Distributed Artificial Intelligence

The Distributed Artificial Intelligence Area is focused on fostering the adoption of the data-driven philosophy by the society through leveraging the potential of Big Data technologies. Some of the most important aspects that the area tackles are:

Scalable Architectures for Big Data processing.

Open Big Data.

Big Data preparation (curate, clean, enrich, integrate).

Data Virtualization (tailored access to shared Big Data environments).

Research Challenges

Data slicing

To develop the data slicing concept for a multi-tenant Big Data architecture for central data repositories.

To study and adapt current Big Data technologies in order to permit tailored and isolated access to shared data resources though data slicing.

To use machine learning techniques aiming at helping the user define schemas (data slices) on top of shared big data lakes.

To mature the data slicing as a scalable option that can replace traditional ETL processes.






Heterogeneous data integration

ETL processes

Central open data repository

Tailored and secure access to shared data resources

Data preparation

Some of our partners

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