In this dissertation, we present a novel foundational framework that lays out a theoretical underpinning for the emerging services science. The proposed framework provides disciplined and systematic support for efficient access to Web services' functionalities. The key components of the proposed framework centers around a novel service model that provides a formal abstraction of the Web services within an application domain. A service calculus and a service algebra are defined to facilitate users in accessing services via declarative service queries. We provide the implementation of the service algebra. This enables the generation of Service Execution Plans (SEPs) that can be used by users to directly access services. We present an optimization algorithm to efficiently select the SEPs with the best QoWS. We then propose a multi-objective optimization approach that releases users from the tedious weight assigning process. We develop service skyline computation techniques that return a set of most interesting SEPs. The service skyline guarantees to include the user desired SEPs. We further explore a set of novel heuristics for computing service skylines over sets of services. This enables users to efficiently and optimally access multiple services simultaneously as an integrated service package. Finally, we consider the performance fluctuation of service providers due to the dynamic service environment. We propose an uncertain QoWS model and a novel concept called p-dominant service skyline. We develop new indexing structures and algorithms to efficiently compute the p-dominant service skyline. We derive analytical models and conduct extensive sets of experiments to evaluate the proposed framework and service query optimization algorithms. / Ph. D.

This dissertation addresses issues for the efficient access to Web databases and services. We propose a distributed ontology for a meaningful organization of and efficient access to Web databases. Next, we dedicate most of our work on presenting a comprehensive query infrastructure for the emerging concept of Web services. The core of this query infrastructure is to enable the efficient delivery of Web services based on the concept of Quality of Web Service. Treating Web services as first class objects is a fundamental step towards achieving the envisioned Semantic Web. Semantics-aware processing of information requires intensive use of Web services. In our research, we propose a new query model where queries are resolved by combining Web service invocations. To efficiently deploy such scheme, we propose an optimization strategy based on aggregating Quality of Web service (QoWS) of different Web services. QoWS is adjusted through a dynamic rating scheme and multilevel matching. Web service rating provides an assessment of their behavior. The multilevel matching allows a larger solution space by enabling similar and partial answers. / Ph. D.

In this thesis, alternative plan generation methods for multiple query optimization(MQO) are introduced and an implementation in the C++ programming.language has been developed. Multiple query optimization, aims to minimize the total cost of executing a set of relational database queries. In traditional single query optimization only the cost of execution of a single relational database query is minimized. In single query optimization a search is performed to investigate possible alternative methods of accessing relational database tables and alternative methods of performing join operations in the case of multi-relation queries where records from two or more relational tables have to be brought together using one of the join algortihms (e.g. nested loops, sort merge, hash join,etc). The choice of join method depends on the availability of indexes, amount of available main memory, the existence of ORDER BY clause for sorted output, the sizes of involved relations, many other factors. A simple way of performing multiple query optimization is to take the query execution plans generated for each of the queries as input to a MQO algorithm, and then try to identify common tasks in those plans using the MQO algorithm. However, this approach will reduce the achievable benefits since a more expensive execution plan (thus discarded by a single query optimizer) could have more common operations with other query execution plans, resulting in a lower total cost for MQO. .For this purpose we will introduce several methods for generating such potentially beneficial alternative query execution plans and experimentaly evaluate and compare their performances.

