ALICIA
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
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
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
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
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.