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13 results on '"University of California-University of California [Irvine] (UCI)"'

1. geomstats: a Python Package for Riemannian Geometry in Machine Learning

Author

Miolane, Nina, Mathe, Johan, Donnat, Claire, Jorda, Mikael, Pennec, Xavier, E-Patient : Images, données & mOdèles pour la médeciNe numériquE (EPIONE), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Inria@SiliconValley (Inria@SiliconValley), Stanford University-University of Southern California (USC)-Institut National de Recherche en Informatique et en Automatique (Inria)-University of California [Santa Barbara] (UCSB), University of California-University of California-University of California [San Diego] (UC San Diego), University of California-Ministère de l'Europe et des Affaires étrangères (MEAE)-CITRIS-University of California [Santa Cruz] (UCSC), University of California-University of California [Irvine] (UCI), University of California, Frog labs AI San Francisco, Stanford University, COMUE Université Côte d'Azur (2015-2019) (COMUE UCA), Inria@SiliconValley, GeomStats, Stanford University-University of California [Santa Cruz] (UC Santa Cruz), University of California (UC)-University of California (UC)-Institut National de Recherche en Informatique et en Automatique (Inria)-University of California [Santa Barbara] (UC Santa Barbara), University of California (UC)-University of California [San Diego] (UC San Diego), University of California (UC)-Ministère de l'Europe et des Affaires étrangères (MEAE)-University of Southern California (USC)-CITRIS-University of California [Irvine] (UC Irvine), and University of California (UC)

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Statistics - Machine Learning, [MATH.MATH-DG]Mathematics [math]/Differential Geometry [math.DG], Machine Learning (stat.ML), Computer Science - Mathematical Software, Mathematics::Differential Geometry, Mathematical Software (cs.MS), Machine Learning (cs.LG), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-MS]Computer Science [cs]/Mathematical Software [cs.MS]
Abstract

We introduce geomstats, a python package that performs computations on manifolds such as hyperspheres, hyperbolic spaces, spaces of symmetric positive definite matrices and Lie groups of transformations. We provide efficient and extensively unit-tested implementations of these manifolds, together with useful Riemannian metrics and associated Exponential and Logarithm maps. The corresponding geodesic distances provide a range of intuitive choices of Machine Learning loss functions. We also give the corresponding Riemannian gradients. The operations implemented in geomstats are available with different computing backends such as numpy, tensorflow and keras. We have enabled GPU implementation and integrated geomstats manifold computations into keras deep learning framework. This paper also presents a review of manifolds in machine learning and an overview of the geomstats package with examples demonstrating its use for efficient and user-friendly Riemannian geometry., Preprint NIPS2018

Published
2019

2. Leveraging the Power of the Crowd and Offloading Urban IoT Networks to Extend their Lifetime

Author

Valérie Issarny, Geraldine Texier, Advanced technologies for operated networks ( ADOPNET ), Université de Rennes 1 ( UR1 ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -IMT Atlantique Bretagne-Pays de la Loire ( IMT Atlantique ) -RÉSEAUX, TÉLÉCOMMUNICATION ET SERVICES ( IRISA_D2 ), Institut de Recherche en Informatique et Systèmes Aléatoires ( IRISA ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Institut National des Sciences Appliquées - Rennes ( INSA Rennes ) -Université de Bretagne Sud ( UBS ) -École normale supérieure - Rennes ( ENS Rennes ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ) -IMT Atlantique Bretagne-Pays de la Loire ( IMT Atlantique ) -Université de Rennes 1 ( UR1 ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Institut National des Sciences Appliquées - Rennes ( INSA Rennes ) -Université de Bretagne Sud ( UBS ) -École normale supérieure - Rennes ( ENS Rennes ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ) -IMT Atlantique Bretagne-Pays de la Loire ( IMT Atlantique ) -Institut de Recherche en Informatique et Systèmes Aléatoires ( IRISA ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Institut National des Sciences Appliquées - Rennes ( INSA Rennes ) -Université de Bretagne Sud ( UBS ) -École normale supérieure - Rennes ( ENS Rennes ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ) -IMT Atlantique Bretagne-Pays de la Loire ( IMT Atlantique ) -Institut National des Sciences Appliquées - Rennes ( INSA Rennes ) -Université de Bretagne Sud ( UBS ) -École normale supérieure - Rennes ( ENS Rennes ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ), Département Systèmes Réseaux, Cybersécurité et Droit du numérique ( SRCD ), IMT Atlantique Bretagne-Pays de la Loire ( IMT Atlantique ), Inria@SiliconValley (INRIA) ( Inria@SiliconValley ), INRIA Rocquencourt (INRIA), Département Systèmes Réseaux, Cybersécurité et Droit du numérique (IMT Atlantique - SRCD), IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Advanced technologies for operated networks (ADOPNET), Université de Rennes (UR)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-RÉSEAUX, TÉLÉCOMMUNICATION ET SERVICES (IRISA-D2), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Inria@SiliconValley (Inria@SiliconValley), Stanford University-University of California [Santa Cruz] (UC Santa Cruz), University of California (UC)-University of California (UC)-Institut National de Recherche en Informatique et en Automatique (Inria)-University of California [Santa Barbara] (UC Santa Barbara), University of California (UC)-University of California [San Diego] (UC San Diego), University of California (UC)-Ministère de l'Europe et des Affaires étrangères (MEAE)-University of Southern California (USC)-CITRIS-University of California [Irvine] (UC Irvine), University of California (UC), Middleware on the Move (MIMOVE), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec, Stanford University-University of Southern California (USC)-Institut National de Recherche en Informatique et en Automatique (Inria)-University of California [Santa Barbara] (UCSB), University of California-University of California-University of California [San Diego] (UC San Diego), University of California-Ministère de l'Europe et des Affaires étrangères (MEAE)-CITRIS-University of California [Santa Cruz] (UCSC), University of California-University of California [Irvine] (UCI), University of California, Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-RÉSEAUX, TÉLÉCOMMUNICATION ET SERVICES (IRISA-D2), and Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique)

