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Ph.D de

Ph.D
Group : Databases

XQUERY updates optimisation

Starts on 01/11/2008
Advisor : BIDOIT, Nicole
[Dario COLAZZO]

Funding : ETR-BGF
Affiliation : Université Paris-Sud
Laboratory : LRI BD

Defended on 21/09/2012, committee :
Bern Amann - Professeur - Université Paris 6 - Rapporteur
Nicole Bidoit-Tollu - Professeur - Université Paris Sud - Directeur de thèse
Dario Colazzo - MdC HdR - Université Paris Sud - Directeur de thèse
Giovanna Guerrini - Professeur - Université Genova (Italie) - Rapporteur
Benjamin Nguye - MdC - Université Versailles - Examinateur

Research activities :

Abstract :
With the widespread diffusion of XML as a format for representing data generated and exchanged over the Web, main query and update engines have been designed and implemented in the last decade. A kind of engines that are playing a crucial role in many applications are « main-memory » systems, which distinguish for the fact that they are easy to manage and to integrate in a programming environment. On the other hand, main-memory systems have scalability issues, as they load the entire document in main-memory before processing.

This Thesis presents an XML partitioning technique that allows main-memory engines to process a class of XQuery expressions (queries and updates), that we dub « iterative », on arbitrarily large input documents. We provide a static analysis technique to recognize these expressions. The static analysis is based on paths extracted from the expression and does not need additional schema information. We provide algorithms using path information for partitioning the input documents, so that the query or update can be separately evaluated on each part in order to compute the final result. These algorithms admit a streaming implementation, whose effectiveness is experimentally validated.

Besides enabling scalability, our approach is also characterized by the fact that it is easily implementable into a MapReduce framework, thus enabling parallel query/update evaluation on the partitioned data.

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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