Section: Partnerships and Cooperations

International Initiatives

Inria International Partners

Informal International Partners

We have regular scientific relationships with research laboratories in

• North America: Univ. of Waterloo (Tamer Özsu), UCSB Santa Barbara (Divy Agrawal and Amr El Abbadi)

• Asia: National Univ. of Singapore (Beng Chin Ooi, Stéphane Bressan), Wonkwang University, Korea (Kwangjin Park)

• Europe: Univ. of Madrid (Ricardo Jiménez-Periz), UPC Barcelona (Josep Lluis Larriba Pey), HES-SO (Henning Müller), University of Catania (Concetto Spampinatto), The Open University (Stefan Rüger)

• North Africa: Univ. of Tunis (Sadok Ben-Yahia)

• Australia: Australian National University (Peter Christen)

• Central America: Technologico de Costa-Rica (Erick Mata, former director of the US initiative Encyclopedia of Life)

Inria Associate Teams Not Involved in an Inria International Lab

SciDISC

• Title: Scientific data analysis using Data-Intensive Scalable Computing

• Inria principal investigator:Patrick Valduriez

• International Partner:

  • Universidade Federal do Rio de Janeiro (Brazil), Marta Mattoso and Alvaro Coutinho

  • Laboratorio Nacional de Computaçao Cientifica, Petropolis (Brazil), Fabio Porto

  • Universidade Federal Fluminense, Niteroi (Brazil), Daniel Oliveira

  • Centro Federal de Educa cao Tecnologica, Rio de Janeiro (Brazil), Eduardo Ogasawara

• Start year: 2017

• See also: https://team.inria.fr/zenith/scidisc/

• Data-intensive science requires the integration of two fairly different paradigms: high-performance computing (HPC) and data-intensive scalable computing (DISC). Spurred by the growing need to analyze big scientific data, the convergence between HPC and DISC has been a recent topic of interest. This project will address the grand challenge of scientific data analysis using DISC (SciDISC), by developing architectures and methods to combine simulation and data analysis. The expected results of the project are: new data analysis methods for SciDISC systems; the integration of these methods as software libraries in popular DISC systems, such as Apache Spark; and extensive validation on real scientific applications, by working with our scientific partners such as INRA and IRD in France and Petrobras and the National Research Institute (INCT) on e-medicine (MACC) in Brazil.

Participation In other International Programs

We are involved in LifeCLEF lab, a self-organized research platform whose main mission is to promote research, innovation, and development of computer-assisted identification of living organisms. It was initiated by Alexis Joly in 2014 in collaboration with several European colleagues: Henning Müller (CH), Robert B Fisher (UK), Andreas Rauber (AU), Concetto Spampinato (IT), Hervé Glotin (FR). Each year, LifeCLEF releases large-scale experimental data covering tens of thousands of species (plants images, birds audio recordings and fish sub-marine videos). About 100-150 research groups register each year to get access to it and tens of them submit reports describing their conducted research (published in CEUR-WS proceedings). Results are then synthesized and further analyzed in joint research papers.

International Initiatives

• BD-FARM

• Title: Big Data Management and Analytics for Agriculture and Farming

• International Partner (Institution - Laboratory - Researcher):

  • Chubu University - International Digital Earth Applied Science Research Center (IDEAS), Kiyoshi Honda

• Duration: 2016 - 2017

• Start year: 2016

• See also: https://team.inria.fr/zenith/bdfarm-2016-2018-stic-asia/

• World population is still growing and people are living longer and older. World demand for food rises sharply and current growth rates in agriculture are clearly not sufficient. But extreme flood, drought, typhoon etc, caused by climate change, give severe damages on traditional agriculture. Today, an urgent and deep redesign of agriculture is crucial in order to increase production and to reduce environmental impact. In this context, collecting, managing and analyzing dedicated, large, complex, and various datasets (Big Data) will allow improving the understanding of complex mechanisms behind adaptive, yield and crop improvement. Moreover, sustainability will require detailed studies such as the relationships between genotype, phenotype and environment. In other words, data science and ICT for agriculture must help improving production. Moreover, it has to be done while getting properly adapted to soil, climatic and agronomic constraints as well as taking into account the genetic specificities of plants.