Subjects
Discrete mathematics, IoT, Computer science, business.industry, [ INFO.INFO-NI ] Computer Science [cs]/Networking and Internet Architecture [cs.NI], 05 social sciences, Crowdsensing, 050801 communication & media studies, 020206 networking & telecommunications, 02 engineering and technology, WSN, Power (physics), [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], Mobile sink, 0508 media and communications, [ INFO.INFO-MC ] Computer Science [cs]/Mobile Computing, 11. Sustainability, 0202 electrical engineering, electronic engineering, information engineering, Internet of Things, business, Urban space, Wireless sensor network, Routing
Abstract

The widespread deployment of connected Things in our cities allows for the enhanced management of the urban space. However, due to environmental constraints, the connected Things are often battery-powered, which affects the lifetime of the $\mathrm {I}\mathrm {o}\mathrm {T}$ Networks. We believe that the urban $\mathrm {I}\mathrm {o}\mathrm {T}$ networks must leverage the power of the crowd, leading to combine the fixed urban $\mathrm {I}\mathrm {o}\mathrm {T}$ networks with the crowdsensing capabilities of the citizens' smartphones. That is, we integrate the offloading of a portion of the traffic generated by the fixed network to mobile crowdsensing devices that also contribute with relevant urban observations (e.g., for noise pollution monitoring). This paper highlights the key technical features of the resulting solution, whose evaluation shows that we are able to extend the lifetime of the battery-operated $\mathrm {I}\mathrm {o}\mathrm {T}$ network by a up to 7 factor.

Published
2018
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3. Matching Technological & Societal Innovations: The Social Design of a Mobile Collaborative App for Urban Noise Monitoring

Author

Lefèvre, Bruno, Issarny, Valerie, Middleware on the Move (MIMOVE), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Inria@SiliconValley (Inria@SiliconValley), Stanford University-University of Southern California (USC)-Institut National de Recherche en Informatique et en Automatique (Inria)-University of California [Santa Barbara] (UCSB), University of California-University of California-University of California [San Diego] (UC San Diego), University of California-Ministère de l'Europe et des Affaires étrangères (MEAE)-CITRIS-University of California [Santa Cruz] (UCSC), University of California-University of California [Irvine] (UCI), University of California, EIT Digital, and Inria IPL CityLab

Subjects
[INFO.INFO-IU]Computer Science [cs]/Ubiquitous Computing, IoT, [INFO.INFO-MC]Computer Science [cs]/Mobile Computing, Noise data, Social design, Collaborative digital science, Crowd- sourcing, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], Mobile Phone Sensing, [SHS.ANTHRO-SE]Humanities and Social Sciences/Social Anthropology and ethnology, [SDE.ES]Environmental Sciences/Environmental and Society, [INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI], Smart cities
Abstract

International audience; Mobile Phone Sensing offers a great opportunity toward the large scale monitoring of urban phenomena, such as the exposure of the population to environmental noise. Our research aims to make available a supporting mobile application together with the associated platform for the analysis of the contributed observations. The technological issues arising have been partly solved but a gap remains between the need for the massive collection of relevant data, and the quantity and accuracy of the measurements that are actually gathered. This paper presents our iterative research process to tackle this challenge, which combines technological innovation and social design. The presented research results contribute to a better understanding of why and how people use mobile phone sensing applications; the results also inform how to best leverage mobile crowd-sensing in the development of smart cities and how it may serve addressing urban challenges related to, e.g., public health or urban planning.; Le recueil de données via les smartphones ouvre des perspectives importantes pour le suivi à grande échelle de phénomènes urbains, dont l'exposition de la population au bruit. Notre recherche vise à rendre disponible une application pour smartphone et sa plateforme associée, pour l'analyse et la gestion des observations recueillies. Si les défis technologiques ont en partie été levés, un écart significatif persiste entre, d'une part la nécessité d'un recueil massif de données significatives et, d'autre part la quantité et la précision des données de mesure actuellement produites et récoltées. Cet article présente notre processus de recherche itératif qui, afin de relever ces défis, combine innovations technologiques et design social. Les résultats présentés ici contribuent à une meilleure compréhension des raisons et conditions dans lesquelles le public utilise des applications mobiles de recueil de données; les résultats nous informent également sur les possibilités d'amélioration du crowd-sensing via smartphones pour le développement de projets de villes intelligentes et pour la santé publique et l'aménagement urbain.

Published
2018

4. Faire concorder innovations technologiques et sociétales : le design social d'une application collaborative pour le suivi du bruit en milieu urbain

Author

Bruno Lefevre, Valérie Issarny, Middleware on the Move (MIMOVE), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Inria@SiliconValley (Inria@SiliconValley), Stanford University-University of Southern California (USC)-Institut National de Recherche en Informatique et en Automatique (Inria)-University of California [Santa Barbara] (UCSB), University of California-University of California-University of California [San Diego] (UC San Diego), University of California-Ministère de l'Europe et des Affaires étrangères (MEAE)-CITRIS-University of California [Santa Cruz] (UCSC), University of California-University of California [Irvine] (UCI), University of California, EIT Digital, Inria IPL CityLab, Stanford University-University of California [Santa Cruz] (UC Santa Cruz), University of California (UC)-University of California (UC)-Institut National de Recherche en Informatique et en Automatique (Inria)-University of California [Santa Barbara] (UC Santa Barbara), University of California (UC)-University of California [San Diego] (UC San Diego), University of California (UC)-Ministère de l'Europe et des Affaires étrangères (MEAE)-University of Southern California (USC)-CITRIS-University of California [Irvine] (UC Irvine), and University of California (UC)

