The euBusinessGraph ontology: A lightweight ontology for harmonizing basic company information

Company data, ranging from basic company information such as company name(s) and incorporation date to complex balance sheets and personal data about directors and shareholders, are the foundation that many data value chains depend upon in various sectors (e.g., business information, marketing and sales, etc.). Company data becomes a valuable asset when data is collected and integrated from a variety of sources, both authoritative (e.g., national business registers) and non-authoritative (e.g., company websites). Company data integration is however a difficult task primarily due to the heterogeneity and complexity of company data, and the lack of generally agreed upon semantic descriptions of the concepts in this domain. In this article, we introduce the euBusinessGraph ontology as a lightweight mechanism for harmonising company data for the purpose of aggregating, linking, provisioning and analysing basic company data. The article provides an overview of the related work, ontology scope, ontology development process, explanations of core concepts and relationships, and the implementation of the ontology. Furthermore, we present scenarios where the ontology was used, among others, for publishing company data (business knowledge graph) and for comparing data from various company data providers. The euBusinessGraph ontology serves as an asset not only for enabling various tasks related to company data but also on which various extensions can be built upon.publishedVersio

Technologies and Applications for Big Data Value

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems

The Elements of Big Data Value

This open access book presents the foundations of the Big Data research and innovation ecosystem and the associated enablers that facilitate delivering value from data for business and society. It provides insights into the key elements for research and innovation, technical architectures, business models, skills, and best practices to support the creation of data-driven solutions and organizations. The book is a compilation of selected high-quality chapters covering best practices, technologies, experiences, and practical recommendations on research and innovation for big data. The contributions are grouped into four parts: · Part I: Ecosystem Elements of Big Data Value focuses on establishing the big data value ecosystem using a holistic approach to make it attractive and valuable to all stakeholders. · Part II: Research and Innovation Elements of Big Data Value details the key technical and capability challenges to be addressed for delivering big data value. · Part III: Business, Policy, and Societal Elements of Big Data Value investigates the need to make more efficient use of big data and understanding that data is an asset that has significant potential for the economy and society. · Part IV: Emerging Elements of Big Data Value explores the critical elements to maximizing the future potential of big data value. Overall, readers are provided with insights which can support them in creating data-driven solutions, organizations, and productive data ecosystems. The material represents the results of a collective effort undertaken by the European data community as part of the Big Data Value Public-Private Partnership (PPP) between the European Commission and the Big Data Value Association (BDVA) to boost data-driven digital transformation

Technologies and Applications for Big Data Value

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems

Big Data na gestão eficiente das Smart Grids. HDS: Uma Plataforma Híbrida, Dinâmica e Inteligente

