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In this project, a prototype visual data mining tool was developed, allowing users to interactively investigate large multi-dimensional datasets visually (using 2D visualization techniques) using so called drill-down, roll-up and slicing operations. The project included all steps of the development, from writing specifications and designing the program to implementing and evaluating it. Using ideas from data warehousing, custom methods for storing pre-computed aggregations of data (commonly referred to as materialized views) and retrieving data from these were developed and implemented in order to achieve higher performance on large datasets. View materialization enables the program to easily fetch or calculate a view using other views, something which can yield significant performance gains if view sizes are much smaller than the underlying raw dataset. The choice of which views to materialize was done in an automated manner using a well-known algorithm - the greedy algorithm for view materialization - which selects the fraction of all possible views that is likely (but not guaranteed) to yield the best performance gain. The use of materialized views was shown to have good potential to increase performance for large datasets, with an average speedup (compared to on-the-fly queries) between 20 and 70 for a test dataset containing 500~000 rows. The end result was a program combining flexibility with good performance, which was also reflected by good scores in a user-acceptance test, with participants from the company where this project was carried out.

XML was recommended by W3C in 1998 as a markup language to be used by device- and system-independent methods of representing information. XML is nowadays used as a data model for storing and querying large volumes of data in database systems. In spite of significant research and systems development, many performance problems are raised by processing very large amounts of XML data. Materialized views have long been used in databases to speed up queries. Materialized views can be seen as precomputed query results that can be re-used to evaluate (part of) another query, and have been a topic of intensive research, in particular in the context of relational data warehousing. This thesis investigates the applicability of materialized views techniques to optimize the performance of Web data management tools, in particular in distributed settings, considering XML data and queries. We make three contributions.We first consider the problem of choosing the best views to materialize within a given space budget in order to improve the performance of a query workload. Our work is the first to address the view selection problem for a rich subset of XQuery. The challenges we face stem from the expressive power and features of both the query and view languages and from the size of the search space of candidate views to materialize. While the general problem has prohibitive complexity, we propose and study a heuristic algorithm and demonstrate its superior performance compared to the state of the art.Second, we consider the management of large XML corpora in peer-to-peer networks, based on distributed hash tables (or DHTs, in short). We consider a platform leveraging distributed materialized XML views, defined by arbitrary XML queries, filled in with data published anywhere in the network, and exploited to efficiently answer queries issued by any network peer. This thesis has contributed important scalability oriented optimizations, as well as a comprehensive set of experiments deployed in a country-wide WAN. These experiments outgrow by orders of magnitude similar competitor systems in terms of data volumes and data dissemination throughput. Thus, they are the most advanced in understanding the performance behavior of DHT-based XML content management in real settings.Finally, we present a novel approach for scalable content-based publish/subscribe (pub/sub, in short) in the presence of constraints on the available computational resources of data publishers. We achieve scalability by off-loading subscriptions from the publisher, and leveraging view-based query rewriting to feed these subscriptions from the data accumulated in others. Our main contribution is a novel algorithm for organizing subscriptions in a multi-level dissemination network in order to serve large numbers of subscriptions, respect capacity constraints, and minimize latency. The efficiency and effectiveness of our algorithm are confirmed through extensive experiments and a large deployment in a WAN.

This thesis addresses the rational properties of women’s gender identification and self-representation from political theorist Lois McNay’s generative logics, employing the Praedia of Julia Felix, Pompeii, as a case study. Previous debates rooted in semantics and representationalism have focused on non-elite stereotypes or negative gendered dichotomies fostered by comprehensive views on Roman women’s exclusion from public life. In contrast, this thesis adopts a new materialist approach, specifically drawing on feminist theorist Karen Barad’s agential realist method building on intra-activity in order to shed new light on the well-studied subject of how a non-elite woman was able to carve out an existence for herself in patriarchal ancient Roman society. Whereas past research has labelled non-elite Roman women as both passive and unproductive individuals, the present thesis proposes that agencies functioned as lived experiences which determined individuals’ abilities to actively connect with things and surroundings in different ways. Therefore, in order to analyze the interceding effects of agencies on gender identification and self-representation in connection to the Praedia of Julia Felix I argue that a broader view of performativity, embodying processes and materiality is needed. This view calls for a reconceptualization of relational entanglements in which material and social worlds are seen as mutually interconnected rather than separate entities isolated from the bodies responsible for creating these settings. Presenting the results based on a combined analysis of generative and new materialist models, I suggest that the Praedia of Julia Felix demonstrates a non-elite woman’s active participation in creating personal sustainability. This dynamic interplay between Julia Felix and her social surroundings in worth understanding in detail.