Modern database systems use a query optimizer to identify the most efficient strategy, called “query execution plan”, to execute declarative SQL queries. The role of the query optimizer is especially critical for the complex decision-support queries featured in current data warehousing and data mining applications. Given an SQL query template that is parametrized on the selectivities of the participating base relations and a choice of query optimizer, a plan diagram is a color-coded pictorial enumeration of the execution plan choices of the optimizer over the query parameter space. Complementary to the plan-diagrams are cost and cardinality diagrams which graphically plot the estimated execution costs and cardinalities respectively, over the query parameter space. These diagrams are collectively known as optimizer diagrams. Optimizer diagrams have proved to be a powerful tool for the analysis and redesign of modern optimizers, and are gaining interest in diverse industrial and academic institutions. However, their utility is adversely impacted by the impractically large computational overheads incurred when standard brute-force approaches are used for producing fine-grained diagrams on high-dimensional query templates. In this thesis, we investigate strategies for efficiently producing close approximations to complex optimizer diagrams. Our techniques are customized for different classes of optimizers, ranging from the generic Class I optimizers that provide only the optimal plan for a query, to Class II optimizers that also support costing of sub-optimal plans and Class III optimizers which offer enumerated rank-ordered lists of plans in addition to both the former features. For approximating plan diagrams for Class I optimizers, we first present database oblivious techniques based on classical random sampling in conjunction with nearest neighbor (NN) inference scheme. Next we propose grid sampling algorithms which consider database specific knowledge such as(a) the structural differences between the operator trees of plans on the grid locations and (b) parametric query optimization principle. These algorithms become more efficient when modified to exploit the sub-optimal plan costing feature available with Class II optimizers. The final algorithm developed for Class III optimizers assume plan cost monotonicity and utilize the rank-ordered lists of plans to efficiently generate completely accurate optimizer diagrams. Subsequently, we provide a relaxed variant, which trades quality of approximation, for reduction in diagram generation overhead. Our proposed algorithms are capable of terminating according to user given error bound for plan diagram approximation. For approximating cost diagrams, our strategy is based on linear least square regression performed on a mathematical model of plan cost behavior over the parameter space, in conjunction with interpolation techniques. Game theoretic and linear programming approaches have been employed to further reduce the error in cost approximation. For approximating cardinality diagrams, we propose a novel parametrized mathematical model as a function of selectivities for characterizing query cardinality behavior. The complete cardinality model is constructed by clustering the data points according to their cardinality values and subsequently fitting the model through linear least square regression technique separately for each cluster. For non-sampled data points the cardinality values are estimated by first determining the cluster they belong to and then interpolating the cardinality value according to the suitable model. Extensive experimentation with a representative set of TPC-H and TPC-DS-based query templates on industrial-strength optimizers indicates that our techniques are capable of delivering 90% accurate optimizer diagrams while incurring no more than 20% of the computational overheads of the exhaustive approach. Infact, for full-featured optimizers, we can guarantee zero error optimizer diagrams which usually require less than 10% overheads. Our results exhibit that (a) the approximation is materially faithful to the features of the exact optimizer diagram, with the errors thinly spread across the picture and Largely confined to the plan transition boundaries and (b) the cost increase at the non-sampled point due to assignment of sub-optimal plan is also limited. These approximation techniques have been implemented in the publicly available Picasso optimizer visualizer tool. We have also modified PostgreSQL’s optimizer to incorporate costing of sub-optimal plans and enumerating rank-ordered lists of plans. In addition to these, we have designed estimators for predicting the time overhead involved in approximating optimizer diagrams with regard to user given error bounds. In summary, this thesis demonstrates that accurate approximations to exact optimizer diagrams can indeed be obtained cheaply and consistently, with typical overheads being an order of magnitude lower than the brute-force approach. We hope that our results will encourage database vendors to incorporate the foreign-plan-costing and plan-rank-list features in their optimizer APIs.

