Description Description

Research on Advanced Information Systems Engineering (RAISE)

The research of the RAISE group at the Department of Electronics, Information and Bioengineering of Politecnico di Milano is focused on the development of adaptive information systems, handling the most critical issues along the entire data lifecycle and the design and development of novel IS architectures. The research mainly addresses quality-aware design of IS architectures to create adaptive information systems, with the goal of dynamically evolve in order to meet specific requirements and maintain the system operations. Application areas include healthcare, industry 4.0/5.0, cybersecurity, judiciary.

Research Topics

Data Quality Assessment and Improvement 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.

(contact: Cinzia Cappiello, Giacomo Palù, Barbara Pernici, Camilla Sancricca)

Data-centric AI 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.

(contact: Cinzia Cappiello, Giacomo Palù, Monica Vitali, Camilla Sancricca, Barbara Pernici)

Cloud/Edge Federated Data Sharing Architectures 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.

(contact: Pierluigi Plebani, Monica Vitali, Matteo Falconi, Valeria Fortina, Yang Shudan)

Business Process Monitoring 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.

(contact: Pierluigi Plebani, Barbara Pernici, Musa Salamov)

Green Information Systems 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.

(contact: Monica Vitali, Barbara Pernici)

Information Security and Security Analysis 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.

(contact: Mariagrazia Fugini, Cinzia Cappiello, Barbara Pernici, Pierluigi Plebani, Mattia Salnitri)

Big Data Analysis 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.

(contact: Chiara Francalanci, Barbara Pernici, Carlo Bono, Jingxian Wang)

Projects

  • H2020 project CS-AWARE-NEXT (2022-2025) (contact for RAISE group: C. Cappiello)
  • H2020 project TEADAL Trustworthy, Energy-Aware federated DAta Lakes along the computing continuum (contact for RAISE group: P. Plebani)
  • HORIZON-HLTH-2023 project BETTER (Better rEal-world healTh-daTa distributEd analytics Research platform) (contact for RAISE group: C. Cappiello)
  • HealthBigData
  • PNRR Project MICS (Made in Italy Circolare e Sostenibile) (contact for RAISE group: P. Plebani)
  • PNRR Project FAIR (Future Artificial Intelligence Research – Spoke 4 Adaptive AI) (2023-25), working on context-based adaptive approaches in machine learning (contact for RAISE group: B. Pernici)
  • PRIN 2022 project Discount quality for responsible data science: Human-in-the-Loop for quality data (2023-2026), working on the impact of improving data quality in data science (contact for RAISE group: B. Pernici)
  • PRIN 2022 project FREEDA (2023-2026) (contact for RAISE group: M. Vitali)
  • Fondazione CARIPLO (contact for RAISE group: P. Plebani)

Staff

Description

Link to information on the RAISE group on the Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB) department website of Politecnico di Milano.