Ph.D
Group : Databases

Analyse statique pour l’optimisation des mises à jour de documents XML temporels

Starts on 01/09/2008
Advisor : BIDOIT, Nicole

Funding : AM
Affiliation : Université Paris-Saclay
Laboratory : LRI BD

Defended on 07/09/2012, committee :

Research activities :

Abstract :
The last decade has witnessed a rapid expansion of XML as a format for representing and exchanging data through the web. In order to follow this evolution, many languages have been proposed to query, update or transform XML documents. At the same time, a range set of systems allowing to store and process XML documents have been developed. Among these systems, main-memory engines are lightweight systems that are the favoured choice for applications that do not require complex functionalities of traditional DBMS such as secondary storage indexes or transaction management. These engines require loading the documents to be processed entirely into main-memory. Consequently, they suffer from space limitations and are not able to process quite large documents.

In this thesis, we investigate issues related to the evolution of XML documents and to the management of the temporal dimension for XML. This thesis consists of two parts sharing the common goal of developing efficient techniques for processing large XML documents using main-memory engines. The first part investigates the optimization of update for static XML documents. We have developed a technique based on XML projection, a method that has been proposed to overcome the limitations of main-memory engines in the case of querying. We have devised for a new scenario for projection allowing the propagation of the updates effects.

The second part of the thesis investigates the issue of building and maintaining time-stamped XML documents under space limitations. Our contribution consists in two methods. The first method can be applied in the general case where no restriction is made on the evolution of the XML documents. This method is designed to be performed in streaming and allows thus processing large time-stamped documents. The second method deals with the case where the changes are specified by updates. This method is based on the projection paradigm which allows it for processing large time-stamped documents and for generating time-stamped documents which are satisfactory from the point of view of storage.

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.