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

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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.

The current systems for detecting employment and training needs show significant shortcomings: - In many cases they are static reports made on a given date, which are quickly obsolete. - They are not updateable in time. - They are based on samples and not on the totality of demands of employment that are produced. - They do not indicate precisely the location of employment and training needs.

In general, they do not respond to issues as relevant as: - Can we know in a real time and centrally the offers of employment of the Construction sector in Europe? - And the offer of training aimed at workers in the sector? - Is it possible to anticipate emerging professional skills? - Do the unemployed professionals and the employed workers have consultation tools that favor the effective and natural movement of people among the territories?

The reality is that it truly difficult to capture, process and transform information about the contents of Construction job offers published, due to are involved a variety of sources or channels, specific portals, job boards social partners, government agencies, etc., or not establish what the training offered periodically offer construction centres and institutions involved in promoting job training. If we add the convenience of identifying where the deals are taking both, jobs and training, in order to facilitate access and mobility of people.

Specifically, we refer to the combination of technologies semantic intelligence to the power of the analysis of large volumes of information at high speed (BigData), in order to perform a capture, processing, analysis and mass storage from formal and informal sources. This analysis can be performed using advanced text mining techniques and resources as well as techniques of semantic enrichment of information (based on languages like RDF and OWL) and the analysis of natural language.

The solution that we propose helps to capture, understand and structure the information, identify patterns and hidden correlations in the data and induce knowledge, agile, precise and easily can convert data into valuable information facilitating strategic decision-making and such as real-time consultation of all information.

The main functionalities to be implemented - Near real time searching - Natural language search in training and job offers - Filter by categories, concepts or any other - Graphical visualization of the documents found - Trends and correlation among training and job offers - Multilingual - Geolocation of the information - Multilingual Ontology annotation tool for topics categorization in job offers and training

The final result of the application of the above technologies to the exposed problematic is going to contribute the core of this Project: DETECTA: Big Data geo system, processing and analysis of job and training relating to Construction. This tool will provide: • A geographical and chronological, evolutionary map of the supply of training for employment in the countries of the project consortium. • A geographical and chronological, evolutionary map of the jobs in the countries of the project consortium. • Detailed real time of the training and existing jobs as well as the source or publisher in each case. • Contrasts between the training required for the jobs offered and the training offer in the market.

The project faces the following problems detected: - The Construction sector is a very changing sector, with a current orientation clearly aimed at adopting strategies for an increasingly energy efficient building. - The existence of studies of qualification needs that have a strictly static character and that, therefore, are quickly obsolete. - The need to turn to primary sources of information, such as experts in the employment market in the construction sector, enterprises and training institutions, to draw valid conclusions about the training needs of the sector. - The difficulty in processing and analyzing such a high volume of information composed of all the contributions of the experts, as well as all job offers and training offers that run public and private entities related to employment. - The low geographic mobility of workers, due to ignorance about which countries and regions there are job offers.

This project, thanks to the application of semantic intelligence and BigData for the construction of a tool for detection and geolocation of job offers and training, solves the problems indicated above.

The overall objective of the project is in line with the purpose of lot 1: Alliances for sectoral competencies for the identification of competency needs, as it is "oriented towards the identification and compilation of detailed data on competency needs in a specific economic sector ", which will allow to mitigate such deficits through the provision of training ".

The specific objectives of the project can be summarized in the following points: - Develop a prospective diagnosis of the Construction sector, affected by technological, regulatory and social changes, in its different aspects: economic-business, skills and training. - Design a methodology for the detection of training needs based on the analysis of various sources: contributions of expert panels of the sector's labor market, job offers and training offers that are being published; All segmented by place, type of competition, date, type of company, etc.) - Develop a BigData platform to extract information from the indicated sources and analyze it to elaborate specific reports of detection of training needs. - Analyze the diverse results of the platform BigData developed, obtaining the deficits competences and formative, as well as their geo-positioning in the European panorama.

Ultimately, the project aims, on the one hand, to adapt the training offer to the needs of companies and, on the other hand, to facilitate the labor mobility of workers in the Construction sector.

The project takes into account the panorama of EU competences, as well as the classification of skills, Competencies, Qualifications and European Occupations (ESCO). The spirit of the proposal is to investigate the labor market in order to anticipate and uncover training needs associated with the skills that are actually needed by the companies.