Données Liées est un terme pour définir un ensemble de meilleures pratiques pour la publication et l'interconnexion des données structurées sur le Web. A mesure que le nombre de fournisseurs de Données Liées augmente, le Web devient un vaste espace de données global. La fédération de requêtes est l'une des approches permettant d'interroger efficacement cet espace de données distribué. Il est utilisé via un moteur de requêtes fédéré qui vise à minimiser le temps de réponse du premier tuple du résultat et le temps d'exécution pour obtenir tous les tuples du résultat. Il existe trois principales étapes dans un moteur de requêtes fédéré qui sont la sélection de sources de données, l'optimisation de requêtes et l'exécution de requêtes. La plupart des études sur l'optimisation de requêtes dans ce contexte se concentrent sur l'optimisation de requêtes statique qui génère des plans d'exécution de requêtes avant l'exécution et nécessite des statistiques. Cependant, l'environnement des Données Liées a plusieurs caractéristiques spécifiques telles que les taux d'arrivée de données imprévisibles et les statistiques peu fiables. En conséquence, l'optimisation de requêtes statique peut provoquer des plans d'exécution inefficaces. Ces contraintes montrent que l'optimisation de requêtes adaptative est une nécessité pour le traitement de requêtes fédéré sur les données liées. Dans cette thèse, nous proposons d'abord un opérateur de jointure adaptatif qui vise à minimiser le temps de réponse et le temps d'exécution pour les requêtes fédérées sur les endpoints SPARQL. Deuxièmement, nous étendons la première proposition afin de réduire encore le temps d'exécution. Les deux propositions peuvent changer la méthode de jointure et l'ordre de jointures pendant l'exécution en utilisant une optimisation de requêtes adaptative. Les opérateurs adaptatifs proposés peuvent gérer différents taux d'arrivée des données et le manque de statistiques sur des relations. L'évaluation de performances dans cette thèse montre l'efficacité des opérateurs adaptatifs proposés. Ils offrent des temps d'exécution plus rapides et presque les mêmes temps de réponse, comparé avec une jointure par hachage symétrique. Par rapport à bind join, les opérateurs proposés se comportent beaucoup mieux en ce qui concerne le temps de réponse et peuvent également offrir des temps d'exécution plus rapides. En outre, le deuxième opérateur proposé obtient un temps de réponse considérablement plus rapide que la bind-bloom join et peut également améliorer le temps d'exécution. Comparant les deux propositions, la deuxième offre des temps d'exécution plus rapides que la première dans toutes les conditions. En résumé, les opérateurs de jointure adaptatifs proposés présentent le meilleur compromis entre le temps de réponse et le temps d'exécution. Même si notre objectif principal est de gérer différents taux d'arrivée des données, l'évaluation de performance révèle qu'ils réussissent à la fois avec des taux d'arrivée de données fixes et variés. / Linked Data is a term to define a set of best practices for publishing and interlinking structured data on the Web. As the number of data providers of Linked Data increases, the Web becomes a huge global data space. Query federation is one of the approaches for efficiently querying this distributed data space. It is employed via a federated query engine which aims to minimize the response time and the completion time. Response time is the time to generate the first result tuple, whereas completion time refers to the time to provide all result tuples. There are three basic steps in a federated query engine which are data source selection, query optimization, and query execution. This thesis contributes to the subject of query optimization for query federation. Most of the studies focus on static query optimization which generates the query plans before the execution and needs statistics. However, the environment of Linked Data has several difficulties such as unpredictable data arrival rates and unreliable statistics. As a consequence, static query optimization can cause inefficient execution plans. These constraints show that adaptive query optimization should be used for federated query processing on Linked Data. In this thesis, we first propose an adaptive join operator which aims to minimize the response time and the completion time for federated queries over SPARQL endpoints. Second, we extend the first proposal to further reduce the completion time. Both proposals can change the join method and the join order during the execution by using adaptive query optimization. The proposed operators can handle different data arrival rates of relations and the lack of statistics about them. The performance evaluation of this thesis shows the efficiency of the proposed adaptive operators. They provide faster completion times and almost the same response times, compared to symmetric hash join. Compared to bind join, the proposed operators perform substantially better with respect to the response time and can also provide faster completion times. In addition, the second proposed operator provides considerably faster response time than bind-bloom join and can improve the completion time as well. The second proposal also provides faster completion times than the first proposal in all conditions. In conclusion, the proposed adaptive join operators provide the best trade-off between the response time and the completion time. Even though our main objective is to manage different data arrival rates of relations, the performance evaluation reveals that they are successful in both fixed and different data arrival rates.

Computing hardware is changing from systems with homogeneous CPUs to systems with heterogeneous computing units like GPUs, Many Integrated Cores, or FPGAs. This trend is caused by scaling problems of homogeneous systems, where heat dissipation and energy consumption is limiting further growths in compute-performance. Heterogeneous systems provide differently optimized computing hardware, which allows different operations to be computed on the most appropriate computing unit, resulting in faster execution and less energy consumption. For database systems, this is a new opportunity to accelerate query processing, allowing faster and more interactive querying of large amounts of data. However, the current hardware trend is also a challenge as most database systems do not support heterogeneous computing resources and it is not clear how to support these systems best. In the past, mainly single operators were ported to different computing units showing great results, while missing a system wide application. To efficiently support heterogeneous systems, a systems approach for query processing and query optimization is needed. In this thesis, we tackle the optimization challenge in detail. As a starting point, we evaluate three different approaches on isolated use-cases to assess their advantages and limitations. First, we evaluate a fork-join approach of intra-operator parallelism, where the same operator is executed on multiple computing units at the same time, each execution with different data partitions. Second, we evaluate using one computing unit statically to accelerate one operator, which provides high code-optimization potential, due to this static and pre-known usage of hardware and software. Third, we evaluate dynamically placing operators onto computing units, depending on the operator, the available computing hardware, and the given data sizes. We argue that the first and second approach suffer from multiple overheads or high implementation costs. The third approach, dynamic placement, shows good performance, while being highly extensible to different computing units and different operator implementations. To automate this dynamic approach, we first propose general placement optimization for query processing. This general approach includes runtime estimation of operators on different computing units as well as two approaches for defining the actual operator placement according to the estimated runtimes. The two placement approaches are local optimization, which decides the placement locally at run-time, and global optimization, where the placement is decided at compile-time, while allowing a global view for enhanced data sharing. The main limitation of the latter is the high dependency on cardinality estimation of intermediate results, as estimation errors for the cardinalities propagate to the operator runtime estimation and placement optimization. Therefore, we propose adaptive placement optimization, allowing the placement optimization to become fully independent of cardinalities estimation, effectively eliminating the main source of inaccuracy for runtime estimation and placement optimization. Finally, we define an adaptive placement sequence, incorporating all our proposed techniques of placement optimization. We implement this sequence as a virtualization layer between the database system and the heterogeneous hardware. Our implementation approach bases on preexisting interfaces to the database system and the hardware, allowing non-intrusive integration into existing database systems. We evaluate our techniques using two different database systems and two different OLAP benchmarks, accelerating the query processing through heterogeneous execution.

