Projects & Contracts

ALICIA

2014-06-20T10:23:48+00:00

Funding: ANR, February 2014 -- January 2017
Partners: Université Paris Sud, Xerox, UPS/IMT
Coordinator:
Scientific lead : S. Amer-Yahia
Weblink:http://www.fp7cases.eu/
2014-2017

The target of this project is the development of methods for information access and intelligent crowdsourcing in collaboration with Universit{\'e} Paris Sud, LTCI, Xerox, and UPS/IMT. In the context of information access (e.g. search or recommendation), building and maintaining user preference profiles helps applications satisfy diverse preferences. For intelligent crowdsourcing (e.g. data sourcing and micro-task completion), expertise profiles help better assign task to users. In both scenarios, the key challenges are that user preferences and expertise cannot be known in advance; and can rarely be explicitly declared by uses in a reliable or stable way. Consequently, preferences and expertise need to be discovered over time via a learning approach. Our project’s goal is the study of models and algorithms that rely on adaptive learning techniques to improve the effectiveness, performance, and scalability of user-centric applications.

Big join

2014-06-20T10:52:51+00:00

Funding: Grenoble INP and University Joseph Fourrier, Programme AGIR (2013-2015)
Partners:
Coordinator:
Scientific lead : S. Amer Yahia
2013-2015

Modèles et algorithmes pour les jointures de Big Data sur Map-Reduce.

CASES

2012-11-26T13:50:36+00:00

Funding: EU Seventh Framework Programme (FP7), PEOPLE program
Partners: Coventry University, UK (COVUNI) , Universitat Politècnica de València, Spain (UPV) , Cranfield University, UK (CU), Grenoble Institute of Technology, France (GRENOBLE INP) , Zhejiang University, China (ZJU) , Nanjing University of Aerospace and Aeronautics, China (NUAA) , Southeast University, China (SEU) , National Metallurgical Academy of Ukraine, Ukraine (NMAU)
Coordinator: LIG - GRENOBLE INP
Scientific lead : Genoveva Vargas-Solar
Weblink:http://www.fp7cases.eu/
2012-2015

CASES (Customised Advisory Services for Energy-efficient Manufacturing Systems) project aims at teaming up transcontinental researchers in the areas of sustainable manufacturing and information technologies to enrich the knowledge base and achieve research synergies to develop smart design and manufacturing services in terms of energy efficiency. The project integrates the complementary expertise of the European, Chinese and Ukrainian teams to devise ICT-based smart services and standards to address the multi-faceted requirements of global eco-design and sustainable manufacturing planning.

Datalyse

2014-06-20T10:21:43+00:00

Funding: Investissement d'Avenir May 2013 -- November 2016
Partners: INRIA Saclay, LIFL, LIRMM, Eolas
Coordinator:
Scientific lead : M.C. Rousset
Weblink:http://www.fp7cases.eu/
2013-2016

The aim of this project is to develop scalable algorithms for data mining and processing in collaboration with INRIA Saclay, LIFL, LIRMM and industrial partners: Eolas and B\&D. The project defines 3 use cases, all of them with industrial impact. The first use case, network analysis applied to data centers datasets, aims to provide interactive traffic monitoring interfaces including traffic aggregation over time abnormal traffic identification. The second use case, digital marketing, applied to server and application logs, aims to provide customer-centric statistics and customer engagement analysis using sequence mining. The third use case, linked open data, aims to develop a platform that integrates open data on the city of Grenoble and makes it readily available for the development of various applications.

