Diego Calvanese

Logic-based languages, such as Datalog and Answer Set Programming, have been recently put forward as a data-centric model to effectively specify and implement network services and protocols, seeing them as dynamic systems of distributed computational nodes where each node evolves an internal database and exchanges data with the other nodes of the network. This approach provides the basis for declarative distributed computing. A rigorous, comprehensive characterization of the decidability and complexity of verification in declarative distributed systems is yet to come. This paper provides a stepping stone towards this grand objective, considering the case where the network is a fixed connected graph, and nodes can incorporate fresh data from the external world into the system. We study the problem from different angles, delineating the decidability frontier of verification and providing tight complexity bounds for the decidable cases.

Artificial intelligence (AI) and data-driven applications are transforming industry and research across domains. Despite research advances, the management of the AI and data life cycle in organizations remains largely manual and ad hoc. At the same time, data spaces initiatives are emerging, providing new data-sharing opportunities but posing new challenges for organizations seeking to leverage all their potential. Tackling these challenges, this chapter describes the new framework proposed by the CyclOps project, which aims to enable interoperable and trustworthy automatic management, governance, and maintenance of the entire data life cycle for large-scale volumes of data generated in heterogeneous distributed sources. This supports the development and deployment of AI-based applications across both business and research contexts. CyclOps operationalizes the end-to-end data life cycle, placing at its core knowledge graphs, an established semantic formalism to represent data and metadata adhering to the FAIR Principles, while capturing relevant information to improve reproducibility, traceability, and explainability of the AI results. This core layer is complemented with tools for the automation of data management tasks; distributed data processing; AI tools, algorithms, and models; data space interoperability; and a human-centric interface. The chapter presents the key innovative propositions of CyclOps and its underlying technologies and illustrates them through selected use cases that highlight the general applicability of the approach.