Data centricity plays a fundamental role in defining new and disruptive business models.
Many organizations in public and private sectors have successfully adopted information technologies to build huge repositories of data that they can analyze to support decision-making and gain a competitive advantage.
However, despite the paramount relevance of data-driven technologies, organizations demand alliance-driven infrastructures capable of supporting controlled data exchange across diverse stakeholders and transparent data management.
Data ecosystems (DEs) are the future of data management, since they allow organizations to share data and collaborate to get valuable insights.
Endeavours, such as the
European Health Data Space, and projects, such as the
International Data Spaces (IDS), demonstrate the importance of data ecosystems for diverse domains.
However, the benefits of data exchange can only be achieved with a holistic approach for generating and sharing knowledge.
Thus, DEs aim to solve issues like managing unstructured and heterogeneous data, offering various data-centric services, including query processing and data analytics, exchanging and integrating data while preserving data privacy, data security, and data sovereignty.
Hence, implementing a data ecosystem imposes tremendous challenges regarding, amongst others, data management, data quality, trust, data exchange, data integration, machine learning, interoperability, and knowledge-based systems.
Additionally, there is a huge potential in applying new methods from generative AI, which can show new directions of solving the above challenges in an efficient way.
In this workshop, we welcome innovative contributions that further the idea of data ecosystems and tackle the challenges resulting from the complexity of data ecosystems.