Subjects
Matching (statistics), IoT, Noise data, Computer science, Population, 02 engineering and technology, [INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI], [INFO.INFO-IU]Computer Science [cs]/Ubiquitous Computing, [INFO.INFO-MC]Computer Science [cs]/Mobile Computing, Urban planning, 0502 economics and business, 11. Sustainability, 0202 electrical engineering, electronic engineering, information engineering, Social design, Leverage (statistics), [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], Mobile Phone Sensing, Environmental noise, education, education.field_of_study, 05 social sciences, Crowd- sourcing, 020206 networking & telecommunications, [SHS.ANTHRO-SE]Humanities and Social Sciences/Social Anthropology and ethnology, Data science, [SDE.ES]Environmental Sciences/Environmental and Society, 13. Climate action, Mobile phone, Scale (social sciences), Collaborative digital science, 050203 business & management, Smart cities
Abstract

International audience; Mobile Phone Sensing offers a great opportunity toward the large scale monitoring of urban phenomena, such as the exposure of the population to environmental noise. Our research aims to make available a supporting mobile application together with the associated platform for the analysis of the contributed observations. The technological issues arising have been partly solved but a gap remains between the need for the massive collection of relevant data, and the quantity and accuracy of the measurements that are actually gathered. This paper presents our iterative research process to tackle this challenge, which combines technological innovation and social design. The presented research results contribute to a better understanding of why and how people use mobile phone sensing applications; the results also inform how to best leverage mobile crowd-sensing in the development of smart cities and how it may serve addressing urban challenges related to, e.g., public health or urban planning.; Le recueil de données via les smartphones ouvre des perspectives importantes pour le suivi à grande échelle de phénomènes urbains, dont l'exposition de la population au bruit. Notre recherche vise à rendre disponible une application pour smartphone et sa plateforme associée, pour l'analyse et la gestion des observations recueillies. Si les défis technologiques ont en partie été levés, un écart significatif persiste entre, d'une part la nécessité d'un recueil massif de données significatives et, d'autre part la quantité et la précision des données de mesure actuellement produites et récoltées. Cet article présente notre processus de recherche itératif qui, afin de relever ces défis, combine innovations technologiques et design social. Les résultats présentés ici contribuent à une meilleure compréhension des raisons et conditions dans lesquelles le public utilise des applications mobiles de recueil de données; les résultats nous informent également sur les possibilités d'amélioration du crowd-sensing via smartphones pour le développement de projets de villes intelligentes et pour la santé publique et l'aménagement urbain.

Published
2018

5. Composing Continuous Services in a CoAP-based IoT

Author

Valérie Issarny, Geraldine Texier, Benjamin Billet, Middleware on the Move ( MIMOVE ), Inria Paris-Rocquencourt, Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ), Inria@SiliconValley ( Inria@SiliconValley ), Stanford University [Stanford]-University of California [Santa Cruz] ( UCSC ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -University of California [Berkeley]-University of California [Santa Barbara]-University of California [San Diego] ( UC San Diego ) -Ministère de l'Europe et des Affaires étrangères ( MEAE ) -University of Southern California ( USC ) -CITRIS-University of California [Irvine] ( UCI ), Advanced technologies for operated networks ( ADOPNET ), Université de Rennes 1 ( UR1 ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -IMT Atlantique Bretagne-Pays de la Loire ( IMT Atlantique ) -RÉSEAUX, TÉLÉCOMMUNICATION ET SERVICES ( IRISA_D2 ), Institut de Recherche en Informatique et Systèmes Aléatoires ( IRISA ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Institut National des Sciences Appliquées - Rennes ( INSA Rennes ) -Université de Bretagne Sud ( UBS ) -École normale supérieure - Rennes ( ENS Rennes ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ) -IMT Atlantique Bretagne-Pays de la Loire ( IMT Atlantique ) -Université de Rennes 1 ( UR1 ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Institut National des Sciences Appliquées - Rennes ( INSA Rennes ) -Université de Bretagne Sud ( UBS ) -École normale supérieure - Rennes ( ENS Rennes ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ) -IMT Atlantique Bretagne-Pays de la Loire ( IMT Atlantique ) -Institut de Recherche en Informatique et Systèmes Aléatoires ( IRISA ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Institut National des Sciences Appliquées - Rennes ( INSA Rennes ) -Université de Bretagne Sud ( UBS ) -École normale supérieure - Rennes ( ENS Rennes ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ) -IMT Atlantique Bretagne-Pays de la Loire ( IMT Atlantique ) -Institut National des Sciences Appliquées - Rennes ( INSA Rennes ) -Université de Bretagne Sud ( UBS ) -École normale supérieure - Rennes ( ENS Rennes ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ), Inria@SiliconValley, Inria IPL CityLab, Middleware on the Move (MIMOVE), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Inria@SiliconValley (Inria@SiliconValley), Stanford University-University of Southern California (USC)-Institut National de Recherche en Informatique et en Automatique (Inria)-University of California [Santa Barbara] (UCSB), University of California-University of California-University of California [San Diego] (UC San Diego), University of California-Ministère de l'Europe et des Affaires étrangères (MEAE)-CITRIS-University of California [Santa Cruz] (UCSC), University of California-University of California [Irvine] (UCI), University of California, Advanced technologies for operated networks (ADOPNET), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-RÉSEAUX, TÉLÉCOMMUNICATION ET SERVICES (IRISA-D2), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Stanford University-University of California [Santa Cruz] (UC Santa Cruz), University of California (UC)-University of California (UC)-Institut National de Recherche en Informatique et en Automatique (Inria)-University of California [Santa Barbara] (UC Santa Barbara), University of California (UC)-University of California [San Diego] (UC San Diego), University of California (UC)-Ministère de l'Europe et des Affaires étrangères (MEAE)-University of Southern California (USC)-CITRIS-University of California [Irvine] (UC Irvine), University of California (UC), Université de Rennes (UR)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-RÉSEAUX, TÉLÉCOMMUNICATION ET SERVICES (IRISA-D2), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), and Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec

