Ph.D
Group : Large-scale Heterogeneous DAta and Knowledge

Traitement efficace de requêtes sparql avec extensions olap pour entrepôts RDF

Starts on 01/09/2011
Advisor : MANOLESCU-GOUJOT, Ioana
[GOASDOUE François]

Funding :
Affiliation : Université Paris-Saclay
Laboratory : LRI

Defended on 22/09/2014, committee :

Research activities :

Abstract :
The utility and relevance of data lie in the information that can be extracted from it. The high rate of data publication and its increased complexity, for instance the heterogeneous, self-describing Semantic Web data, motivate the interest in efficient techniques for data manipulation. In this thesis we leverage mature relational data management technology for querying Semantic Web data.

The first part focuses on query answering over data subject to RDFS constraints, stored in relational data management systems. The implicit information resulting from RDF reasoning is required to correctly answer such queries. We introduce the database fragment of RDF, going beyond the expressive power of previously studied fragments. We devise novel techniques for answering Basic Graph Pattern queries within this fragment, exploring the two established approaches for handling RDF semantics, namely graph saturation and query reformulation.
In particular, we consider graph updates within each approach and propose a method for incrementally maintaining the saturation. We experimentally study the performance trade-offs of our techniques, which can be deployed on top of any relational data management engine.

The second part of this thesis considers the new requirements for data analytics tools and methods emerging from the development of the Semantic Web. We fully redesign, from the bottom up, core data analytics concepts and tools in the context of RDF data. We propose the first complete formal framework for warehouse-style RDF analytics. Notably, we define analytical schemas tailored to heterogeneous, semantic-rich RDF graphs, analytical queries which (beyond relational cubes) allow flexible querying of the data and the schema as well as powerful aggregation and OLAP-style operations. Experiments on a fully-implemented platform demonstrate the practical interest of our approach.

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.