This master´s thesis deals with data integration that is used for data transfer within various database systems in both directions - data migration and replication. We become familiar with the technologies of distributed databases. In detail the system of health care providers is described and particular tables involved into its data integration are explored. For the project execution the proposal for integration of this system is created and whereupon following implementation is described.

[pt] O framework Outer-Tuning serve para apoiar a sintonia fina de índices (automática ou não) em um sistema de banco de dados. Trata-se de uma abordagem que oferece transparência acerca das alternativas disponíveis para possíveis cenários de sintonia fina, possibilitando combinar estratégias independentes para obter um melhor desempenho do SGBD e permitindo a discussão de justificativas para as ações realizadas. Através do uso de uma ontologia específica para sintonia fina de bancos de dados relacionais, é possível adicionar semântica ao processo com o entendimento dos conceitos envolvidos e gerar, de maneira (semi)automática, novas práticas de sintonia fina, que podem ser inferidas a partir das práticas existentes ou de novas regras e conceitos que venham a surgir no futuro. Este trabalho de pesquisa apresenta como contribuição inicial o projeto e implementação do framework Outer-Tuning por meio da formalização de uma arquitetura de software que atende aos requisitos funcionais especificados. Este trabalho também contribui com a extensão da ontologia de domínio e a inclusão de novas heurísticas na ontologia de tarefas para contemplar soluções de sintonia fina com o uso de visões materializadas. Desta forma, passa a ser possível propor o uso de heurísticas para realizar a sintonia fina tanto para índices como também para visões materializadas. / [en] The Outer-tuning framework may be used to support automatic (or not) database tuning, particularity index. It is an approach that offers transparency about the available alternatives to feasible tuning scenarios, making it possible to combine either independent strategies or allow discussion of justifications for actions performed in order to obtain better performances. Using a specific ontology for fine tuning relational databases, we add semantics to the process with the understanding of the concepts involved and generate (semi)automatic new tuning actions, which can be inferred from existing practices or new rules and concepts that arise in the future. This research presents as an initial contribution the actual design and implementation of the Outer-tuning framework through the formalization of a software architecture that meets the specified functional requirements. This work also contributes with the extension of the domain ontology and the inclusion of new heuristics to a task ontology, in order to accomplish fine tuning solutions with the use of materialized views. Thus, it becomes possible to propose the use of tuning heuristics for indexes as well as for materialized views.

Near real-time event streams are a key feature in many popular social media applications. These types of applications allow users to selectively follow event streams to receive a curated list of real-time events from various sources. Due to the emphasis on recency, relevance, personalization of content, and the highly variable cardinality of social subgraphs, it is extremely difficult to implement feed following at the scale of major social media applications. This leads to multiple architectural approaches, but no consensus has been reached as to what is considered to be an idiomatic solution. As of today, there are various theoretical approaches exploiting the dynamic nature of social graphs, but not all of them have been applied in practice. In this paper, large-cardinality graphs are placed in the context of existing research to highlight the exceptional data management challenges that are posed for large-scale real-time social media applications. This work outlines the key characteristics of data dissemination in large-cardinality social graphs, and overviews existing research and state-of-the-art approaches in industry, with the goal of stimulating further research in this direction.