Subjects
Engineering, IoT, computer.internet_protocol, Distributed computing, [ INFO.INFO-NI ] Computer Science [cs]/Networking and Internet Architecture [cs.NI], [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], [ INFO.INFO-IU ] Computer Science [cs]/Ubiquitous Computing, 02 engineering and technology, computer.software_genre, [INFO.INFO-IU]Computer Science [cs]/Ubiquitous Computing, [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], middleware, 0202 electrical engineering, electronic engineering, information engineering, Leverage (statistics), Reference architecture, streaming, Architecture, business.industry, 020206 networking & telecommunications, 020207 software engineering, Service-oriented architecture, Software deployment, Applications architecture, Middleware (distributed applications), [ INFO.INFO-OH ] Computer Science [cs]/Other [cs.OH], business, Wireless sensor network, computer
Abstract

International audience; We revisit core Service-Oriented Architecture –SOA– concepts to integrate advanced continuous processing techniques. The resulting architecture allows overcoming the high heterogeneity and the very large data scale that arise in the Internet of Things. Further, we discuss how to leverage the CoAP protocol so as to enable the resource-efficient deployment of the proposed architecture over the large diversity of sensor networks.

Published
2017

6. Spinel: An Opportunistic Proxy for Connecting Sensors to the Internet of Things

Author

Benjamin Billet, Valérie Issarny, Middleware on the Move (MIMOVE), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Inria@SiliconValley (Inria@SiliconValley), Stanford University-University of Southern California (USC)-Institut National de Recherche en Informatique et en Automatique (Inria)-University of California [Santa Barbara] (UCSB), University of California-University of California-University of California [San Diego] (UC San Diego), University of California-Ministère de l'Europe et des Affaires étrangères (MEAE)-CITRIS-University of California [Santa Cruz] (UCSC), University of California-University of California [Irvine] (UCI), University of California, Inria@SiliconValley, Stanford University-University of California [Santa Cruz] (UC Santa Cruz), University of California (UC)-University of California (UC)-Institut National de Recherche en Informatique et en Automatique (Inria)-University of California [Santa Barbara] (UC Santa Barbara), University of California (UC)-University of California [San Diego] (UC San Diego), University of California (UC)-Ministère de l'Europe et des Affaires étrangères (MEAE)-University of Southern California (USC)-CITRIS-University of California [Irvine] (UC Irvine), and University of California (UC)

Subjects
Opportunistic Proxy, Computer Networks and Communications, Computer science, Reliability (computer networking), Opportunistic Networking, Internet of Things, Context (language use), 02 engineering and technology, computer.software_genre, Computer security, [INFO.INFO-IU]Computer Science [cs]/Ubiquitous Computing, [INFO.INFO-MC]Computer Science [cs]/Mobile Computing, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Middleware, business.industry, 020206 networking & telecommunications, Open data, Software deployment, Middleware (distributed applications), The Internet, 6LoWPAN, business, computer, Wireless sensor network, Wireless Sensor Network, Computer network
Abstract

Nowadays, various static wireless sensor networks (WSN) are deployed in the environment for many purposes: traffic control, pollution monitoring, and so on. The willingness to open these legacy WSNs to the users is emerging, by integrating them to the Internet network as part of the future Internet of Things (IoT), for example, in the context of smart cities and open data policies. While legacy sensors cannot be directly connected to the Internet in general, emerging standards such as 6LoWPAN are aimed at solving this issue but require us to update or replace the existing devices. As a solution to connect legacy sensors to the IoT, we propose to take advantage of the multi-modal connectivity as well as the mobility of smartphones to use phones as opportunistic proxies , that is, mobile proxies that opportunistically discover closeby static sensors and act as intermediaries with the IoT, with the additional benefit of bringing fresh information about the environment to the smartphones’ owners. However, this requires us to monitor the smartphone’s mobility and further infer when to discover and register the sensors to guarantee the efficiency and reliability of opportunistic proxies. To that end, we introduce and evaluate an approach based on mobility analysis that uses a novel path prediction technique to predict when and where the user is not moving, and thereby serves to anticipate the registration of sensors within communication range. We show that this technique enables the deployment of low-cost resource-efficient mobile proxies to connect legacy WSNs with the IoT.

Published
2017
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7. Dos and Don'ts in Mobile Phone Sensing Middleware: Learning from a Large-Scale Experiment

Author

Vivien Mallet, Kinh Nguyen, Fadwa Rebhi, Valérie Issarny, Pierre-Guillaume Raverdy, Raphaël Ventura, Inria@SiliconValley (Inria@SiliconValley), Stanford University-University of Southern California (USC)-Institut National de Recherche en Informatique et en Automatique (Inria)-University of California [Santa Barbara] (UCSB), University of California-University of California-University of California [San Diego] (UC San Diego), University of California-Ministère de l'Europe et des Affaires étrangères (MEAE)-CITRIS-University of California [Santa Cruz] (UCSC), University of California-University of California [Irvine] (UCI), University of California, Middleware on the Move (MIMOVE), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Coupling environmental data and simulation models for software integration (CLIME), Inria IPL CityLab, Inria@SiliconValley, Stanford University-University of California [Santa Cruz] (UC Santa Cruz), University of California (UC)-University of California (UC)-Institut National de Recherche en Informatique et en Automatique (Inria)-University of California [Santa Barbara] (UC Santa Barbara), University of California (UC)-University of California [San Diego] (UC San Diego), University of California (UC)-Ministère de l'Europe et des Affaires étrangères (MEAE)-University of Southern California (USC)-CITRIS-University of California [Irvine] (UC Irvine), and University of California (UC)

Subjects
Mobile phone sensing, Computer science, Sensors heterogeneity, Urban sensing, 020206 networking & telecommunications, 020207 software engineering, 02 engineering and technology, Computer security, computer.software_genre, Data science, Crowd-sensing, Sensing accuracy, [INFO.INFO-IU]Computer Science [cs]/Ubiquitous Computing, Empirical research, 13. Climate action, Phone, Mobile phone, Order (exchange), Middleware, Scale (social sciences), 11. Sustainability, 0202 electrical engineering, electronic engineering, information engineering, Key (cryptography), Dimension (data warehouse), computer
Abstract

