Section: Partnerships and Cooperations

National Initiatives

• Type: ANR Project (N ANR-15-CE25-0013-01)

• Instrument: PRCE

• Duration: February 2016 - 36 mois

• Coordinator: Renata Teixeira

• Other partners: Inria Paris, LORIA, Ip-label SaS, ORANGE SA , TSP (SAMOVAR) Telecom SudParis, University of Lille 1 (CRIStAL)

• Inria contact: Renata Teixeira

• Abstract: As our lives become more dependent on the Internet, it is easy to understand peoples frustration when poor Internet performance prevents them from accomplishing ever-more important online activities. The Quality of Experience (QoE) when accessing the Internet is thus a key factor for todays society. When users experience poor Internet QoE, they are often helpless. The complexity of Internet services and of users local connectivity has grown dramatically in the last years with the proliferation of proxies and caches at the core and of home wireless and 3G/4G access. However, it is hard even for experts to diagnose the sources of performance bottlenecks. We argue that the new rules for regulating Internet access and plans to improve it should focus on the end-to-end Internet QoE - i.e., the user experience when performing her usual online activities on the Internet - and should provide means for all different players to accurately diagnose the sources of poor Internet QoE. The objective of BottleNet is to deliver methods, algorithms, and software systems to measure Internet QoE and diagnose the root cause of poor Internet QoE. Our goal calls for tools that run directly at users devices. We plan to collect network and application performance metrics directly at users devices and correlate it with user perception to model Internet QoE, and to correlate measurements across users and devices to diagnose poor Internet QoE. This data-driven approach is essential to address the challenging problem of modeling user perception and of diagnosing sources of bottlenecks in complex Internet services. BottleNet will lead to new solutions to assist users, network and service operators as well as regulators in understanding Internet QoE and the sources of performance bottleneck.

• Type: CNRS Mission Interdisciplinarité (MI) DEFI MASTODONS "La qualité des données dans les Big Data"

• Instrument: AAP 2016

• Duration: Mars 2016 - 12 mois

• Coordinator: Dimitris Kotzinos (University of Cergy Pointoise)

• Other partners: Inria Paris, IGN-COGIT

• Inria contact: Vassilis Christophides

• Abstract: Big Geo Data represents an important type of the crowd sourced data that are available today at a global scale. This kind of data refers to locations, i.e., Points of Interest (POIs), and is usually published in social media (e.g., Facebook, Google+) or in specialized platforms (e.g., Open Street Maps, Yelp). The quality (e.g., precision, accuracy, consistency) of geo-referenced crowd sourced content depends on the origin (machine vs. human generated), the level of detail of the extraction methods, as well as the obfuscation techniques used to protect users privacy. There is clearly a tradeoff between enhancing the quality of published geo data and the privacy risks entailed for the individuals, also known as geoprivacy, to uncover places visited, trajectories pursuit etc. Understanding the different aspects of geographic/geometric/geospatial quality involved in crowd-sourced geo data and assessing the privacy risks introduced by enhancing its quality in personal, social, and urban applications is a challenging topic. In this project we are interested in studying the following questions in concrete use-cases:

  • How can we measure the quality of geographic/geometric/geospatial data involved in crowd-sourced content?

  • How can we assess the privacy risks introduced by enhancing geospatial quality in personal, social and urban applications especially in the context of social media platforms?

  In this project Inria (MUSE) is interested in the opportunities and privacy concerns of mobile location analytics supporting customers or travellers experience in venues of various types and sizes (e.g., retail stores, shopping malls, airports, theme parks, etc.). Such (indoor) location-based services in smart spaces presents new privacy risks as data continuously flows between visitors mobile devices, networked sensors embedded into the environment as well as the backend analytics platform in order to track users and anticipate their needs.