Strategies for Managing Linked Enterprise Data

Data, information and knowledge become key assets of our 21st century economy. As a result, data and knowledge management become key tasks with regard to sustainable development and business success. Often, knowledge is not explicitly represented residing in the minds of people or scattered among a variety of data sources. Knowledge is inherently associated with semantics that conveys its meaning to a human or machine agent. The Linked Data concept facilitates the semantic integration of heterogeneous data sources. However, we still lack an effective knowledge integration strategy applicable to enterprise scenarios, which balances between large amounts of data stored in legacy information systems and data lakes as well as tailored domain specific ontologies that formally describe real-world concepts. In this thesis we investigate strategies for managing linked enterprise data analyzing how actionable knowledge can be derived from enterprise data leveraging knowledge graphs. Actionable knowledge provides valuable insights, supports decision makers with clear interpretable arguments, and keeps its inference processes explainable. The benefits of employing actionable knowledge and its coherent management strategy span from a holistic semantic representation layer of enterprise data, i.e., representing numerous data sources as one, consistent, and integrated knowledge source, to unified interaction mechanisms with other systems that are able to effectively and efficiently leverage such an actionable knowledge. Several challenges have to be addressed on different conceptual levels pursuing this goal, i.e., means for representing knowledge, semantic data integration of raw data sources and subsequent knowledge extraction, communication interfaces, and implementation. In order to tackle those challenges we present the concept of Enterprise Knowledge Graphs (EKGs), describe their characteristics and advantages compared to existing approaches. We study each challenge with regard to using EKGs and demonstrate their efficiency. In particular, EKGs are able to reduce the semantic data integration effort when processing large-scale heterogeneous datasets. Then, having built a consistent logical integration layer with heterogeneity behind the scenes, EKGs unify query processing and enable effective communication interfaces for other enterprise systems. The achieved results allow us to conclude that strategies for managing linked enterprise data based on EKGs exhibit reasonable performance, comply with enterprise requirements, and ensure integrated data and knowledge management throughout its life cycle

Traductor de consultas SPARQL, formuladas sobre fuentes de datos incompletamente alineadas, que aporta una estimación de la calidad de la traducción.

147 p.Hoy en día existe en la Web un número cada vez mayor de conjuntos de datos enlazados de distinta procedencia, referentes a diferentes dominios y que se encuentran accesibles al público en general para ser libremente explotados. Esta tesis doctoral centra su estudio en el ámbito del procesamiento de consultas sobre dicha nube de conjuntos de datos enlazados, abordando las dificultades en su acceso por aspectos relacionados con su heterogeneidad. La principal contribución reside en el planteamiento de una nueva propuesta que permite traducir la consulta realizada sobre un conjunto de datos enlazado a otro sin que estos se encuentren completamente alineados y sin que el usuario tenga que conocer las características técnicas inherentes a cada fuente de datos. Esta propuesta se materializa en un traductor que transforma una consulta SPARQL, adecuadamente expresada en términos de los vocabularios utilizados en un conjunto de datos de origen, en otra consulta SPARQL adecuadamente expresada para un conjunto de datos objetivo que involucra diferentes vocabularios. La traducción se basa en alineaciones existentes entre términos en diferentes conjuntos de datos. Cuando el traductor no puede producir una consulta semánticamente equivalente debido a la escasez de alineaciones de términos, elsistema produce una aproximación semántica de la consulta para evitar devolver una respuesta vacía al usuario. La traducción a través de los distintos conjuntos de datos se logra gracias a la aplicación de un variado grupo de reglas de transformación. En esta tesis se han definido cinco tipos de reglas, dependiendo de la motivación de la transformación, que son: equivalencia, jerarquía, basadas en las respuestas de la consulta, basadas en el perfil de los recursos que aparecen en la consulta y basadas en las características asociadas a los recursos que aparecen en la consulta.Además, al no garantizar el traductor la preservación semántica debido a la heterogeneidad de los vocabularios se vuelve crucial el obtener una estimación de la calidad de la traducción producida. Por ello otra de las contribuciones relevantes de la tesis consiste en la definición del modo en que informar al usuario sobre la calidad de la consulta traducida, a través de dos indicadores: un factor de similaridad que se basa en el proceso de traducción en sí, y un indicador de calidad de los resultados, estimado gracias a un modelo predictivo.Finalmente, esta tesis aporta una demostración de la viabilidad estableciendo un marco de evaluación sobre el que se ha validado un prototipo del sistema