International audience; Mobile phone sensing contributes to changing the way we approach science: massive amount of data is being contributed across places and time, and paves the way for advanced analyses of numerous phenomena at an unprecedented scale. Still, despite the extensive research work on enabling resource-efficient mobile phone sensing with a very-large crowd, key challenges remain. One challenge is facing the introduction of a new heterogeneity dimension in the traditional middleware research landscape. The middleware must deal with the heterogeneity of the contributing crowd in addition to the system's technical heterogeneities. In order to tackle these two heterogeneity dimensions together, we have been conducting a large-scale empirical study in cooperation with the city of Paris. Our experiment revolves around the public release of a mobile app for urban pollution monitoring that builds upon a dedicated mobile crowd-sensing middleware. In this paper, we report on the empirical analysis of the resulting mobile phone sensing efficiency from both technical and social perspectives, in face of a large and highly heterogeneous population of participants. We concentrate on the data originating from the 20 most popular phone models of our user base, which represent contributions from over 2,000 users with 23 million observations collected over 10 months. Following our analysis, we introduce a few recommendations to overcome-technical and crowd-heterogeneities in the implementation of mobile phone sensing applications and supporting middleware.

Published
2016
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8. Engineering software assemblies for participatory democracy

Author

James Holston, Cristhian Parra, Valérie Issarny, Lawrence Berkeley National Laboratory [Berkeley] (LBNL), Inria@SiliconValley (Inria@SiliconValley), Stanford University-University of Southern California (USC)-Institut National de Recherche en Informatique et en Automatique (Inria)-University of California [Santa Barbara] (UCSB), University of California-University of California-University of California [San Diego] (UC San Diego), University of California-Ministère de l'Europe et des Affaires étrangères (MEAE)-CITRIS-University of California [Santa Cruz] (UCSC), University of California-University of California [Irvine] (UCI), University of California, Middleware on the Move (MIMOVE), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Inria IPL CityLab, Inria@SiliconValley, MIMOVE, Stanford University-University of California [Santa Cruz] (UC Santa Cruz), University of California (UC)-University of California (UC)-Institut National de Recherche en Informatique et en Automatique (Inria)-University of California [Santa Barbara] (UC Santa Barbara), University of California (UC)-University of California [San Diego] (UC San Diego), University of California (UC)-Ministère de l'Europe et des Affaires étrangères (MEAE)-University of Southern California (USC)-CITRIS-University of California [Irvine] (UC Irvine), and University of California (UC)

Subjects
Engineering, Knowledge management, Deliberation, media_common.quotation_subject, Participatory action research, [INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE], 02 engineering and technology, Collective action, [INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI], [INFO.INFO-IU]Computer Science [cs]/Ubiquitous Computing, Participatory GIS, [INFO.INFO-CY]Computer Science [cs]/Computers and Society [cs.CY], 050602 political science & public administration, 0202 electrical engineering, electronic engineering, information engineering, Collaborative pro-posal making, Software system, Online participatory democracy, media_common, Government, business.industry, Versioning, 05 social sciences, 020207 software engineering, Public relations, Democracy, 0506 political science, Software composition, Information and Communications Technology, Demo-cratic assembly, ComputerSystemsOrganization_MISCELLANEOUS, Participatory budgeting, Voting, business
Abstract

The worldwide use of the Internet and social networking has transformed the constraints of time and space in human interaction: we can now be heard at a massive scale unprecedented in human history. As a result, information and communication technologies may enable citizens to undertake both government through direct assembly and collective action at a scale and an efficacy previously considered impossible. Our research concerns this opportunity to leverage a new sort of political life. We focus specifically on how software systems may enable participatory democracy, that is, the participation of citizens in democratic assembly, action, and governance. As an initial step, we have developed a service-oriented software platform, called AppCivist-PB, focused on a specific, yet representative use case of participatory democracy, namely, Participatory Budgeting (PB for short). PB is an allocation process used in many cities around the world through which they commit a percentage of their annual budget (often 5%) to implement citizen-proposed projects. In PB, residents of a city (or a higher level territorial organization), brainstorm, develop, and select project proposals that local government institutions are required to fund and implement. The key contribution of AppCivist-PB is to enable the cohesive creation of both citizen and software assemblies that together implement a given participatory budgeting campaign.

Published
2016
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9. Functional Dependencies Unleashed for Scalable Data Exchange

