Ph.D
Group : Verification of Algorithms, Languages and Systems

A Coq Formalization of Relational and Deductive Databases - and Mechanizations of Datalog

Starts on 01/10/2012
Advisor : BENZAKEN, Véronique
[CONTEJEAN Evelyne]

Funding : Contrat doctoral uniquement recherche
Affiliation : Université Paris-Saclay
Laboratory : LRI-Proval

Defended on 02/12/2016, committee :

Research activities :
   - Formalisation of (Specification and Programming) Languages in Proof Assistants
   - Data-Centric Languages and Systems

Abstract :
This thesis presents a Coq formalization of fundamental database languages and algorithms. It provides formal specifications stemming from two different approaches in defining database models: relational and logic based.

As such, the first contribution of the thesis is the development of a Coq library for the relational model. This contains formalizations of the relational algebra and of conjunctive queries. It also includes a mechanization of integrity constraints and of their inference procedures. We model two types of dependencies, which are among the most widely used: the functional dependencies and the multivalued dependencies, as well as their corresponding axiomatizations. We formally prove the soundness of their inference algorithms and, for the case of functional dependencies, also the completeness. These types of dependencies are instances of more general ones, namely general dependencies: equality generating dependencies and, respectively, tuple generating dependencies. We model general dependencies and their inference procedure, i.e, "the chase", for which we establish the soundness. Finally, we formally prove the fundamental database theorems, i.e, algebraic equivalence, the homomorphism theorem and the minimization of conjunctive queries.

A second contribution consists of the development of a Coq/ssreflect library for logic programming, restricted to Datalog. As part of this work, we provide a first mechanization of a standard Datalog engine, as well as of its extension with negation. The library contains a formalization of their model-theoretic semantics, together with the fixpoint one, implemented through a stratified evaluation procedure. The library is complete with the corresponding soundness, termination and completeness proofs. In this setting, we construct a preliminary framework for reasoning about stratified programs. The platform paves the way towards the certification of data-centric applications.

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.