[POR]Nos últimos anos tem-se verificado um acréscimo exponencial de informação gerada e disponibilizada a cada dia. Devido ao rápido avanço tecnológico (dispositivos móveis; sensores; comunicação wireless; etc.) biliões e biliões de bytes são criados todos os dias. Este fenómeno, denominado por Big Data, é caracterizado por 5 Vs (i.e. Volume, Velocidade, Variedade, Veracidade, Valor) e cada um deles representa verdadeiros desafios (e.g. como recolher e transportar um grande volume de informação; como armazenar essa informação; como minerá-la, como analisá-la e extrair conhecimento, como garantir a sua segurança e privacidade, como processá-la em tempo real, etc.). É unanime na comunidade científica que o valor a extrair de toda esta informação constituirá um fator de extrema importância para a tomada de decisão, determinante no sucesso das mais variadíssimas áreas económicas, bem como na resolução de inúmeros problemas. Nestas áreas inclui-se o ecossistema energético que por razões ecológicas, económicas e políticas conduziu ao repensar da forma como consumimos e produzimos energia. Devido ao aumento das necessidades energéticas provocado pelo avanço tecnológico, ao previsto esgotamento dos recursos energéticos não renováveis e devido às diretivas para a eficiência energética impostas pela União Europeia, muitos têm sido os estudos feitos na área da gestão de recursos energéticos. O termo Smart Grids surgiu nas últimas décadas com o objetivo de definir um ecossistema energético inteligente, que visa não só a integração de inteligência, mas também de automação na operabilidade extremamente complexa de todos os seus processos. As Smart Grids têm sido alvo de grandes estudos e investimentos dos quais têm resultado avanços significativos. No entanto, alguns desafios estão ainda por concretizar nomeadamente na gestão do seu complexo fluxo de dados. É neste contexto que se enquadra a presente dissertação cujo principal objetivo se centra na obtenção de soluções para alguns dos problemas identificados no domínio de Smart Grids com recurso às novas técnicas e metodologias propostas na área de Big Data. Este trabalho apresenta um estudo sobre os recentes e crescentes avanços tecnológicos realizados na área de Big Data, onde são identificados os seus grandes desafios. Destes destacam-se a complexidade na gestão de fluxos contínuos e desordenados, a necessidade de reduzir o tempo despendido na prépreparação dos dados e o desafio de explorar soluções que proporcionem a automatização analítica. Por outro lado, o estudo analisa o impacto da aplicação nas novas tecnologias no desenvolvimento das Smart Grids, no qual se conclui que apesar de embrionária, a sua aplicação é imprescindível para a evolução do ecossistema energético. Deste estudo resultou ainda a identificação dos principais desafios na área das Smart Grids, dos quais se destacam a complexidade na gestão do seu fluxo de dados em tempo real e a necessidade de melhorar a precisão das previsões de consumo e produção de energia. Face aos desafios identificados foi proposto um modelo conceptual, baseado na arquitetura Docker Container, para o desenvolvimento de uma plataforma. Este modelo objetiva a flexibilidade e agilidade de forma a permitir a integração e validação das novas e crescentes abordagens tecnológicas propostas na área de Big Data, necessárias ao desenvolvimento das Smart Grids. A fim de validar o modelo proposto, foi desenvolvida uma stack onde foram implementados vários serviços que visaram contribuir para os desafios identificados na área de Big Data e Smart Grids, nomeadamente: visualização e monitorização dos dados recolhidos em tempo real; preparação dos dados recolhidos em tempo real; previsão em tempo real de várias séries temporais simultaniamente; deteção de anomalias; avaliação da precisão das previsões e geração de novos modelos para a previsão de consumo e produção de energia segundo determinados critérios. Finalmente foram desenvolvidos vários casos de estudo cujos resultados obtidos permitiram concluir sobre a importância da pré-preparação dos dados na fase analítica, sobre a eficiência na automatização analítica e sobre as vantagens da análise de ponta (Edge Analytics). Ao contrário de abordagens mais tradicionais que visam a execução centralizada do processo analítico, o edge analytics explora a possibilidade de executar a analise de dados de forma descentralizada a partir de um ponto não central do sistema. Os resultados permitiram concluir que o edge analytics traz vantagens acrescidas para a precisão das previsões. Permitiram ainda, inferir sobre como recolher os resultados a fim de se obter uma melhor precisão nas previsões, i.e., quanto mais específica e ajustada ao contexto forem executadas as previsões maior será a sua precisão.[ES]En los últimos años se ha verificado un aumento exponencial de información generada y disponible cada día. Debido al rápido avance tecnológico (dispositivos móviles, sensores, comunicación inalámbrica, etc.) billones y billones de bytes se crean todos los días. Este fenómeno, denominado Big Data, se caracteriza por 5 Vs (es decir, Volumen, Velocidad, Variedad, Veracidad, Valor) y cada uno de ellos representa verdaderos desafíos (por ejemplo, cómo recoger y transportar un gran volumen de información, cómo almacenar esa información, minarla, cómo analizarla y extraer conocimiento, cómo garantizar su seguridad y privacidad, cómo procesarla en tiempo real, etc.). Es unánime en la comunidad científica que el valor a extraer de toda esta información constituirá un factor de extrema importancia para la toma de decisión, determinante el éxito de las variadísimas áreas económicas, así como en la resolución de innumerables problemas. En estas áreas se incluye el ecosistema energético que por razones ecológicas, económicas y políticas condujo a repensar la forma en que consumimos y producimos energía. Debido al aumento de las necesidades energéticas provocado por el avance tecnológico, al previsto agotamiento de los recursos energéticos no renovables y debido a las directivas para la eficiencia energética impuestas por la Unión Europea, muchos han sido los estudios realizados en el ámbito de la gestión de recursos energéticos. El término Smart Grid surgió en las últimas décadas con el objetivo de definir un ecosistema energético inteligente, que apunta no sólo a la integración de inteligencia, sino también de automatización en la operatividad extremadamente compleja de todos sus procesos. Las Smart Grids han sido objeto de grandes estudios e inversiones de los cuales han resultado avances significativos. Sin embargo, algunos desafíos aún no se concretan en la gestión de su complejo flujo de datos. Es en este contexto que se encuadra la presente disertación cuyo principal objetivo se centra en la obtención de soluciones para algunos de los problemas identificados en el dominio de Smart Grids utilizando las nuevas técnicas y metodologías propuestas en el área de Big Data. Este trabajo presenta un estudio sobre los recientes y crecientes avances tecnológicos realizados en el área de Big Data, donde se identifican sus grandes desafíos. De ellos se destacan la complejidad en la gestión de flujos continuos y desordenados, la necesidad de reducir el tiempo empleado en la prepreparación de los datos y el desafío de explorar soluciones que proporcionen la automatización analítica. Por otro lado, el estudio analiza el impacto de la aplicación de nuevas tecnologías en el desarrollo de las Smart Grids, en el que se concluye que, a pesar de embrionaria, su aplicación es imprescindible para