de contacto entre mi solicitud y D. Luis Ques; a ambos les estaré siempre agradecido, ya que sin su colaboración no hubiera podido realizar el trabajo de campo y, en consecuencia, esta tesis no hubiera sido viable.Los responsables de GICAMAN, S.A me trataron magníficamente y pusieron a mi disposición toda la información que les requería. Quiero mostrar mi agradecimiento a D. Gustavo Nofuentes García, en su día Director General de GICAMAN, S.A; a D.

<p>The availability of large training datasets (TDS) has enabled much of the innovative use of Machine Learning (ML) and Artificial Intelligence (AI) in fields such as computer vision or language processing. In Earth Observation and geospatial science/applications, the availability of TDS has generally been limited and there are a number of specific geospatial challenges to consider (e.g. spatial reference systems, spatial/spectral/radiometric/temporal resolutions). Creating TDS for EO applications commonly involves labor intensive processes and the willingness to share such datasets has been very limited. While the current open accessibility of EO datasets is unprecedented, the availability of training and ground truth datasets has not improved much over the last years, and this is limiting the potential innovative impact that new ML/AI methodologies could have in the EO domain. Next to general availability and accessibility, further challenges need to be addressed in terms of making TDS interoperable and findable and lowing the barriers for non-geospatial experts.</p> <p> </p> <p>In the response to these challenges, ESA has initiated development of the Earth Observation Training Data Lab (EOTDL). EOTDL is being developed on top of federated European cloud infrastructure and aims to address the EO community requirements for working with TDS in EO workflows, adopting FAIR data principles and following open science best-practices. <br /><br /></p> <p>The specific capabilities that EOTDL will support include:</p> <ul> <li>Repository and Curation: host, import and maintain training datasets, ground truth data, pretrained models and benchmarks, providing versioning, tracking and provenance.</li> <li>Tooling: provide a set of integrated open-source tools compatible with the major ML/AI frameworks to create, analyze and optimize TDS and to support data ingestion, model training and inference operations.</li> <li>Feature engineering: Link with the main EO data archives and EO analytics platforms to support feature engineering and large-scale inference.</li> <li>Quality assurance: embed QA throughout the offered capabilities, also taking advantage of automated deterministic checks and defined levels of TDS maturity.</li> </ul> <p>To achieve these goals, EOTDL is building on proven technologies, such as STAC (Spatio Temporal Asset Catalog) to support data cataloguing and discoverability, openEO and SentinelHub APIs for EO data access and feature engineering, GeoDB for vector geometry and attribute handing, and EoxHub to support interactive tooling. The EOTDL functionality will be exposed via web-based GUIs, python libraries and command line interfaces.  </p> <p>A central objective is also the incentivization of community engagement to support quality assurance and encourage the contribution of datasets. For this award mechanisms are being established. The initial data population consists of around 100 datasets while intuitive data ingestion pipelines allow for continuous community contributions. Three defined product maturity levels are linked to QA procedures and support the trustworthiness of the data population. The development is coordinated with Radiant ML Hub to seek synergies rather than duplicate the offered capabilities.  </p> <p>This presentation will showcase the current development status of EOTDL and discuss in detail some key aspects such as the data curation with STAC and the adopted quality assurance and feature engineering approaches. A set of use cases that establish new TDS creation tools and result in large scale datasets are presented as well.</p>

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Internet has become the main channel of sales and promotion for the hotel industry, having particular importance the opinions disseminated by the guest about their experiences through Internet, which requires special attention from hoteliers. This is a phenomenon of the so-called Web 2.0, which involves large amount of user generated content (UGC) and the extension of the traditional "word of mouth" through Internet (eWom). It has also resulted in large structured databases, like Booking and TripAdvisor, which allow researchers to develop specific sector studies in a rapid, inexpensive and convenient manner.When analyzing the Booking score system, we discovered some unexpected peculiarities, which were not considered in previous studies using this database. We deal with the controversial issue of TripAdvisor reliability using different approaches and methodologies .We conclude that there is no evidence of massive manipulation, but we found a number of cases of infringement of its rules, that makes us hesitate about the control systems implemented by this website. Using a large sample of Spanish coast hotels and TripAdvisor andBooking databases, we conducted several investigations, including implementation level of social media and relation between the proper use of these tools and the scores obtained by hotels. Additionally, we studied scores evolution during the period 2011-2014, as well as relevant factors affecting it.Booking. 4.5.1. Metodología de selección de la muestra general___________________________ 63 4.5.2. Magnitudes principales de la muestra ___________________________________ 64 4.6. Conclusiones sobre las bases de datos de opiniones de hoteles _______ 67 FUNCIONAMIENTO DEL SISTEMA DE PUNTUACIONES DE BOOKING 705.1. La escala 2,5 -10 de Booking ____________________________________ 71 Efectos de la escala del sistema de puntuaciones deBooking _________ 75 5.2.1. Priceline __________________________________________________________ 75 5.2.2. Objetivos y Metodología ______________________________________________ 78 5.2.3. Muestra ___________________________________________________________ 80 5.2.4. Resultados de la comparación entre Booking y Priceline ____________________ 82 5.2.5. El caso del Hotel Pennsylvania ________________________________________ 88 5.2.6. Conclusiones ______________________________________________________ 91 6. EVOLUCIÓN DE LAS VALORACIONES DE LOS HOTELES ESPAÑOLES DE COSTA (2011-2014) ________________________________________ 95 6.1. Introducción y Metodología ______________________________________ 95 6.2. Resultados obtenidos ___________________________________________ 96 6.3. Conclusiones __________________________________________________ 99 7. FACTORES QUE INFLUYEN EN LA PUNTUACIÓN DE LOS HOTELES 102 7.1. Relación entre número de estrellas y puntuación del hotel ___________ 102 7.2. Relación entre el tamaño del hotel y su número de estrellas y empleados ________________________________________________________________ 104 7.3. Relación entre el tamaño de un hotel y el número d...