Data-intensive science such as agronomy,astronomy, biology and environmental science must deal with overwhelming amounts of experimental data produced through empirical observation and simulation. The three main challenges of scientific data management can be summarized by: (1) scale (big data, big applications); (2) complexity (uncertain, multi-scale data with lots of dimensions), (3) heterogeneity (in particular, data semantics heterogeneity).
The overall goal of Zenith is to address these challenges, by proposing innovative solutions with significant advantages in terms of scalability, functionality, ease of use, and performance. To produce generic results, these solutions are in terms of architectures, models and algorithms that can be implemented in terms of components or services in specific computing environments, e.g. grid, cloud.
We design and validate our solutions by working closely with our scientific application partners such as INRA and IRD in France, or the National Research Institute on e-medicine (MACC) in Brazil. To further validate our solutions and extend the scope of our results, we also foster industrial collaborations, even in non scientific applications, provided that they exhibit similar challenges.