Energy communities (ECs) are an important tool towards a fair energy transition. Hence, the European Union (EU) has positioned ECs at the centre of its energy strategy and the foreseen transformation of its energy system. This paper aims to give an overview of key aspects and challenges for the implementation of the EC concept. Firstly, the regulatory framework is examined with a focus on the new definitions for ECs introduced by the EU, Renewable Energy Communities (RECs) and Citizen Energy Communities (CECs). Secondly, examples of established ECs and their main objectives are mentioned. Additionally, based on the identified challenges and requirements of establishing ECs, the key technologies that are implemented or have the potential to be deployed in an EC are examined, as well as innovative cross-cutting services that are optimally suited to be integrated in an EC. Moreover, the data management challenges linked to some of these technologies are considered. Finally, an overview of actual or potential financing schemes to support the EC development is given. Overall, the analysis highlighted the regulatory, technical and financial aspects and challenges that ECs are facing and the need to address them so that the EC concept is effective and successful. The main challenges identified for each of these aspects are the regulatory compliance with the legal framework, the data management dimension when innovative technological concepts are adopted and the financing of new projects.

This paper analyzes the challenges and requirements of establishing energy data ecosystems (EDEs) as data-driven infrastructures that overcome the limitations of currently fragmented energy applications. It proposes a new data- and knowledge-driven approach for management and processing. This approach aims to extend the analytics services portfolio of various energy stakeholders and achieve two-way flows of electricity and information for optimized generation, distribution, and electricity consumption. The approach is based on semantic technologies to create knowledge-based systems that will aid machines in integrating and processing resources contextually and intelligently. Thus, a paradigm shift in the energy data value chain is proposed towards transparency and the responsible management of data and knowledge exchanged by the various stakeholders of an energy data space. The approach can contribute to innovative energy management and the adoption of new business models in future energy data spaces.

Recently, a large number of open data repositories, catalogs and portals have been emerging in the scientific and government realms. In this chapter, we characterise this newly emerging class of information systems. We describe the key functionality of open data portals, present a conceptual model and showcase the pan-European data portal PublicData.eu as a prominent example. Using examples from Serbia and Poland, we present an approach for lifting the often semantically shallow datasets registered at such data portals to Linked Data in order to make data portals the backbone of a distributed global data warehouse for our information society on the Web.

Big Data refers to data volumes in the range of Exabyte (10 ¹⁸ ) and beyond. Such volumes exceed the capacity of current on-line storage and processing systems. With characteristics like volume, velocity and variety big data throws challenges to the traditional IT establishments. Computer assisted innovation, real time data analytics, customer-centric business intelligence, industry wide decision making and transparency are possible advantages, to mention few, of Big Data. There are many issues with Big Data that warrant quality assessment methods. The issues are pertaining to storage and transport, management, and processing. This paper throws light into the present state of quality issues related to Big Data. It provides valuable insights that can be used to leverage Big Data science activities.

Critical infrastructures (CI) are difficult to handle due to their complexity, size and the number of stakeholders involved. During emergency situations (e.g. fire or terrorist attack), a CI operator in the control room is faced with a flood of information coming from different sensors and legacy monitoring systems. Since in these situations, time is critical and the operators are under a great deal of pressure, a holistic management of all the technical systems and actors involved is needed, reinforced by the Recommendation and Decision Support System (RDSS) that helps emergency managers to take correct and timely decisions. One way to provide an adequate RDSS support to the operator is proposed in this paper which is based on an intelligent, event driven layer that sits on top of the legacy CI monitoring system. Powered by the complex event processing capabilities and facility data model implemented in the form of CI ontology, this layer processes events originating from different sources, conducts the situation and risk assessment, and reacts accordingly, either automatically or via recommendations proposed to emergency personnel. To validate the proposed approach, an event-driven RDSS was deployed on an airport use case (Nikola Tesla airport in Belgrade), as one of the most complex CIs.