The DETECTA project is funded by the European Commission under the Erasmus + program.

Funding body: Erasmus Plus: Sector Skills Alliance
Grant amount: €496, 833
Co-ordinator: FORTEC Formation Y Technologia (Spain), UL partner
Principle Investigators (UL): Dr Emma O’Brien and Dr Mark Southern
Partners: UL (Ireland), UFEMAT (Belgium), Technological Institute of Aragon (Spain)
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Twitter: @DetectaProject

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Project Background
The Construction sector is one of the most relevant at European level. The number of companies in the sector amounts to 3,429,268 (EUROSTAT 2015) and is estimated to reach 15,580,000 workers (CEDEFOP, 2008). The sector is affected by significant technological and regulatory changes and must address the challenges of producing buildings and infrastructures adapted to changing social and economic needs and meet global challenges such as energy security and climate change.

Any change in regulations, technology, social, etc., involves the adaptation of companies and workers, with training being the essential instrument to carry out such adaptation. At present it is possible to find static studies of training needs related to the professional skills for which it is necessary to qualify workers in the sector, but the reality is that tools such as the one proposed by the present project are needed that can detect qualification needs Dynamically and continuously over time and deliver results on:

- Training requirements, emanating from the companies themselves
- Trends in emerging professional skills
- Current training offer and its adaptation to the demands of companies.
- Geo-positioning of both the job offer and the training offer

The main contribution of the project is DETECTA, a totally innovative tool that provides the results indicated previously. Its operation is based on the management and processing of all the information published in prospective reports, job offers and training offers to know in real time where and with what qualification the workers are needed and if existing training strategies respond to the demands of the companies. This project will successfully do this by combining of semantic intelligence technologies with the Big Data’s large-volume information analysis power.

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