Big Data
-
Art+Science driven innovation
We encourage and foster collaborations with artists because we strongly believe that art and artists can benefit from and contribute to science and technology.
-
Big Data Frameworks
This research line has developed the ALOJA Project, an open research benchmarking and analysis platform that aims to lower the total cost of ownership (TCO) of Big Data deployments and study their performance characteristics for optimization.
-
Data-Centric Architectures
This research line aims to develop new data-centric architectures that leverage emerging technologies (accelerators, NVMe) to accelerate workloads, including the development of new interfaces to access the devices as well as new programming paradigms (active storage, KV stores).
-
Data-Driven Scientific Computing
Providing big data scientifc applications with a simple and efficient data system
-
Distributed Object Management
Managing objects from the datacenter to the edge to facilitate application development and improve performance.
-
High-performance IO and storage
Storage has become a key component in HPC systems, and the challenges for the Exascale era are huge. In this research line we address such problems both for data and metadata.
-
Integration of Programming Models and Persistent Storage Systems
This research line is focused on the integration of COMPSs programming model with persistent storage systems in order to target Big Data and persistency problems.
-
Interactive data visualization
We understand quantitative information better by interacting with it: requesting, processing, and communicating information becomes an efficient tool for making decisions when it is done in an intuitive and convenient way, with a good user experience.
-
NoSQL technologies applied to Life Sciences
Present bioinformatics faces an exponential growth of data. Genomics, clinical records, or simulation data accumulate terabytes of data that require new ways of storage. NoSQL database managers have become increasingly popular as an easily scalable solution to data management in biology.
-
Provenance, Metadata and Reproducibility
Record metadata of experiments as provenance information, and leverage it for Governance, Reproducibility, Traceability and Knowledge Extraction.
-
Scientific Visualization and storytelling
Good communication fosters the advance of science and returns value to society. We develop visual strategies to help scientists communicate their research with the most suitable solution for each dataset and story.