Big Data

  • 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 diagnostics

    The team, currently formed by 4 members, is in charge of all the data management questions of the Earth Sciences: from software development (visualization, computation of statistics, formatting of the data, physical diagnostics) to exploration of new Big Data technologies for Earth Sciences

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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

    The goal of this area is to offer a simple and efficient data system. Currently we focus on big data scientific applications. Our main interests are: exploit novel hierarchical storage systems, design a data interface independent of the data system and support efficient multidimensional queries.

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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.

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  • Storage platform for data sharing

    The Big Data challenge responds to the growing need for combining disparate data sources to gain new insights from data.  The goal of this research line is to realize a convenient way of sharing data, both for consumers and for providers, in order to motivate data sharing and foster the creation of new knowledge and services that would otherwise be impossible to provide.

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