A Survey of the First 20 Years of Research on Semantic Web and Linked Data

International audienceThis paper is a survey of the research topics in the field of Semantic Web, Linked Data and Web of Data. This study looks at the contributions of this research community over its first twenty years of existence. Compiling several bibliographical sources and bibliometric indicators , we identify the main research trends and we reference some of their major publications to provide an overview of that initial period. We conclude with some perspectives for the future research challenges.Cet article est une étude des sujets de recherche dans le domaine du Web sémantique, des données liées et du Web des données. Cette étude se penche sur les contributions de cette communauté de recherche au cours de ses vingt premières années d'existence. En compilant plusieurs sources bibliographiques et indicateurs bibliométriques, nous identifions les principales tendances de la recherche et nous référençons certaines de leurs publications majeures pour donner un aperçu de cette période initiale. Nous concluons avec une discussion sur les tendances et perspectives de recherche

Enhanced Living Environments

This open access book was prepared as a Final Publication of the COST Action IC1303 “Algorithms, Architectures and Platforms for Enhanced Living Environments (AAPELE)”. The concept of Enhanced Living Environments (ELE) refers to the area of Ambient Assisted Living (AAL) that is more related with Information and Communication Technologies (ICT). Effective ELE solutions require appropriate ICT algorithms, architectures, platforms, and systems, having in view the advance of science and technology in this area and the development of new and innovative solutions that can provide improvements in the quality of life for people in their homes and can reduce the financial burden on the budgets of the healthcare providers. The aim of this book is to become a state-of-the-art reference, discussing progress made, as well as prompting future directions on theories, practices, standards, and strategies related to the ELE area. The book contains 12 chapters and can serve as a valuable reference for undergraduate students, post-graduate students, educators, faculty members, researchers, engineers, medical doctors, healthcare organizations, insurance companies, and research strategists working in this area

ExtremeEarth meets satellite data from space

Bringing together a number of cutting-edge technologies that range from storing extremely large volumesof data all the way to developing scalable machine learning and deep learning algorithms in a distributed manner, and having them operate over the same infrastructure poses unprecedentedchallenges. One of these challenges is the integration of European Space Agency (ESA)s Thematic Exploitation Platforms (TEPs) and data information access service platforms with a data platform, namely Hopsworks, that enables scalable data processing, machine learning, and deep learning on Copernicus data, and development of very large training datasets for deep learning architectures targeting the classification of Sentinel images. In this paper, we present the software architecture of ExtremeEarth that aims at the development of scalable deep learning and geospatial analytics techniques for processing and analyzing petabytes of Copernicus data. The ExtremeEarth software infrastructure seamlessly integrates existing and novel software platforms and tools for storing, accessing, processing, analyzing, and visualizing large amounts of Copernicus data. New techniques in the areas of remote sensing and artificial intelligence with an emphasis on deep learning are developed. These techniques and corresponding software presented in thispaper are to be integrated with and used in two ESA TEPs, namely Polar and Food Security TEPs. Furthermore, we presentthe integration of Hopsworks with the Polar and Food Securityuse cases and the flow of events for the products offered through the TEPs

Deployment and Operation of Complex Software in Heterogeneous Execution Environments

This open access book provides an overview of the work developed within the SODALITE project, which aims at facilitating the deployment and operation of distributed software on top of heterogeneous infrastructures, including cloud, HPC and edge resources. The experts participating in the project describe how SODALITE works and how it can be exploited by end users. While multiple languages and tools are available in the literature to support DevOps teams in the automation of deployment and operation steps, still these activities require specific know-how and skills that cannot be found in average teams. The SODALITE framework tackles this problem by offering modelling and smart editing features to allow those we call Application Ops Experts to work without knowing low level details about the adopted, potentially heterogeneous, infrastructures. The framework offers also mechanisms to verify the quality of the defined models, generate the corresponding executable infrastructural code, automatically wrap application components within proper execution containers, orchestrate all activities concerned with deployment and operation of all system components, and support on-the-fly self-adaptation and refactoring