Le langage XML, proposé par le W3C, est aujourd’hui utilisé comme un modèle de données pour le stockage et l’interrogation de grands volumes de données dans les systèmes de bases de données. En dépit d’importants travaux de recherche et le développement de systèmes efficace, le traitement de grands volumes de données XML pose encore des problèmes des performance dus à la complexité et hétérogénéité des données ainsi qu’à la complexité des langages courants d’interrogation XML. Les vues matérialisées sont employées depuis des décennies dans les bases de données afin de raccourcir les temps de traitement des requêtes. Elles peuvent être considérées les résultats de requêtes pré-calculées, que l’on réutilise afin d’éviter de recalculer (complètement ou partiellement) une nouvelle requête. Les vues matérialisées ont fait l’objet de nombreuses recherches, en particulier dans le contexte des entrepôts des données relationnelles.Cette thèse étudie l’applicabilité de techniques de vues matérialisées pour optimiser les performances des systèmes de gestion de données Web, et en particulier XML, dans des environnements distribués. Dans cette thèse, nos apportons trois contributions.D’abord, nous considérons le problème de la sélection des meilleures vues à matérialiser dans un espace de stockage donné, afin d’améliorer la performance d’une charge de travail des requêtes. Nous sommes les premiers à considérer un sous-langage de XQuery enrichi avec la possibilité de sélectionner des noeuds multiples et à de multiples niveaux de granularités. La difficulté dans ce contexte vient de la puissance expressive et des caractéristiques du langage des requêtes et des vues, et de la taille de l’espace de recherche de vues que l’on pourrait matérialiser.Alors que le problème général a une complexité prohibitive, nous proposons et étudions un algorithme heuristique et démontrer ses performances supérieures par rapport à l’état de l’art.Deuxièmement, nous considérons la gestion de grands corpus XML dans des réseaux pair à pair, basées sur des tables de hachage distribuées. Nous considérons la plateforme ViP2P dans laquelle des vues XML distribuées sont matérialisées à partir des données publiées dans le réseau, puis exploitées pour répondre efficacement aux requêtes émises par un pair du réseau. Nous y avons apporté d’importantes optimisations orientées sur le passage à l’échelle, et nous avons caractérisé la performance du système par une série d’expériences déployées dans un réseau à grande échelle. Ces expériences dépassent de plusieurs ordres de grandeur les systèmes similaires en termes de volumes de données et de débit de dissémination des données. Cette étude est à ce jour la plus complète concernant une plateforme de gestion de contenus XML déployée entièrement et testée à une échelle réelle.Enfin, nous présentons une nouvelle approche de dissémination de données dans un système d’abonnements, en présence de contraintes sur les ressources CPU et réseau disponibles; cette approche est mise en oeuvre dans le cadre de notre plateforme Delta. Le passage à l’échelle est obtenu en déchargeant le fournisseur de données de l’effort de répondre à une partie des abonnements. Pour cela, nous tirons profit de techniques de réécriture de requêtes à l’aide de vues afin de diffuser les données de ces abonnements, à partir d’autres abonnements.Notre contribution principale est un nouvel algorithme qui organise les vues dans un réseau de dissémination d’information multi-niveaux ; ce réseau est calculé à l’aide d’outils techniques de programmation linéaire afin de passer à l’échelle pour de grands nombres de vues, respecter les contraintes de capacité du système, et minimiser les délais de propagation des information. L’efficacité et la performance de notre algorithme est confirmée par notre évaluation expérimentale, qui inclut l’étude d’un déploiement réel dans un réseau WAN. / XML was recommended by W3C in 1998 as a markup language to be used by device- and system-independent methods of representing information. XML is nowadays used as a data model for storing and querying large volumes of data in database systems. In spite of significant research and systems development, many performance problems are raised by processing very large amounts of XML data. Materialized views have long been used in databases to speed up queries. Materialized views can be seen as precomputed query results that can be re-used to evaluate (part of) another query, and have been a topic of intensive research, in particular in the context of relational data warehousing. This thesis investigates the applicability of materialized views techniques to optimize the performance of Web data management tools, in particular in distributed settings, considering XML data and queries. We make three contributions.We first consider the problem of choosing the best views to materialize within a given space budget in order to improve the performance of a query workload. Our work is the first to address the view selection problem for a rich subset of XQuery. The challenges we face stem from the expressive power and features of both the query and view languages and from the size of the search space of candidate views to materialize. While the general problem has prohibitive complexity, we propose and study a heuristic algorithm and demonstrate its superior performance compared to the state of the art.Second, we consider the management of large XML corpora in peer-to-peer networks, based on distributed hash tables (or DHTs, in short). We consider a platform leveraging distributed materialized XML views, defined by arbitrary XML queries, filled in with data published anywhere in the network, and exploited to efficiently answer queries issued by any network peer. This thesis has contributed important scalability oriented optimizations, as well as a comprehensive set of experiments deployed in a country-wide WAN. These experiments outgrow by orders of magnitude similar competitor systems in terms of data volumes and data dissemination throughput. Thus, they are the most advanced in understanding the performance behavior of DHT-based XML content management in real settings.Finally, we present a novel approach for scalable content-based publish/subscribe (pub/sub, in short) in the presence of constraints on the available computational resources of data publishers. We achieve scalability by off-loading subscriptions from the publisher, and leveraging view-based query rewriting to feed these subscriptions from the data accumulated in others. Our main contribution is a novel algorithm for organizing subscriptions in a multi-level dissemination network in order to serve large numbers of subscriptions, respect capacity constraints, and minimize latency. The efficiency and effectiveness of our algorithm are confirmed through extensive experiments and a large deployment in a WAN.

