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Ph.D de

Ph.D
Group : Artificial Intelligence and Inference Systems

Diagnostic distribué de systèmes respectant la confidentialité

Starts on 01/10/2007
Advisor : DAGUE, Philippe

Funding : AM
Affiliation : Université Paris-Sud
Laboratory : LRI INRIA LEO

Defended on 27/09/2012, committee :
M. Philippe DAGUE, Directeur de Thèse, Université Paris Sud Directeur de thèse
M. Laurent SIMON, Maître de conférence, Université Paris Sud Co-directeur de thèse
M. Philippe JEGOU, Professeur, Université Paul Cézanne Rapporteur
M. Pierre MARQUIS, Professeur, Université d'Artois Rapporteur
Mme Marie-Odile CORDIER, Professeur, Université Rennes 1 Examinatrice
M. Alain DENISE, Professeur, Université Paris Sud Examinateur

Research activities :

Abstract :
In this thesis, we focus on diagnosing inherently distributed systems such as peer-to-peer, where each peer has access to only a sub-part of the description of an overall system.In addition, due to a too restrictive access control policy, it can be possible that neither the peer nor the supervisor is able to explain the behaviour of the overall system.We aim at explaining this behaviour using a set of peers (each having a limited local view).
First, we show that any new system logically equivalent to the initially observed peer-to-peer setting ensures that all the diagnoses of a peer may be extended to a global diagnosis (in this case the new system ensures the correctness of the distributed diagnosis).Moreover, we prove that if the new system is structured (i.e. it contains a spanning tree for which all peers containing the same variable form a connected graph) then it ensures that any global diagnosis can be found through a set of local diagnoses (in this case the new system ensures the completeness of the distributed diagnoses).
For a succinct representation and in order to comply with the privacy policy of the vocabulary of each peer, we present a new algorithm Token Elimination (TE), which decomposes the original peer system to a structured one. We experimentally show that TE produces better quality decompositions (i.e. smaller tree widths) than other proposed methods in a distributed context.
Exploiting the structured system built by TE, we transform each local description into globally consistent DNF. We demonstrate that the latter system is correct and complete for the distributed diagnosis. Finally, we present an algorithm that can efficiently check that any local diagnosis is part of a global minimal diagnosis, turning the structured system of DNFs into a compiled system for distributed diagnosis.

Ph.D. dissertations & Faculty habilitations
DECODING THE PLATFORM SOCIETY: ORGANIZATIONS, MARKETS AND NETWORKS IN THE DIGITAL ECONOMY
The original manuscript conceptualizes the recent rise of digital platforms along three main dimensions: their nature of coordination devices fueled by data, the ensuing transformations of labor, and the accompanying promises of societal innovation. The overall ambition is to unpack the coordination role of the platform and where it stands in the horizon of the classical firm – market duality. It is also to precisely understand how it uses data to do so, where it drives labor, and how it accommodates socially innovative projects. I extend this analysis to show continuity between today’s society dominated by platforms and the “organizational society”, claiming that platforms are organized structures that distribute resources, produce asymmetries of wealth and power, and push social innovation to the periphery of the system. I discuss the policy implications of these tendencies and propose avenues for follow-up research.

DISTRIBUTED COMPUTING WITH LIMITED RESOURCES


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