Big Data
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Big data analytics and visualization
We work on creating visual and algorithmic tools to analize and study large volumes of data, helping extract knowledge from complex sources and produce better informed decisions.
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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.
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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).
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Data-Driven Scientific Computing
Providing big data scientifc applications with a simple and efficient data system
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Distributed Object Management
Managing objects from the datacenter to the edge to facilitate application development and improve performance.
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High-performance IO
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.
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Human Computer Interaction
Human computer interaction is performed in different ways and increasingly in more types of devices. The way we access information, process it, and communicate back with a machine should be done in an intuitive and convenient way to obtain a good user experience.
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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.
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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.
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Scientific Visualization and storytelling
Science needs to be shared among peers to foster its advance, and it needs to return to society to close the investment cycle. We develop visual strategies to help scientists to communicate their research, trying to find the most suitable solution for each dataset and for each story.