Dans cette thèse, nous nous intéressons au problème de la confidentialité des données. Nous proposons une nouvelle approche pour faciliter l'administration des règles de contrôle d'accès pour assurer la confidentialité des données au niveau des vues matérialisées. Dans les bases de données relationnelles, une vue est une table virtuelle représentant le résultat d'une requête. À la différence d'une vue simple, une vue matérialisée stocke le résultat de la requête dans une table. Cette dernière peut être alors interrogée comme une table quelconque. Il est donc important d'y contrôler l'accès. Parmi les différents modèles proposés pour contrôler l'accès aux relations de base, nous nous basons dans notre approche sur l'utilisation des vues d'autorisations pour exprimer des règles de contrôle d'accès à grains fins. Nous proposons d'inférer, à partir des vues d'autorisations attachées aux tables de base, les vues d'autorisations qui doivent être attachées aux vues matérialisées. Répondre à ce problème revient à répondre à un problème fondamental dans les bases de données relationnelles : Comment caractériser les informations calculables à partir de deux ensembles de vues ? Nous répondons à cette question en nous appuyant sur la réécriture de requêtes. Nous adaptons l'algorithme de réécriture de requêtes M iniCon aux spécificités de notre problème et nous proposons l'algorithme 1-l M iniCon+ qui se base sur un enchainement de réécritures. Nous nous intéressons aux vues représentées par des requêtes conjonctives en autorisant les égalités. Nous nous sommes intéressés par la suite aux propriétés de cet algorithme. Nous démontrons que cet algorithme permet de calculer un ensemble de vues correctes, c.-à-d. toute information calculable à partir de l'ensemble de vues générées est cal­ culable à partir de chacun des deux ensembles de vues de départ / In this thesis, we address the problem of data confidentiality. We propose a new approach to facilitate the administration of access control policies to ensure confidentiality of data in materialized views. In relational databases, a view is a virtual table representing the result of a query. Unlike a simple view, a materialized view persistently stores the data in a table. The latter can be queried like any other database table. We then need to control the access to the materialized view. Among the various models proposed for controlling access to base relations, we choose to express fine-grained access control through authorization views. We propose to infer, from the basic authorization views attached to the base tables, authorization views that will be attached to the materialized views. Tackling this problem amounts to address a fundamental problem in relational databases : How to characterize computable information from two sets of views ? We handle this problem by resorting to query rewriting. We adapt the query rewriting algorithm MiniCon to the context of materialized views with access control and propose the H MiniCon+ algorithm which is based on successive rewritings. We mainly consider conjunctive queries with equalities. We study the properties of our approach. We show that our algorithm can calculate a correct set of views, i.e. any computable information from the generated views is calculable from the two sets of views. In order to prove the termination of our algorithm, we define rewriting trees generated by the application of 1-l MiniCon+ and we study their features. We characterize in which case a tree is finite and show that the approach is maximal, i.e., any derivable information from the two sets of views can be derived from the set of generated views. We characterize in which case the algorithm could not terminate i.e., infinite application of the query rewriting algorithm. In this case, it is impossible to determine the maximality of results and this remains an open problem. We implemented a prototype of the approach and we led some experiments by using synthetic data sets