Laboratorio Cini Data Science

  • Data Architectures: The Data Architecture describes the way data will be processed, stored and used by the organization that will use it. It lays out the criteria on processing operations including the whole flow of the system.

  • Cloud-edge continuum, decentralized intelligence

  • Data integration and interoperability

  • Data processing, analytics and visualization: The research area on Data Processing, Analytics, and Visualization encompasses a wide range of techniques and methodologies aimed to unlock valuable insights from large and complex datasets. This area plays a pivotal role in extracting valuable knowledge from data, empowering informed decision-making, and driving innovation in various domains such as business, healthcare, finance, and more.

  • Data quality and utility: The area commonly referred to as Data Quality consists of algorithms, methods, and measures that can be used to assess, understand, and improve data in their capability of correctly representing the business scenario that they describe.

  • Data security and protection

  • Data shaping, modeling and design: Data Shaping, Modeling, and Design is a fertile research area within computer science that focuses on how data is structured, interpreted, and utilized in various applications. The research area covers several interrelated subfields aimed at optimizing the ways data is managed and leveraged. This area addresses how data is collected, structured, represented, and made useful for various computational processes, and it includes a spectrum of methodologies and approaches aimed at transforming raw data into meaningful representations that facilitate analysis, decision-making, and innovation.

  • Data sovereignty and governance

  • Data storage, preparation and operation: The field of data storage, preparation, and operation encompasses various aspects of managing data efficiently and effectively. This research area is critical in today's data-driven world, where organizations face challenges in handling large volumes of diverse data sources and require streamlined processes for data preparation and operational tasks.

  • Data trustworthiness and ethics: Ensuring data integrity and ethical use across data pipelines, transforming data-driven digital interactions into trustworthy experiences

  • Open science and open data:Open Science represents a transformative approach to research embracing principles of transparency, collaboration, and inclusivity. At its core, Open Science advocates for the unrestricted sharing of research findings, methodologies, and processes, emerges as a critical component aimed at democratizing access to research data.

  • Process mining and business intelligence

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