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Learning about databases is indispensable for individuals studying software engineering or computer science or those involved in the IT industry. We analyzed a remote educational escape room for teaching about databases in four different higher education courses in two consecutive academic years. We employed three instruments for evaluation: a pre- and posttest to assess the escape room's effectiveness for student learning, a questionnaire to gather students' perceptions, and a Web platform that unobtrusively records students' interactions and performance. We show novel evidence that educational escape rooms conducted remotely can be engaging as well as effective for teaching about databases.

Educational Data Virtual Lab (EDVL) is an open-source platform for data exploration and analysis that combines the power of a coding environment, the convenience of an interactive visualization engine, and the infrastructure needed to handle the complete data lifecycle. Based on the building blocks of the FIWARE European platform and Apache Zeppelin, this tool allows domain experts to become acquainted with data science methods using the data available within their own organization, ensuring that the skills they acquire are relevant to their field and driven by their own professional goals. We used EDVL in a pilot study in which we carried out a focus group within a multinational company to gain insight into potential users’ perceptions of EDVL, both from the educational and operational points of view. The results of our evaluation suggest that EDVL holds a great potential to train the workforce in data science skills and to enable collaboration among professionals with different levels of expertise.

The collaboration of the real world and the virtual world, known as Digital Twin, has become a trend with numerous successful use cases. However, there are challenges mentioned in the literature that must be addressed. One of the most important issues is the difficulty of collaboration of Digital Twins due to the lack of standardization in their implementation. This article continues a previous work that proposed a generic architecture based on the FIWARE components to build Digital Twins in any field. Our work proposes the use of Linked Open Data as a mechanism to facilitate the communication of Digital Twins. We validate our proposal with a use case of an urban Digital Twin that collaborates with a parking Digital Twin. We conclude that Linked Open Data in combination with the FIWARE ecosystem is a real reference option to deploy Digital Twins and to enable the collaboration between Digital Twins.

We study the benefits and challenges of using Linked Open Data in smart city applications and propose a set of open source, highly scalable tools within the case of a public-rental bicycle system, which can act as a reference guide for other smart city applications.

The aerospace sector is one of the many sectors in which large amounts of data are generated. Thanks to the evolution of technology, these data can be exploited in several ways to improve the operation and management of industrial processes. However, to achieve this goal, it is necessary to define architectures and data models that allow to manage and homogenise the heterogeneous data collected. In this paper, we present an Airport Digital Twin Reference Conceptualisation’s and data model based on FIWARE Generic Enablers and the Next Generation Service Interfaces-Linked Data standard. Concretely, we particularise the Airport Digital Twin to improve the efficiency of flight turnaround events. The architecture proposed is validated in the Aberdeen International Airport with the aim of reducing delays in commercial flights. The implementation includes an application that shows the real state of the airport, combining two-dimensional and three-dimensional virtual reality representations of the stands, and a mobile application that helps ground operators to schedule departure and arrival flights.

The use of digitial twins (DTs) in industry has become a growing trend in recent years, allowing improvement of the life cycle of any process by taking advantage of the relationship between the physical and virtual worlds. Existing literature posits several challenges for building DTs, as well as some proposals for overcoming them. However, in the vast majority of the cases, the architectures and technologies presented are strongly bounded to the domain where the DTs are applied. This article proposes the FIWARE Ecosystem, combining its catalog of components and smart data models as a solution for the development of any DT. We also provide a use case to show how to use FIWARE for building DTs through a complete example of a parking DT. We conclude that the FIWARE Ecosystem constitutes a real reference option for developing DTs in any domain.

In recent years, many proposals of context-aware systems applied to IoT-based smart environments have been presented in the literature. Most previous works provide a generic high-level structure of how a context-aware system can be operationalized, but do not offer clues on how to implement it. On the other hand, there are many implementations of context-aware systems applied to specific IoT-based smart environments that are context-specific: it is not clear how they can be extended to other use cases. In this article, we aim to provide an open-source reference implementation for providing context-aware data analytics capabilities to IoT-based smart environments. We rely on the building blocks of the FIWARE ecosystem and the NGSI data standard, providing an agnostic end-to-end solution that considers the complete data lifecycle, covering from data acquisition and modeling, to data reasoning and dissemination. In other words, our reference implementation can be readily operationalized in any IoT-based smart environment regardless of its field of application, providing a context-aware solution that is not context-specific. Furthermore, we provide two example use cases that showcase how our reference implementation can be used in a variety of fields.

In recent years, a new business paradigm has emerged which revolves around effectively extracting value from data. In this scope, providing a secure ecosystem for data sharing that ensures data governance and traceability is of paramount importance as it holds the potential to create new applications and services. Protecting data goes beyond restricting who can access what resource (covered by identity and Access Control): it becomes necessary to control how data are treated once accessed, which is known as data Usage Control. Data Usage Control provides a common and trustful security framework to guarantee the compliance with data governance rules and responsible use of organizations’ data by third-party entities, easing and ensuring secure data sharing in ecosystems such as Smart Cities and Industry 4.0. In this article, we present an implementation of a previously published architecture for enabling access and Usage Control in data-sharing ecosystems among multiple organizations using the FIWARE European open source platform. Additionally, we validate this implementation through a real use case in the food industry. We conclude that the proposed model, implemented using FIWARE components, provides a flexible and powerful architecture to manage Usage Control in data-sharing ecosystems.