Author

Ioana Ileana, Michele Linardi, Angela Bonifati, Université Claude Bernard Lyon 1 (UCBL), Université de Lyon, Base de Données (BD), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Inria@SiliconValley (Inria@SiliconValley), Stanford University-University of California [Santa Cruz] (UC Santa Cruz), University of California (UC)-University of California (UC)-Institut National de Recherche en Informatique et en Automatique (Inria)-University of California [Santa Barbara] (UC Santa Barbara), University of California (UC)-University of California [San Diego] (UC San Diego), University of California (UC)-Ministère de l'Europe et des Affaires étrangères (MEAE)-University of Southern California (USC)-CITRIS-University of California [Irvine] (UC Irvine), University of California (UC), Department of Computer Science and Engineering [Univ California San Diego] (CSE - UC San Diego), University of California [San Diego] (UC San Diego), University of California (UC)-University of California (UC), Université Paris Descartes - Paris 5 (UPD5), ACM, Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA), Stanford University [Stanford]-University of California [Santa Cruz] (UCSC), University of California-University of California-Institut National de Recherche en Informatique et en Automatique (Inria)-University of California [Santa Barbara] (UCSB), University of California-University of California [San Diego] (UC San Diego), University of California-Ministère de l'Europe et des Affaires étrangères (MEAE)-University of Southern California (USC)-CITRIS-University of California [Irvine] (UCI), University of California, Database group, Computer Science and engeenering Department [San Diego] (DB CSE UCSD), University of California-University of California, Université Claude Bernard Lyon 1 ( UCBL ), Base de Données ( BD ), Laboratoire d'InfoRmatique en Image et Systèmes d'information ( LIRIS ), Université Lumière - Lyon 2 ( UL2 ) -École Centrale de Lyon ( ECL ), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Centre National de la Recherche Scientifique ( CNRS ) -Institut National des Sciences Appliquées de Lyon ( INSA Lyon ), Université de Lyon-Institut National des Sciences Appliquées ( INSA ) -Institut National des Sciences Appliquées ( INSA ) -Université Lumière - Lyon 2 ( UL2 ) -École Centrale de Lyon ( ECL ), Université de Lyon-Institut National des Sciences Appliquées ( INSA ) -Institut National des Sciences Appliquées ( INSA ), Inria@SiliconValley ( Inria@SiliconValley ), Stanford University [Stanford]-University of California [Santa Cruz] ( UCSC ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -University of California [Berkeley]-University of California [Santa Barbara]-University of California [San Diego] ( UC San Diego ) -Ministère de l'Europe et des Affaires étrangères ( MEAE ) -University of Southern California ( USC ) -CITRIS-University of California [Irvine] ( UCI ), Database group, Computer Science and engeenering Department [San Diego] ( DB CSE UCSD ), University of California [San Diego] ( UC San Diego ), Université Paris Descartes - Paris 5 ( UPD5 ), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Stanford University-University of Southern California (USC)-Institut National de Recherche en Informatique et en Automatique (Inria)-University of California [Santa Barbara] (UCSB), University of California-University of California-University of California [San Diego] (UC San Diego), University of California-Ministère de l'Europe et des Affaires étrangères (MEAE)-CITRIS-University of California [Santa Cruz] (UCSC), and University of California-University of California [Irvine] (UCI)

Subjects
FOS: Computer and information sciences, Theoretical computer science, Chase algorithm, Interleaving, Computer science, ACM : H.: Information Systems/H.2: DATABASE MANAGEMENT/H.2.5: Heterogeneous Databases/H.2.5.0: Data translation, 02 engineering and technology, Bottleneck, Computer Science - Databases, Functional Dependencies, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Direct consequence, ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, Chase Algorithm, Data Exchange, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT/H.2.5: Heterogeneous Databases/H.2.5.0: Data translation, Databases (cs.DB), ACM : H.: Information Systems/H.2: DATABASE MANAGEMENT/H.2.5: Heterogeneous Databases, Multiple source, ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT/H.2.5: Heterogeneous Databases, [ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB], Data exchange, Efficient constraint enforcement, Scalability, 020201 artificial intelligence & image processing, Functional dependency, ACM : H.: Information Systems/H.2: DATABASE MANAGEMENT
Abstract

We address the problem of efficiently evaluating target functional dependencies (fds) in the Data Exchange (DE) process. Target fds naturally occur in many DE scenarios, including the ones in Life Sciences in which multiple source relations need to be structured under a constrained target schema. However, despite their wide use, target fds' evaluation is still a bottleneck in the state-of-the-art DE engines. Systems relying on an all-SQL approach typically do not support target fds unless additional information is provided. Alternatively, DE engines that do include these dependencies typically pay the price of a significant drop in performance and scalability. In this paper, we present a novel chase-based algorithm that can efficiently handle arbitrary fds on the target. Our approach essentially relies on exploiting the interactions between source-to-target (s-t) tuple-generating dependencies (tgds) and target fds. This allows us to tame the size of the intermediate chase results, by playing on a careful ordering of chase steps interleaving fds and (chosen) tgds. As a direct consequence, we importantly diminish the fd application scope, often a central cause of the dramatic overhead induced by target fds. Moreover, reasoning on dependency interaction further leads us to interesting parallelization opportunities, yielding additional scalability gains. We provide a proof-of-concept implementation of our chase-based algorithm and an experimental study aiming at gauging its scalability with respect to a number of parameters, among which the size of source instances and the number of dependencies of each tested scenario. Finally, we empirically compare with the latest DE engines, and show that our algorithm outperforms them.

Published
2016
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10. Réputation et respect de la vie privée dans les réseaux dynamiques auto-organisés

Author

Lajoie-Mazenc, Paul, Confidentialité, Intégrité, Disponibilité et Répartition (CIDRE), CentraleSupélec-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SYSTÈMES LARGE ÉCHELLE (IRISA-D1), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-Centre National de la Recherche Scientifique (CNRS), Inria@SiliconValley (Inria@SiliconValley), Stanford University-University of California [Santa Cruz] (UC Santa Cruz), University of California (UC)-University of California (UC)-Institut National de Recherche en Informatique et en Automatique (Inria)-University of California [Santa Barbara] (UC Santa Barbara), University of California (UC)-University of California [San Diego] (UC San Diego), University of California (UC)-Ministère de l'Europe et des Affaires étrangères (MEAE)-University of Southern California (USC)-CITRIS-University of California [Irvine] (UC Irvine), University of California (UC), Université de Rennes, Emmanuelle Anceaume, Valérie Viêt Triêm Tông, CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA), Stanford University-University of Southern California (USC)-Institut National de Recherche en Informatique et en Automatique (Inria)-University of California [Santa Barbara] (UCSB), University of California-University of California-University of California [San Diego] (UC San Diego), University of California-Ministère de l'Europe et des Affaires étrangères (MEAE)-CITRIS-University of California [Santa Cruz] (UCSC), University of California-University of California [Irvine] (UCI), University of California, and Université Rennes 1

Subjects
Applied cryptography, Réputation, [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR], Privacy, Cryptographie appliquée, Systèmes distribués, Distributed systems, Vie privée, Reputation
Abstract

