This line of research has defined models and methods for the evaluation / monitoring and improvement of data quality, and has developed methodologies for the analysis and redesign of quality-oriented processes.

Data-centric AI emphasizes the critical role of high-quality input data in data analytics. Complete, consistent, and error-free data significantly enhance the performance and accuracy of AI models, enabling the generation of valuable insights.

The research aims to define methods and models for establishing trusted, efficient, and sustainable data sharing among organizations within a federation, covering every stage from planning to actual data transfer. A data mesh approach serves as the reference model, supported by deployable platforms in a hybrid cloud/edge environment that enforces policies based on agreements between participating parties.

Research in Business Process Management focuses on enhancing the monitoring phase by developing tools to model monitoring requirements through BPMN extensions, incorporating commitments, and utilizing robust platforms based on smart devices integrated with blockchain. Additionally, it involves analyzing collected data to predict future behaviors and the impact of low quality in event logs.

Reducing the environmental impact of service-oriented applications deployed across heterogeneous infrastructures is essential for minimizing the overall IT footprint. Our research aims to enhance these applications with adaptive capabilities, enabling them to adjust dynamically based on their operational context. Specifically, we focus on monitoring, adapting, and optimizing energy efficiency and carbon footprint, leveraging insights from real-time data.

Security research addresses privacy in service systems and the Cloud, architectural solutions in the integration of information systems, and security of big data and business intelligence systems. For the design phase, the design and impact of security policies in IS and data are studied. To support security monitoring, security warnings based on low-level system log analysis and process mining are studied.

Research in this field has focused on the design of systems for data segmentation, recommendation systems for personalized content filtering based on user requests and prediction systems able to simulate future behavior and support users in their decisions. This line of research has also developed methodologies and tools for Social Media Intelligence. The challenge of the research concerns the measurement of the impact of emergency events through social media.