Section: Overall Objectives

Overall Objectives

Data-intensive science such as agronomy, astronomy, biology and environmental science must deal with overwhelming amounts of experimental data, as produced through empirical observation and simulation. Similarly, digital humanities are faced for decades with the problem of exploiting vast amounts of digitized cultural and historical data, such as broadcasted radio or TV content. Such data must be processed (cleaned, transformed, analyzed) in order to draw new conclusions, prove scientific theories and eventually produce knowledge. However, constant progress in scientific observational instruments (e.g. satellites, sensors, large hadron collider), simulation tools (that foster in silico experimentation) or digitization of new content by archivists create a huge data overload. For example, climate modeling data has hundreds of exabytes.

Scientific data is very complex, in particular because of the heterogeneous methods, the uncertainty of the captured data, the inherently multiscale nature (spatial, temporal) of many sciences and the growing use of imaging (e.g. molecular imaging), resulting in data with hundreds of dimensions (attibutes, features, etc.). Modern science research is also highly collaborative, involving scientists from different disciplines (e.g. biologists, soil scientists, and geologists working on an environmental project), in some cases from different organizations in different countries. Each discipline or organization tends to produce and manage its own data, in specific formats, with its own processes. Thus, integrating such distributed data gets difficult as the amounts of heterogeneous data grow. Finally, a major difficulty is to interpret scientific data. Unlike web data, e.g. web page keywords or user recommendations, which regular users can understand, making sense out of scientific data requires high expertise in the scientific domain. And interpretation errors can have highly negative consequences, e.g. deploying an oil driller under water at a wrong position.

Despite the variety of scientific data, we can identify common features: big data; manipulated through workflows; typically complex, e.g. multidimensional; with uncertainty in the data values, e.g., to reflect data capture or observation; important metadata about experiments and their provenance; and mostly append-only (with rare updates).

The three main challenges of scientific data management can be summarized by: (1) scale (big data, big applications); (2) complexity (uncertain, high-dimensional data), (3) heterogeneity (in particular, data semantics heterogeneity). These challenges are also those of data science, with the goal of making sense out of data by combining data management, machine learning, statistics and other disciplines. 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, we strive to develop architectures, models and algorithms that can be implemented as components or services in specific computing environments, e.g. the cloud. We design and validate our solutions by working closely with our scientific partners in Montpellier such as CIRAD, INRA and IRD, which provide the scientific expertise to interpret the data. 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.

Our approach is to capitalize on the principles of distributed and parallel data management. In particular, we exploit: high-level languages as the basis for data independence and automatic optimization; declarative languages to manipulate data and workflows; and highly distributed and parallel environments such as cluster and cloud for scalability and performance. We also exploit machine learning, probabilities and statistics for high-dimensional data processing, data analytics and data search.