1,721,009 research outputs found
Requêtes et Schémas Hétérogènes : Complexité et Apprentissage
Get PDFSpecifying a database query using a formal query language is typically a challenging task for non-expert users. In the context of big data, this problem becomes even harder because it requires the users to deal with database instances of large size and hence difficult to visualize. Such instances usually lack a schema to help the users specify their queries, or have an incomplete schema as they come from disparate data sources. In this thesis, we address the problem of query specification for non-expert users. We identify two possible approaches for tackling this problem: learning queries from examples and translating the data in a format that the user finds easier to query. Our contributions are aligned with these two complementary directions and span over three of the most popular data models: XML, relational, and graph. This thesis consists of two parts, dedicated to (i) schema definition and translation, and to (ii) learning schemas and queries. In the first part, we define schema formalisms for unordered XML and we analyze their computational properties; we also study the complexity of the data exchange problem in the setting of a relational source and a graph target database. In the second part, we investigate the problem of learning from examples the schemas for unordered XML proposed in the first part, as well as relational join queries and path queries on graph databases. The interactive scenario that we propose for these two classes of queries is immediately applicable to assisting non-expert users in the process of query specification.La spécification de requêtes est généralement une tâche difficile pour les utilisateurs non-experts. Le problème devient encore plus difficile quand les utilisateurs ont besoin d'interroger des bases de données de grande taille et donc difficiles à visualiser. Le schéma pourrait aider à cette spécification, mais celui-ci manque souvent ou est incomplet quand les données viennent de sources hétérogènes. Dans cette thèse, nous abordons le problème de la spécification de requêtes pour les utilisateurs non-experts. Nous identifions deux approches pour attaquer ce problème : apprendre les requêtes à partir d'exemples ou transformer les données dans un format plus facilement interrogeable par l'utilisateur. Nos contributions suivent ces deux directions et concernent trois modèles de données parmi les plus populaires : XML, relationnel et orienté graphe. Cette thèse comprend deux parties, consacrées à (i) la définition et la transformation de schémas, et (ii) l'apprentissage de schémas et de requêtes. Dans la première partie, nous définissons des formalismes de schémas pour les documents XML non-ordonnés et nous analysons leurs propriétés computationnelles; nous étudions également la complexité du problème d'échange de données entre une source relationnelle et une cible orientée graphe. Dans la deuxième partie, nous étudions le problème de l'apprentissage à partir d'exemples pour les schémas XML proposés dans la première partie, ainsi que pour les requêtes de jointures relationnelles et les requêtes de chemins sur les graphes. Nous proposons notamment un scénario interactif qui permet d'aider des utilisateurs non-experts à définir des requêtes dans ces deux classes
Analyse Sécurisée de Données Externalisées
No full textThe goal of this document is to summarize my research activities since my recruitment as a Maître de Conférences in 2016. Most of my research in this period has been dedicated to addressing the data security concerns that occur when outsourcing some data to the cloud and then performing some computations (such as evaluating a query or a machine learning algorithm) on the data directly in the cloud. I focused on the honest-but-curious cloud model i.e., that executes tasks dutifully, but tries to gain as much information as possible. I contributed to: (i) secure evaluation of SPARQL queries on outsourced graphs, as well as synthetic generation of graphs and queries; (ii) secure protocols for sequential machine learning algorithms, with an emphasis on multi-armed bandits; (iii) secure MapReduce protocols for matrix multiplication and relational query evaluation. Our approach consists of developing distributed and secure protocols that combine existing algorithms with cryptographic schemes (such as AES and Paillier) and secure multi-party computations. Our protocols guarantee the same result as the standard, non-secure algorithms while enjoying desirable security properties.Le but de ce document est de résumer mes activités de recherche depuis mon recrutement en tant que Maître de Conférences en 2016. La plupart de mes recherches pendant cette période ont été consacrées aux problèmes de sécurité des données qui apparaissent lors de l'externalisation des données dans le cloud, suivie par certains calculs (tels que l'évaluation d'une requête ou d'un algorithme d'apprentissage automatique) directement dans le cloud. Je me suis concentré sur le modèle de cloud honnête-mais-curieux, c'est-à-dire qui exécute les tâches consciencieusement, mais essaie d'apprendre le plus d'informations possible. J'ai contribué à: (i) l'évaluation sécurisée de requêtes SPARQL sur des graphes externalisés, et sur la génération synthétique de graphes et requêtes; (ii) des protocoles sécurisés pour les algorithmes d'apprentissage automatique séquentiel, en mettant l'accent sur les bandits; (iii) des protocoles MapReduce sécurisés pour le produit matriciel et l'évaluation des requêtes relationnelles. Notre approche consiste à développer des protocoles distribués et sécurisés qui combinent des algorithmes existants avec des techniques cryptographiques (telles que AES et Paillier) et du calcul multipartite sécurisé. Nos protocoles garantissent le même résultat que les algorithmes standard non-sécurisés tout en bénéficiant de propriétés de sécurité
