The trend towards smaller transistor technologies and lower operating voltages stresses the hardware and makes transistors more susceptible to transient errors. In future systems, performance and power gains will come at the cost of unreliable areas on the chip. For this reason, there is an increased need for low-overhead highly-reliable error detection methodologies. In the last years, several techniques have been proposed. The majority of them are based on redundancy which can be implemented at several levels (e.g., hardware, instruction, thread, process, etc). In instruction-level error detection approaches, the compiler replicates the instructions of the program and inserts checks wherever they are needed. The checks evaluate code correctness and decide whether or not an error has occurred. This type of error detection is more flexible than the hardware alternatives. It allows the programmer to choose the protected area of the program and it can be applied without any hardware modifications. On the other hand, the replicated instructions and the checks cause a large slowdown making software techniques less appealing. In this thesis, we propose two techniques that aim at reducing the error detection overhead of compiler-based approaches and improving system’s performance without sacrificing the fault-coverage. The first technique, DRIFT, achieves this by decoupling the execution of the code (original and replicated) from the checks. The checks are compare and jump instructions. The latter ones tend to make the code sequential and prohibit the compiler from performing aggressive instruction scheduling optimizations. We call this phenomenon basic-block fragmentation. DRIFT reduces the impact of basic-block fragmentation by breaking the synchronized execute-check-confirm-execute cycle. In this way, DRIFT generates a scheduler-friendly code with more instruction-level parallelism (ILP). As a result, it reduces the performance overhead down to 1.29× (on average) and outperforms the state-of-the-art by up to 29.7% retaining the same fault-coverage. Next, CASTED focuses on reducing the impact of error detection overhead on single-chip scalable architectures that are composed of tightly-coupled cores. The proposed compiler methodology adaptively distributes the error detection overhead to the available resources across multiple cores, fully exploiting the abundant ILP of these architectures. CASTED adapts to a wide range of architecture configurations (issue-width, inter-core communication). The results show that CASTED matches the performance of, and often outperforms, sometimes by as mush as 21.2%, the best fixed state-of-the-art approach while maintaining the same fault coverage.

Users of current web search engines are often presented with a large number of returns after submitting a search term and choosing from the list might lead to them suffering from the effect of “choice overload”, as reported in earlier work. However, these search results are typically presented in an ordered list so as to simplify the search process, which may influence search behaviour and moderate the effect of number of choices. In this thesis, the effects of the number of search returns and their ordering on user behaviour and satisfaction are explored. A mixed methods approach combining multiple data collection and analysis techniques is employed in order to investigate these effects in terms of three specific issues, namely, choice overload in search engine use, variety seeking behaviour in a situation where multiple aspects of search results are required, and the chance of encountering serendipity. The participants were given search tasks and asked to choose from the sets of returns under experimental conditions. The results from the first three experiments revealed that large numbers of search results returned from a search engine tended to be associated with more satisfaction with the selected options when the decision was made without a time limit. In addition, when time was more strongly constrained the choices from a small number of returns led to relatively higher satisfaction than for a large number. Moreover, users’ behaviour was strongly influenced by the ordering of options in that they often looked and selected options presented near the top of the result lists when they perceived the ranking was reliable. The next experiment further investigated the ranking reliance behaviour when potentially useful search results were presented in supplementary lists. The findings showed that when users required a variety of options, they relied less on the ordering and tended to adapt their search strategies to seek variety by browsing more returns through the list, selecting options located further down, and/or choosing the supplementary web pages provided. Finally, with the aim of illustrating how chance encountering can be supported, a model of an automated synonym-enhanced search was developed and employed in a real-world literature search. The results showed that the synonym search was occasionally useful for providing a variety of search results, which in turn increased users’ opportunity to come across serendipitous experiences.