PAGODA

2014-06-20T10:27:01+00:00

Funding: ANR 12 JS02 007 01, Programme JCJC, January 2013 -- December 2016
Partners: LRI, LIRMM, LADAF
Coordinator: Meghyn Bienvenu
Scientific lead : M.C. Rousset
Weblink:http://www.fp7cases.eu/
2013-2016

The aim of this project is to develop practical algorithms for ontology-based data access (OBDA) in collaboration with LRI, LIRMM, and the LADAF (Laboratoire d'anatomie de Grenoble). This project is centered on two challenges:
\textit{(i)} Scalability: in contrast with relational database management systems that benefit from decades of research on querying algorithms and optimizations, ontology-based data access is a young area of study, and despite important recent advances, including the identification of interesting tractable ontology languages, much work remains to be done in designing scalable OBDA query answering algorithms.
\textit{(ii)} Handling data inconsistencies: In real-world applications involving large amounts of data or multiple data sources, it is very likely that the data will be inconsistent with the ontology, rendering standard querying algorithms useless (as everything is entailed from a contradiction). Appropriate mechanisms for dealing with inconsistent data are thus crucial to the successful use of OBDA in practice, yet have been little explored thus far.

QUALINCA

2012-10-15T12:06:59+00:00

Funding: ANR, 2012-2015
Partners: LIG,LIRMM, LRI, ABES
Coordinator: LIRMM
Scientific lead: M.C. Rousset
Weblink: http://www.lirmm.fr/qualinca/?q=en
2012-2015

QUALINCA is a ANR Contint funded research project looking at developing mechanisms allowing to quantify the quality level of a bibliographical knowledge base, to improve the afore mentioned quality level, to maintain the quality when updating the knowledge base and to exploit the knowledge bases taking into account their quality levels.
This project aims to develop mechanisms to:
- describe the quality of an existing document database;
- maintain a given level of quality by controlling updates on such databases;
- improve the quality of a database;
- exploit these databases according to their level of quality.

Smart-Energy

2013-04-30T09:44:30+00:00

Funding: MSTIQ project, 2009
Partners: G-SCOP, G2ELab, gipsa-lab, LIG
Coordinator: G2ELab
Scientific lead : Ch. Collet
Weblink:
2012-2014

This projects aims at federating the scientific communities from Grenoble INP supported laboratories around the development of Smart Grid technologies.
The idea is to identify common interests among researchers to propose new research projects.

SocTrace

2012-10-15T11:58:56+00:00

Funding: FUI-Minalogic, OSEO 2011-2015
Partners: INRIA, LIG, TIMA, STMicroelectronics, Magilem, probayes
Coordinator: STMicroelectronics
Scientific lead : Alexandre Termier
Weblink:
2011-2015

The SoC-Trace project aims to develop a set of methods and tools based on traces of execution produced by multi-core embedded applications. It will allow developers to optimize and debug these applications more efficiently. Such methods and tools should become a building block for the design of embedded software, in response to the growing needs of analysis and debugging required by the industry. The technological barriers consist of a scaling problem (millions of events stored on gigabytes) and a trace understanding problem related to applications whose complexity is increasing. The project addresses the problem of controlling the volume of tracks and of developing new analysis techniques. SocTrace is composed of academic partners with related themes, and several industry partners including STMicroelectronics.

SoGrid

2013-04-30T07:48:55+00:00

Funding: ADEME, Le réseau électrique de demain
Partners: Grenoble INP (LIG), l'École polytechnique (LIX), ERDF, STMicroelectronics, Nexans, Sagemcom, Landis+Gyr, Capgemini, Trialog, LAN
Coordinator: ERDF et STMicroelectronics
Scientific lead : Ch. Collet
Weblink:
2013-2017

SoGrid aims at confirming the path opened by ERDF in the technological revolution of the Smart Grid. The goal is to develop a global communication chain linking all components as the basis of future Smart Grids. By deploying intelligence all along this communication chain, SoGrid will allow (i) real-time supervision and control of the electrical grid; (ii) integration of decentralized renewable energy production; (iii) anticipation and support of new uses of electricity, in particular electric vehicles; (iv) the possibility to ensure that at every moment the balance between production and consumption, particularly during peak consumption; and (v) Control of consumption by the end-user and better quality of service. HADAS group is developing new optimized distributed and adaptable event stream management techniques taking into account the specificities of the Smart Grids.