Analyse Sécurisée de Données Externalisées
No full textThe goal of this document is to summarize my research activities since my recruitment as a Maître de Conférences in 2016. Most of my research in this period has been dedicated to addressing the data security concerns that occur when outsourcing some data to the cloud and then performing some computations (such as evaluating a query or a machine learning algorithm) on the data directly in the cloud. I focused on the honest-but-curious cloud model i.e., that executes tasks dutifully, but tries to gain as much information as possible. I contributed to: (i) secure evaluation of SPARQL queries on outsourced graphs, as well as synthetic generation of graphs and queries; (ii) secure protocols for sequential machine learning algorithms, with an emphasis on multi-armed bandits; (iii) secure MapReduce protocols for matrix multiplication and relational query evaluation. Our approach consists of developing distributed and secure protocols that combine existing algorithms with cryptographic schemes (such as AES and Paillier) and secure multi-party computations. Our protocols guarantee the same result as the standard, non-secure algorithms while enjoying desirable security properties.Le but de ce document est de résumer mes activités de recherche depuis mon recrutement en tant que Maître de Conférences en 2016. La plupart de mes recherches pendant cette période ont été consacrées aux problèmes de sécurité des données qui apparaissent lors de l'externalisation des données dans le cloud, suivie par certains calculs (tels que l'évaluation d'une requête ou d'un algorithme d'apprentissage automatique) directement dans le cloud. Je me suis concentré sur le modèle de cloud honnête-mais-curieux, c'est-à-dire qui exécute les tâches consciencieusement, mais essaie d'apprendre le plus d'informations possible. J'ai contribué à: (i) l'évaluation sécurisée de requêtes SPARQL sur des graphes externalisés, et sur la génération synthétique de graphes et requêtes; (ii) des protocoles sécurisés pour les algorithmes d'apprentissage automatique séquentiel, en mettant l'accent sur les bandits; (iii) des protocoles MapReduce sécurisés pour le produit matriciel et l'évaluation des requêtes relationnelles. Notre approche consiste à développer des protocoles distribués et sécurisés qui combinent des algorithmes existants avec des techniques cryptographiques (telles que AES et Paillier) et du calcul multipartite sécurisé. Nos protocoles garantissent le même résultat que les algorithmes standard non-sécurisés tout en bénéficiant de propriétés de sécurité
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Demonstration of GOOSE: A Secure Framework for Graph Outsourcing and SPARQL Evaluation
No full textInternational audienceWe demonstrate GOOSE, an open-source framework for secure graph outsourcing and SPARQL evaluation. We showcase the workflow of GOOSE over various real-world use cases, the scalability of GOOSE, and the security properties that GOOSE guarantees in the honest-but-curious cloud security model
Learning Queries for Relational, Semi-structured, and Graph Databases
Get PDFInternational audienceWeb applications store their data within various database models, such as relational, semi-structured, and graph data models to name a few. We study learning algorithms for queries for the above mentioned models. As a further goal, we aim to apply the results to learning cross-model database mappings, which can also be seen as queries across different schemas
Learning Schemas for Unordered XML
Get PDFWe consider unordered XML, where the relative order among siblings is ignored, and we investigate the problem of learning schemas from examples given by the user. We focus on the schema formalisms proposed in [10]: disjunctive multiplicity schemas (DMS) and its restriction, disjunction-free multiplicity schemas (MS). A learning algorithm takes as input a set of XML documents which must satisfy the schema (i.e., positive examples) and a set of XML documents which must not satisfy the schema (i.e., negative examples), and returns a schema consistent with the examples. We investigate a learning framework inspired by Gold [18], where a learning algorithm should be sound i.e., always return a schema consistent with the examples given by the user, and complete i.e., able to produce every schema with a sufficiently rich set of examples. Additionally, the algorithm should be efficient i.e., polynomial in the size of the input. We prove that the DMS are learnable from positive examples only, but they are not learnable when we also allow negative examples. Moreover, we show that the MS are learnable in the presence of positive examples only, and also in the presence of both positive and negative examples. Furthermore, for the learnable cases, the proposed learning algorithms return minimal schemas consistent with the examples
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