Ph.D
Group : Artificial Intelligence and Inference Systems

Open-Source Software Distribution in a Peer-to-Peer Environment

Starts on 01/10/2004
Advisor :
[Dan Vodislav]

Funding : Convention industrielle de formation par la recherche
Affiliation : vide
Laboratory : CNAM, INRIA Saclay (Gemo)

Defended on 24/11/2008, committee :

Research activities :
   - Optimization

Abstract :
The aim of this research work is to improve the mechanisms for distributing open-source software among the developers and users communities. We designed a novel system for content dissemination, based on a peer-to-peer (P2P) architecture, providing a large panel of functionalities such as publishing content, subscription and notification mechanisms, querying and content downloading. We propose a structured model for content, annotated with metadata, on which we build a complex information system with advanced capabilities for resource location. The decentralized approach inherent to a P2P structure, combined with a rich model for content description, create together the premises for a scalable and flexible system, where all the peers in the network (publishers, mirrors or clients) are involved in the distribution process. Our solution comes to face a real necessity in open-source software development and offers a replacement alternative to the current
distribution mechanisms. We integrated several existing subsystems (a distributed index for metadata management and a dissemination platform based on content clustering and multicast) into a complex system that transparently provides to the user all the content management functionalities. Our implementation took the shape of an industrial prototype, evaluated on a large scale network deployment and ready to be adopted by Mandriva Linux community.

CAUSAL LEARNING FOR DIAGNOSTIC SUPPORT


CAUSAL UNCERTAINTY QUANTIFICATION UNDER PARTIAL KNOWLEDGE AND LOW DATA REGIMES


MICRO VISUALIZATIONS: DESIGN AND ANALYSIS OF VISUALIZATIONS FOR SMALL DISPLAY SPACES
The topic of this habilitation is the study of very small data visualizations, micro visualizations, in display contexts that can only dedicate minimal rendering space for data representations. For several years, together with my collaborators, I have been studying human perception, interaction, and analysis with micro visualizations in multiple contexts. In this document I bring together three of my research streams related to micro visualizations: data glyphs, where my joint research focused on studying the perception of small-multiple micro visualizations, word-scale visualizations, where my joint research focused on small visualizations embedded in text-documents, and small mobile data visualizations for smartwatches or fitness trackers. I consider these types of small visualizations together under the umbrella term ``micro visualizations.'' Micro visualizations are useful in multiple visualization contexts and I have been working towards a better understanding of the complexities involved in designing and using micro visualizations. Here, I define the term micro visualization, summarize my own and other past research and design guidelines and outline several design spaces for different types of micro visualizations based on some of the work I was involved in since my PhD.