute an unique

count by General_ID

FT Document

OJV

Place

DatesLang

Occupation

ContractEd Level

Sector

Salary

WorkingHours

Experience

Source Site

Company

1

n

FT Skill Analysis

OJV

Place

DatesLang

Occupation

ContractEd Level

Sector

Salary

WorkingHours

Experience

Source Site

Company

Skill1

n

1n

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The DataLabArchitecture

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The DataLabTools

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The DataLabData

Which data are contained in the DataLab?

Place

DatesOJVs collected between 2018Q3 and 2019Q1, so:• 201807 à 201812 (2019S2)• 201901 à 201903 (2019Q1)

OJVs related to Vacancies located in:• IT, UK, IE, CZ, FR, DE, ES à Early Release• AT, PL, BE, NL, SE, LU à Green Release

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Interactive session with the DataLab

Time to “hand-on”