Section: Partnerships and Cooperations

International Initiatives

The team has two PhD students funded by an Algerian initiative ("Bourses d'excellence Algériennes "):

• Khadidja Meguelati, since 2016, "Massively Distributed Time Series Clustering via Dirichlet Mixture Models"

• Lamia Djebour, since 2019, "Parallel Time Series Indexing and Retrieval with GPU architectures"

Inria International Labs

In the context of LIRIMA, P. Valduriez gave a one week course in big data at IMSP, Bénin, in march, and an online seminar on Blockchain on 13 dec at Inria Rennes.

Inria Associate Teams Not Involved in an Inria International Labs

SciDISC

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

• International Partner (Institution - Laboratory - Researcher):

  • Universidade Federal do Rio de Janeiro (Brazil) - Computer Laboratory - Marta Mattoso

• 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 [[Coutinho 2014, Valduriez 2015]. 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.

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), Northwestern Univ. (Chicago), university of Florida (Pamela Soltis, Cheryl Porter, Gil Nelson), Harvard (Charles Davis), UCSB (Susan Mazer).

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

• Europe: Univ. of Madrid (Ricardo Jiménez-Periz), UPC Barcelona (Josep Lluis Larriba Pey), HES-SO (Henning Müller), University of Catania (Concetto Spampinatto), Cork School of Music (Ireland), RWTH (Aachen, Germany), Chemnitz technical university (Stefan Kahl), Berlin Museum für Naturkunde (Mario Lasseck), Stefanos Vrochidis (Greece, ITI), UK center for hydrology and ecology (Tom August)

• Africa: Univ. of Tunis (Sadok Ben-Yahia), IMSP, Bénin (Jules Deliga)

• Australia: Australian National University (Peter Christen)

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

Participation in Other International Programs

Inria International Chairs

• Dennis Shasha (NYU)

• Title: Data Science in a Dynamic World

• International Partner: New York University (NYU), USA

• Duration: 2015 - 2019

• Start year: 2015

• Many fundamental problems in natural science from astronomy to microbiology require data from heterogeneous sources, hence giving rise to a new “data science”. The basic workflow is to collect that data, form some kind of similarity metric between objects based on each data source, and then weight those different similarity metrics for some data analysis task. The goal is to gain actionable insight such as the cause of some symptoms, the function of some protein, or the likely source of some epidemic. Most often this is conceived of as "do-it-once" exercise. However, as data acquisition techniques improve, data may evolve continuously. When that happens the question is whether new revised insights can be obtained in a close to real time manner. Whether this is possible depends on the qualities of the new data, the weighting of the data sources, and the machine learning algorithms used. This project addresses data science in a dynamic world, aiming to find fast and minimalist methods to update insights as new data appears. This will result in new data management algorithms that will be implemented in tools and validated in the context of real data, in particular biology data.

Visits of International Scientists

• Renan Souza (COPPE/UFRJ and IBM,Brazil): “Providing Online Data Analytical Support for Humans in the Loop of Computational Science and Engineering Applications” on Jan 15.

• Youcef Djenouri (Norwegian University of Science and Technology, Trondheim): “Urban traffic outlier detection” on Feb 14.

• Dennis Shasha (NYU) “Bounce Blockchain: a secure, energy-efficient permission less blockchain” on May 27.

• Alvaro Coutinho (COPPE/UFRJ, Brazil): “Some Reflections on Predictive Science in Geophysical Applications” on Nov 20.

• Marta Mattoso (COPPE/UFRJ, Brazil): “Adding Provenance Data to Experiments: From Computational Science to Deep Learning” on Nov 20.

• Eduardo Ogasawara, (CEFET-RJ, Brazil): “Event Detection in Time Series” on Nov 20.

• Heraldo Borges (CEFET-RJ, Brazil): “Discovering Patterns in Restricted Space-Time Datasets” on Nov 20.