Section: Partnerships and Cooperations

National Initiatives

Investissements d’avenir


  • Title: Entrepôt Intelligent pour Big Data hétérogènes. Investissements d’Avenir Développement de l’Economie Numérique.

  • Call: Cloud Computing, num 3 – Big Data.

  • Duration: May 2013 - November 2016

  • Coordinator: Business & Decision Eolas

  • Others partners: Groupement des Mousquetaires, Inria Saclay (OAK EPC), LIG (Hadas and Erods teams), LIRMM (Montpellier), LIFL (Lille).

  • See also: http://www.datalyse.fr/

  • Abstract: Project Datalyse aims at designing and deploying an infrastructure for big data storage, collection, certification, integration, categorisation, enrichment and sharing over very large heterogeneous data sets. It relies on an industrial platform, to be made available on the cloud, and focuses on three flagship applications, showcasing three uses of big data over different data sets:

    • Data-Center Monitoring: The goal of this application is to provide features such as traceability, reporting, optimisation and analysis of abnormal behaviour regarding energy efficiency and security issues. The application will be built with an existing application called ScopeBR (Eolas) and will be deployed in two different green data centers, those of Eolas and GDF SUEZ.

    • ‘Territoire de données ouvertes et liées’: This application aims at extracting and provisioning public open data collected from the city of Grenoble and its suburbs. The goal is to make public data available to third-party application developers and to federate local actors around a single platform.

    • Real-time Business Intelligence for the management and processing of points of sale: this application will focus on real-time data analytics and will be deployed within ‘Groupement des Mousquetaires’ in support of their business intelligence platforms.



  • Title: Compilation of intermediate Languages into Efficient big dAta Runtimes

  • Call: Appel à projets générique 2016 défi ‘Société de l’information et de la communication’ – JCJC

  • Duration: October 2016 – September 2020

  • Coordinator: Pierre Genevès

  • See also: http://tyrex.inria.fr/clear

  • Abstract: This project addresses one fundamental challenge of our time: the construction of effective programming models and compilation techniques for the correct and efficient exploitation of big and linked data. We study high-level specifications of pipelines of data transformations and extraction for producing valuable knowledge from rich and heterogeneous data. We investigate how to synthesize code which is correct and optimized for execution on distributed infrastructures.


  • Title: Mobile Augmented Reality Applications for Smart Cities

  • Call: Persyval Labex (‘Laboratoire d’excellence’).

  • Duration: 2014 – 2017

  • Coordinators: Pierre Genevès and Nabil Layaïda

  • Others partners: NeCS team at GIPSA-Lab laboratory.

  • Abstract: The goal of this project is to increase the relevance and reliability of augmented reality (AR) applications, through three main objectives:

    1. Finding and developing appropriate representations for describing the physical world (3D maps, indoor buildings, ways...), integrated advanced media types (3D, 3D audio, precisely geo-tagged pictures with lat., long. and orientation, video...)

    2. Integrating the different abstraction levels of these data streams (ranging from sensors data to high level rich content such as 3D maps) and bridging the gap with Open Linked Data (the semantic World). This includes opening the way to query the environment (filtering), and adapt AR browsers to users’ capabilities (e.g. blind people). The objective here is to provide an open and scalable platform for mobile-based AR systems (just like the web represents).

    3. Increasing the reliability and accuracy of localization technologies. Robust and high-accuracy localization technologies play a key role in AR applications. Combined with geographical data, they can also be used to identify user-activity patterns, such as walking, running or being in an elevator. The interpretation of sensor values, coupled with different walking models, allows one to ensure the continuity of the localization, both indoor and outdoor. However, dead reckoning based on Inertial Navigation Systems (INS) or Step-and-Heading Systems (SHS) is subject to cumulative errors due to many factors (sensor drift (accelerometers, gyroscopes, etc.), missed steps, bad estimation of the length of each stride, etc.). One objective is to reduce such errors by merging and mixing these approaches with various external signals such as GPS and Wi-Fi or relying on the analyses of user trajectories with the help of a structured map of the environment. Some filtering methods (Kalman Filter, observer, etc.) will be useful to achieve this task.