Queries submitted to a web search engine are typically short and often ambiguous. With the enormous size of the Web, a misunderstanding of the information need underlying an ambiguous query can misguide the search engine, ultimately leading the user to abandon the originally submitted query. In order to overcome this problem, a sensible approach is to diversify the documents retrieved for the user's query. As a result, the likelihood that at least one of these documents will satisfy the user's actual information need is increased. In this thesis, we argue that an ambiguous query should be seen as representing not one, but multiple information needs. Based upon this premise, we propose xQuAD---Explicit Query Aspect Diversification, a novel probabilistic framework for search result diversification. In particular, the xQuAD framework naturally models several dimensions of the search result diversification problem in a principled yet practical manner. To this end, the framework represents the possible information needs underlying a query as a set of keyword-based sub-queries. Moreover, xQuAD accounts for the overall coverage of each retrieved document with respect to the identified sub-queries, so as to rank highly diverse documents first. In addition, it accounts for how well each sub-query is covered by the other retrieved documents, so as to promote novelty---and hence penalise redundancy---in the ranking. The framework also models the importance of each of the identified sub-queries, so as to appropriately cater for the interests of the user population when diversifying the retrieved documents. Finally, since not all queries are equally ambiguous, the xQuAD framework caters for the ambiguity level of different queries, so as to appropriately trade-off relevance for diversity on a per-query basis. The xQuAD framework is general and can be used to instantiate several diversification models, including the most prominent models described in the literature. In particular, within xQuAD, each of the aforementioned dimensions of the search result diversification problem can be tackled in a variety of ways. In this thesis, as additional contributions besides the xQuAD framework, we introduce novel machine learning approaches for addressing each of these dimensions. These include a learning to rank approach for identifying effective sub-queries as query suggestions mined from a query log, an intent-aware approach for choosing the ranking models most likely to be effective for estimating the coverage and novelty of multiple documents with respect to a sub-query, and a selective approach for automatically predicting how much to diversify the documents retrieved for each individual query. In addition, we perform the first empirical analysis of the role of novelty as a diversification strategy for web search. As demonstrated throughout this thesis, the principles underlying the xQuAD framework are general, sound, and effective. In particular, to validate the contributions of this thesis, we thoroughly assess the effectiveness of xQuAD under the standard experimentation paradigm provided by the diversity task of the TREC 2009, 2010, and 2011 Web tracks. The results of this investigation demonstrate the effectiveness of our proposed framework. Indeed, xQuAD attains consistent and significant improvements in comparison to the most effective diversification approaches in the literature, and across a range of experimental conditions, comprising multiple input rankings, multiple sub-query generation and coverage estimation mechanisms, as well as queries with multiple levels of ambiguity. Altogether, these results corroborate the state-of-the-art diversification performance of xQuAD.

We study representation systems for relational data based on relational algebra expressions with unions, products, and singleton relations. Algebraic factorisation using the distributivity of product over union allows succinct representation of many-to-many relationships; further succinctness is brought by sharing repeated subexpressions. We show that these techniques are especially applicable to results of conjunctive queries. In the first part of the dissertation we derive tight asymptotic size bounds for two flavours of factorised representations of results of conjunctive queries. Any conjunctive query is characterised by rational parameters that govern the factorisability of its results independently of the database instance. We relate these parameters to fractional edge covers and fractional hypertree decompositions. Factorisation naturally extends from relational data to its provenance. We characterise conjunctive queries by tight bounds on their readability, which captures how many times each input tuple is used to contribute to an output tuple, and we define syntactically the class of queries with bounded readability. In the second part of the dissertation we describe FDB, a relational database engine that uses factorised representations at the physical layer to reduce data redundancy and boost query performance. We develop algorithms for optimisation and evaluation of queries with selection, projection, join, aggregation and order-by clauses on factorised representations. By introducing novel operators for factorisation restructuring and a new optimisation objective to maintain intermediate and final results succinctly factorised, we allow query evaluation with lower time complexity than on flat relations. Experiments show that for data sets with many-to-many relationships, FDB can outperform relational engines by orders of magnitude.

One of the most popular challenges facing academia and industry is the development of effective techniques and tools for maximizing the availability of data as the most valuable source of knowledge. The internet has dominated as the core for maximizing data availability and XML (eXtensible Markup Language) has emerged and is being gradually accepted as the universal standard format for platform independent publishing and exchanging data over the Internet. On the other hand, there remain large amount of data held in structured databases and database management systems have been traditionally used for the effective storage and manipulation of large volumes of data. This raised the need for effective methodologies capable of smoothly transforming data between different formats in general and between XML and structured databases in particular. This dissertation addresses the issue by proposing a two-way mapping approach between XML and object-oriented databases. The basic steps of the proposed approach are applied in a systematic way to produce a graph from the source and then transform the graph into the destination format. In other words, the derived graph summarizes characteristics of the source whether XML (elements and attributes) or object-oriented database (classes, inheritance and nesting hierarchies). Then, the developed methodology classifies nodes and links from the graph into the basic constructs of the destination, i.e., elements and attributes for XML or classes, inheritance and nesting hierarchies for object-oriented databases. The methodology has been successfully implemented and illustrative case studies are presented in this document.