Reputation mechanisms are very powerful mechanisms to foster trust between unknown users, by rewarding good behaviors and punishing bad ones. Reputation mechanisms must guarantee that the computed reputation scores are precise and robust against attacks; to guarantee such properties, existing mechanisms require information that jeopardize users' privacy: for instance, clients' interactions might be tracked. Privacy-preserving reputation mechanisms have thus been proposed, protecting both clients' privacy and the providers' one. However, to guarantee strong privacy properties, these mechanisms provide imprecise reputation scores, particularly by preventing clients to testify about their negative interactions. In this thesis, we propose a new distributed privacy-preserving reputation mechanism allowing clients to issue positive as well as negative feedback. Such a construction is made possible thanks to tools from the distributed systems community -- distributed third parties that allow for a distribution of trust and that tolerate malicious behaviors -- as well as from the cryptographic one -- for instance zero-knowledge proofs of knowledge or anonymous proxy signatures. Furthermore, we prove that our mechanism guarantees the required privacy and security properties, and we show with theoretical and practical analysis that this mechanism is usable.; Les mécanismes de réputation sont des outils très utiles pour inciter des utilisateurs ne se connaissant pas à se faire confiance, en récompensant les bons comportements et, inversement, en pénalisant les mauvais. Cependant, pour que la réputation des fournisseurs de service soit précise et robuste aux attaques, les mécanismes de réputation existants requièrent de nombreuses informations qui menacent la vie privée des utilisateurs; par exemple, il est parfois possible de traquer les interactions effectuées par les clients. Des mécanismes de réputation préservant aussi bien la vie privée des clients que celle des fournisseurs sont donc apparus pour empêcher de telles attaques. Néanmoins, pour garantir des propriétés fortes de vie privée, ces mécanismes ont dû proposer des scores de réputation imprécis, notamment en ne permettant pas aux clients de témoigner de leurs interactions négatives.Dans cette thèse, nous proposons un nouveau mécanisme de réputation distribué préservant la vie privée, tout en permettant aux clients d'émettre des témoignages négatifs. Une telle construction est possible grâce à des outils issus des systèmes distribués -- des tierces parties distribuées qui permettent de distribuer la confiance et de tolérer des comportements malveillants -- et de la cryptographie -- par exemple des preuves de connaissance à divulgation nulle de connaissance ou des signatures proxy anonymes. Nous prouvons de plus que ce mécanisme garantit les propriétés de vie privée et de sécurité nécessaires, et montrons par des analyses théoriques et pratiques que ce mécanisme est utilisable.

Published
2015

11. Toward Scalable Hybrid Stores

Author

Bugiotti, Francesca, Bursztyn, Damian, Deutsch, Alin, Ileana, Ioana, Manolescu, Ioana, Database optimizations and architectures for complex large data (OAK), Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), CentraleSupélec, Université Paris-Sud - Paris 11 (UP11), Inria@SiliconValley (Inria@SiliconValley), Stanford University-University of California [Santa Cruz] (UC Santa Cruz), University of California (UC)-University of California (UC)-Institut National de Recherche en Informatique et en Automatique (Inria)-University of California [Santa Barbara] (UC Santa Barbara), University of California (UC)-University of California [San Diego] (UC San Diego), University of California (UC)-Ministère de l'Europe et des Affaires étrangères (MEAE)-University of Southern California (USC)-CITRIS-University of California [Irvine] (UC Irvine), University of California (UC), Department of Computer Science and Engineering [Univ California San Diego] (CSE - UC San Diego), University of California [San Diego] (UC San Diego), University of California (UC)-University of California (UC), OAKSAD, Stanford University-University of Southern California (USC)-Institut National de Recherche en Informatique et en Automatique (Inria)-University of California [Santa Barbara] (UCSB), University of California-University of California-University of California [San Diego] (UC San Diego), University of California-Ministère de l'Europe et des Affaires étrangères (MEAE)-CITRIS-University of California [Santa Cruz] (UCSC), University of California-University of California [Irvine] (UCI), University of California, Database group, Computer Science and engeenering Department [San Diego] (DB CSE UCSD), and University of California-University of California

Subjects
[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]
Abstract

National audience; Data centric applications often use heterogeneous datasets: some very large while others of moderate size, some highly structured (e.g., relations) while others complex structured (e.g., graphs) or little structured (e.g., log data). Facing them is a variety of storage systems but none of which is the best for all, at all times.We present Estocada, an architecture we are currently developing to efficiently handle highly heterogeneous datasets based on a dynamic set of potentially very different data stores. Estocada provides to the ap- plication layer access to each dataset in its native format, while hosting them internally in a set of potentially overlapping fragments, possibly distributed across heterogeneous stores. At the core of Estocada lie powerful view-based rewriting and view selection algorithms to marry correctness with high performance.

Published
2015

12. Invisible Glue: Scalable Self-Tuning Multi-Stores

Author

Bugiotti, Francesca, Bursztyn, Damian, Deutsch, Alin, Ileana, Ioana, Manolescu, Ioana, Inria@SiliconValley (Inria@SiliconValley), Stanford University-University of California [Santa Cruz] (UC Santa Cruz), University of California (UC)-University of California (UC)-Institut National de Recherche en Informatique et en Automatique (Inria)-University of California [Santa Barbara] (UC Santa Barbara), University of California (UC)-University of California [San Diego] (UC San Diego), University of California (UC)-Ministère de l'Europe et des Affaires étrangères (MEAE)-University of Southern California (USC)-CITRIS-University of California [Irvine] (UC Irvine), University of California (UC), Database optimizations and architectures for complex large data (OAK), Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Paris-Sud - Paris 11 (UP11), Department of Computer Science and Engineering [Univ California San Diego] (CSE - UC San Diego), University of California [San Diego] (UC San Diego), University of California (UC)-University of California (UC), Télécom ParisTech, OAKSAD, Stanford University-University of Southern California (USC)-Institut National de Recherche en Informatique et en Automatique (Inria)-University of California [Santa Barbara] (UCSB), University of California-University of California-University of California [San Diego] (UC San Diego), University of California-Ministère de l'Europe et des Affaires étrangères (MEAE)-CITRIS-University of California [Santa Cruz] (UCSC), University of California-University of California [Irvine] (UCI), University of California, Database group, Computer Science and engeenering Department [San Diego] (DB CSE UCSD), and University of California-University of California