la evolución del ecosistema energético. De este estudio resultó también la identificación de los principales desafíos en el área de las Smart Grids, de los cuales se destacan la complejidad en la gestión de su flujo de datos en tiempo real y la necesidad de mejorar la precisión de las previsiones de consumo y producción de energía. En cuanto a los desafíos identificados, se propuso un modelo conceptual, basado en la arquitectura Docker Container, para el desarrollo de una plataforma. Este modelo tiene como objetivo la flexibilidad y agilidad para permitir la integración y validación de los nuevos y crecientes enfoques tecnológicos propuestos en el área de Big Data, necesarios para el desarrollo de las Smart Grids. Con el fin de validar el modelo propuesto, se desarrolló una stack donde se implementaron varios servicios que pretendían contribuir a los desafíos identificados en el área de Big Data y Smart Grids, en particular: visualización y seguimiento de los datos recogidos en tiempo real; preparación de los datos recogidos en tiempo real; previsión en tiempo real de multillas series temporales simultáneamente; detección de anomalías; evaluación de la precisión del predicción y generación de nuevos modelos para la previsión de consumo y producción de energía según ciertos criterios. Finalmente, se desarrollaron una serie de casos de estudo cuyos resultados nos permitieron concluir sobre la importancia de la preparación previa de los datos en la fase analítica, la eficiencia en la automatización analítica y las ventajas del análisis de borde (Edge Analytics). A diferencia de los enfoques más tradicionales para la ejecución centralizada del proceso analítico, el análisis de borde explora la posibilidad de realizar análisis de datos de forma descentralizada desde un punto no central del sistema. Los resultados permitieron concluir que el análisis de borde aporta ventajas añadidas a la precisión de los pronósticos. También nos permitieron inferir cómo recopilar los resultados para obtener una mejor precisión en las predicciones, por ejemplo, cuanto más precisos y ajustados al contexto se ejecuten los pronósticos, mayor será su precisión.[EN]In recent years, there has been an exponential increase of information generated and made available every day. Due to rapid technological advancement (e.g., mobile devices, sensors, wireless communication, etc.) billions and billions of bytes are created every day. This phenomenon, called Big Data, is characterized by 5 Vs (i.e., Volume, Velocity, Variety, Veracity, Value) and each represents real challenges (e.g., how to collect and carry a large amount of information; how to store this information; how mining it, analyzing it and extracting knowledge; how to ensure its security and privacy; how to process it in real time, etc.). It is unanimous in the scientific community that the value to be extracted from all this information will be a factor of extreme importance for the decision making, determining the success of the most varied economic areas, as well as the resolution of numerous problems. These areas include the energy ecosystem that, for ecological, economic and political reasons, led us to rethink the way we consume and produce energy. Due to the increase in energy needs caused by technological advances, the expected depletion of non-renewable energy resources and due to the energy efficiency directives imposed by the European Union, many studies have been carried out in the area of energy resources management. The term Smart Grid has emerged in the last decades with the objective of defining an intelligent energy ecosystem, which aims not only to integrate intelligence but also to automate the extremely complex operability of all its processes. Smart grids have been the subject of major studies and investments which have resulted in significant advances. However, some challenges have to be addressed in the management of its complex data flow. It is in this context that the present dissertation falls, with the main objective on obtaining solutions to some of the problems identified in the field of Smart Grids using new techniques and methodologies proposed in the area of Big Data. This paper presents a study on the recent and growing technological advances in the area of Big Data, where its major challenges are identified. These include complexity in the management of continuous and disordered flows, the need to reduce the time spent in pre-preparation of data and the challenge of exploring solutions that provide analytical automation. On the other hand, the study analyzes the impact of the application in the new technologies in the development of the Smart Grids, in which it is concluded that, although embryonic, its application is essential for the evolution of the energy ecosystem. This study also resulted in the identification of the main challenges in the area of Smart Grids, which highlight the complexity in managing its data flow in real time and the need to improve the accuracy of energy consumption and production forecasts. Given the identified challenges, a conceptual model, based on the Docker Container architecture, was proposed for the development of a platform. This model aims at flexibility and agility in order to allow the integration and validation of the new and growing technological approaches proposed in the area of Big Data, necessary for the development of Smart Grids. In order to validate the proposed model, a stack was developed where several services were implemented that aimed to contribute to the challenges identified in the area of Big Data and Smart Grids, namely: visualization and monitoring of data collected in real time; preparation of data collected in real time; real-time forecasting of multiple time series simultaneously; detection of anomalies; evaluation of the accuracy of forecasting and generation of new models for the forecast of consumption and production of energy according to certain criteria. Finally, a number of case studies were developed whose results allowed us to conclude on the importance of the pre-preparation of the data in the analytical phase, on the efficiency in the analytical automation and on the advantages of the Edge Analytics. Unlike more traditional approaches to the centralized execution of the analytic process, edge analytics explores the possibility of performing data analysis in a decentralized way from a non-central point of the system. The results allowed to conclude that edge analytics brings added advantages to the precision of the forecasts. Results allowed us to infer how to collect the data in order to obtain a better precision in the predictions, i.e., the more precise and context-adjusted the forecasts are executed the greater their accuracy

Modelling and linking company data in the EubusinessGraph platform

In the business environment, knowledge of company data is essential for a variety of tasks. The European funded project euBusinessGraph enables the establishment of a company data platform where data providers and consumers can publish and access company data. The core of the platform is the semantic data model that is the conceptual representation of company data in a common way so that it is easier to share and interlink company data. In this paper we show how the unified model and Grafterizer, a tool for manipulating and transforming raw data into Linked Data, support the linking challenge proposed in FEIII 2019. Results show that geographical enrichment of RDF data supports the interlinking process between company entities in different datasets