Dynamic system on Big Data for the detection of competency needs in the construction sector

Reference: 591843 EPP-1-2017-1-ES-EPP-1-201-1-ES-EPPKA2-SSA-NE

DETECTA project belongs to ERASMUS+ call (KA2: Cooperation for innovation and the exchange of good practices – Sector Skills Alliances, EACEA/04/2017 call)


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 for 2012 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 to such changes, 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 the Network on prospective reports, job offers and training offers to know in real time where and with what qualification the workers are needed and if the training strategies that are proposed In the market respond to the demands of the companies. This is possible thanks to the combination of semantic intelligence technologies with the Big Data’s large-volume information analysis power.


PROJECT BUDGET 496,834 € 496,834





Co-financed by EU Erasmus+ program

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