Subjects
[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]
Abstract

International audience; Next-generation data centric applications often involve di-verse datasets, some very large while others may be of mod-erate size, some highly structured (e.g., relations) while others may have more complex structure (e.g., graphs) or little structure (e.g., text or log data). Facing them is a variety of storage systems, each of which can host some of the datasets (possibly after some data migration), but none of which is likely to be best for all, at all times. Deploying and efficiently running data-centric applications in such a complex setting is very challenging. We propose Estocada, an architecture for efficiently han-dling highly heterogeneous datasets based on a dynamic set of potentially very different data stores. Estocada pro-vides to the application/programming layer access to each data set in its native format, while hosting them internally in a set of potentially overlapping fragments, possibly dis-tributing (fragments of) each dataset across heterogeneous stores. Given workload information, Estocada self-tunes for performance, i.e., it automatically choses the fragments of each data set to be deployed in each store so as to op-timize performance. At the core of Estocada lie powerful view-based rewriting and view selection algorithms, required in order to correctly handle the features (nesting, keys, con-straints etc.) of the diverse data models involved, and thus to marry correctness with high performance.

Published
2015

13. Community Access to MODIS Satellite Reprojection and Reduction Pipeline and Data Sets

Author

Hendrix, Val, Li, Jie, Jackson, Keith, Ramakrishnan, Lavanya, Ryu, Youngryel, Beattie, Keith, Morin, Christine, Skinner, David, Van Ingen, Catharine, Agarwal, Deb, Advanced Computing for Science Department, Advanced Computing for Science Department [LBNL Berkeley] (ACS), Lawrence Berkeley National Laboratory [Berkeley] (LBNL)-Lawrence Berkeley National Laboratory [Berkeley] (LBNL), eScience Group, Department of Computer Science [Charlottesville] (CS), University of Virginia [Charlottesville]-University of Virginia [Charlottesville], Advanced Computing for Science, Dept. Landscape Architecture and Rural Systems Engineering, Department of Landscape Architecture and Rural Systems Engineering, Seoul National University [Seoul] (SNU)-Seoul National University [Seoul] (SNU), Design and Implementation of Autonomous Distributed Systems (MYRIADS), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SYSTÈMES LARGE ÉCHELLE (IRISA-D1), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA), Inria@SiliconValley (Inria@SiliconValley), Stanford University-University of Southern California (USC)-Institut National de Recherche en Informatique et en Automatique (Inria)-University of California [Santa Barbara] (UCSB), University of California-University of California-University of California [San Diego] (UC San Diego), University of California-Ministère de l'Europe et des Affaires étrangères (MEAE)-CITRIS-University of California [Santa Cruz] (UCSC), University of California-University of California [Irvine] (UCI), University of California, National Energy Research Scientific Computing Center, National Energy Research Scientific Computing Center (NERSC), US Department of Energy-US Department of Energy, San Francisco, Microsoft Research [Redmond], Microsoft Corporation [Redmond, Wash.]-Microsoft Corporation [Redmond, Wash.], DALHIS, University of Virginia-University of Virginia, Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Stanford University-University of California [Santa Cruz] (UC Santa Cruz), University of California (UC)-University of California (UC)-Institut National de Recherche en Informatique et en Automatique (Inria)-University of California [Santa Barbara] (UC Santa Barbara), University of California (UC)-University of California [San Diego] (UC San Diego), University of California (UC)-Ministère de l'Europe et des Affaires étrangères (MEAE)-University of Southern California (USC)-CITRIS-University of California [Irvine] (UC Irvine), and University of California (UC)

Subjects
Remote Sensing, MODIS, INFORMATICS, Computational models, BIOGEOSCIENCES, Remote sensing, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], Software tools and services, Portals and user interfaces, algorithms, Computational models, algorithms
Abstract

Poster; International audience; Moderate Resolution Imaging Spectroradiometer (MODIS), the key instrument aboard NASA's Terra and Aqua satellites, continuously generates data as the satellites cover the entire surface of earth every one to two days. This data is important to many scientific analyses, however, data procurement and processing can be challenging and cumbersome for user communities. Our current work is focused on enabling calculations using a combination of land and atmosphere products over land. Before performing the calculation the data must be downloaded and transformed, from a swath space and time system to a sinusoidal tiling system. Downloading data for a single product for an entire year can take several days for a single product and involves downloading via FTP many small files (on average ~83,000 files) in hierarchical data format (HDF4). The data processing, a swath-to-sinusoidal reprojection, is computationally intensive and currently available community tools only work for single sinusoidal tiles. We have developed a data-processing pipeline that downloads the MODIS products and reprojects them on HPC systems. HPC systems do not traditionally run these high-throughput data-intensive jobs and hence we need to address unique challenges for our pipeline. The first stage in the pipeline uses a catalog to determine what files need to be downloaded and downloads identified data sets. The downloaded files will in the future trigger an event that causes the reprojection job to be entered into a job queue. The output data is stored in an archival system. The resulting reprojected data will soon be widely available to the community through a front-end web portal. The portal will allow users to download reprojected data (~1 TB/year) for the following land and atmosphere products: MODO4_L2 (Aerosol), MOD05_L2 (Water Vapor), MOD06_L2 (Cloud), MOD07_L2 (Atmosphere Profile) and MOD11_L2 (Land Surface Temperature Emissivity). In this talk we will describe the architecture of the overall system and pipeline. Our long term plan is to allow users to reproject data on-demand and/or run algorithms on the reprojected MODIS data such as an evapotranspiration calculation.

Published
2012

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