Un nombre croissant de sources de données est publié sur le Web, décrites dans les langages proposés par le W3C tels que RDF, RDF(S) et OWL. Une quantité de données sans précédent est ainsi disponible pour les utilisateurs et les applications, mais l'exploitation pertinente de ces sources constitue encore un défi : l'interrogation des sources est en effet limitée d'abord car elle suppose la maîtrise d'un langage de requêtes tel que SPARQL, mais surtout car elle suppose une certaine connaissance de la source de données qui permet de cibler les ressources et les propriétés pertinentes pour les besoins spécifiques des applications. Le travail présenté ici s'intéresse à l'exploration de sources de données RDF, et ce selon deux axes complémentaires : découvrir d'une part les thèmes sur lesquels porte la source de données, fournir d'autre part un support pour l'interrogation d'une source sans l'utilisation de langage de requêtes, mais au moyen de mots clés. L'approche d'exploration proposée se compose ainsi de deux stratégies complémentaires : l'exploration thématique et la recherche par mots clés. La découverte de thèmes dans une source de données RDF consiste à identifier un ensemble de sous-graphes, non nécessairement disjoints, chacun représentant un ensemble cohérent de ressources sémantiquement liées et définissant un thème selon le point de vue de l'utilisateur. Ces thèmes peuvent être utilisés pour permettre une exploration thématique de la source, où les utilisateurs pourront cibler les thèmes pertinents pour leurs besoins et limiter l'exploration aux seules ressources composant les thèmes sélectionnés. La recherche par mots clés est une façon simple et intuitive d'interroger les sources de données. Dans le cas des sources de données RDF, cette recherche pose un certain nombre de problèmes, comme l'indexation des éléments du graphe, l'identification des fragments du graphe pertinents pour une requête spécifique, l'agrégation de ces fragments pour former un résultat, et le classement des résultats obtenus. Nous abordons dans cette thèse ces différents problèmes, et nous proposons une approche qui permet, en réponse à une requête mots clés, de construire une liste de sous-graphes et de les classer, chaque sous-graphe correspondant à un résultat pertinent pour la requête. Pour chacune des deux stratégies d'exploration d'une source RDF, nous nous sommes intéressés à prendre en compte de la connaissance externe, permettant de mieux répondre aux besoins des utilisateurs. Cette connaissance externe peut représenter des connaissances du domaine, qui permettent de préciser le besoin exprimé dans le cas d'une requête, ou de prendre en compte des connaissances permettant d'affiner la définition des thèmes. Dans notre travail, nous nous sommes intéressés à formaliser cette connaissance externe et nous avons pour cela introduit la notion de pattern. Ces patterns représentent des équivalences de propriétés et de chemins dans le graphe représentant la source. Ils sont évalués et intégrés dans le processus d'exploration pour améliorer la qualité des résultats. / An increasing number of datasets is published on the Web, expressed in languages proposed by the W3C to describe Web data such as RDF, RDF(S) and OWL. The Web has become a unprecedented source of information available for users and applications, but the meaningful usage of this information source is still a challenge. Querying these data sources requires the knowledge of a formal query language such as SPARQL, but it mainly suffers from the lack of knowledge about the source itself, which is required in order to target the resources and properties relevant for the specific needs of the application. The work described in this thesis addresses the exploration of RDF data sources. This exploration is done according to two complementary ways: discovering the themes or topics representing the content of the data source, and providing a support for an alternative way of querying the data sources by using keywords instead of a query formulated in SPARQL. The proposed exploration approach combines two complementary strategies: thematic-based exploration and keyword search. Theme discovery from an RDF dataset consists in identifying a set of sub-graphs which are not necessarily disjoints, and such that each one represents a set of semantically related resources representing a theme according to the point of view of the user. These themes can be used to enable a thematic exploration of the data source where users can target the relevant theme and limit their exploration to the resources composing this theme. Keyword search is a simple and intuitive way of querying data sources. In the case of RDF datasets, this search raises several problems, such as indexing graph elements, identifying the relevant graph fragments for a specific query, aggregating these relevant fragments to build the query results, and the ranking of these results. In our work, we address these different problems and we propose an approach which takes as input a keyword query and provides a list of sub-graphs, each one representing a candidate result for the query. These sub-graphs are ordered according to their relevance to the query. For both keyword search and theme identification in RDF data sources, we have taken into account some external knowledge in order to capture the users needs, or to bridge the gap between the concepts invoked in a query and the ones of the data source. This external knowledge could be domain knowledge allowing to refine the user's need expressed by a query, or to refine the definition of themes. In our work, we have proposed a formalization to this external knowledge and we have introduced the notion of pattern to this end. These patterns represent equivalences between properties and paths in the dataset. They are evaluated and integrated in the exploration process to improve the quality of the result.