Dans cette thèse, nous proposons un cadre générique d'optimisation de requêtes dans les environnements hétérogènes répartis. Nous proposons un modèle générique de description de sources (GSD), qui permet de décrire tous les types d'informations liées au traitement et à l'optimisation de requêtes. Avec ce modèle, nous pouvons en particulier obtenir les informations de coût afin de calculer le coût des différents plans d'exécution. Notre cadre générique d'optimisation fournit les fonctions unitaires permettant de mettre en œuvre les procédures d'optimisation en appliquant différentes stratégies de recherche. Nos résultats expérimentaux mettent en évidence la précision du calcul de coût avec le modèle GSD et la flexibilité de notre cadre générique d'optimisation lors du changement de stratégie de recherche. Notre cadre générique d'optimisation a été mis en œuvre et intégré dans un produit d'intégration de données (DVS) commercialisé par l'entreprise Xcalia - Progress Software Corporation. Pour des requêtes contenant beaucoup de jointures inter-site et interrogeant des sources de grand volume, le temps de calcul du plan optimal est de l'ordre de 2 secondes et le temps d'exécution du plan optimal est réduit de 28 fois par rapport au plan initial non optimisé. / This thesis proposes a generic framework for query optimization in heterogeneous and distributed environments. We propose a generic source description model (GSD), which allows describing any type of information related to query processing and optimization. With GSD, we can use cost information to calculate the costs of execution plans. Our generic framework for query optimization provides a set of unitary functions used to perform optimization by applying different search strategies. Our experimental results show the accuracy of cost calculus when using GSD, and the flexibility of our generic framework when changing search strategies. Our proposed approach has been implemented and integrated in a data integration product (DVS) licensed by Xcalia – Progress Software Corporation. For queries with many inter-site joins accessing large size data sources, the time used for finding the optimal plan is in the order of 2 seconds, and the execution time of the optimized plan is reduced by 28 times, as compared with the execution time of the non optimized original plan.

Consultas por similaridade constituem um paradigma de busca que fornece suporte à diversas tarefas computacionais, tais como agrupamento, classificação e recuperação de informação. Neste contexto, medir a similaridade entre objetos requer comparar a distância entre eles, o que pode ser formalmente modelado pela teoria de espaços métricos. Recentemente, um grande esforço de pesquisa tem sido dedicado à inclusão de consultas por similaridade em Sistemas Gerenciadores de Bases de Dados (SGBDs), com o objetivo de (i) permitir a combinação de comparações por similaridade com as comparações por identidade e ordem já existentes em SGBDs e (ii) obter escalabilidade para grandes bases de dados. Nesta tese, procuramos dar um próximo passo ao estendermos também o otimizador de consultas de um SGBD. Em particular, propomos a ampliação de dois módulos do otimizador: o módulo de Espaço de Distribuição de Dados e o módulo de Modelo de Custo. Ainda que o módulo de Espaço de Distribuição de Dados permita representar os dados armazenados, essas representações são insuficientes para modelar o comportamento das comparações em espaços métricos, sendo necessário estender este módulo para contemplar distribuições de distância. De forma semelhante, o módulo Modelo de Custo precisa ser ampliado para dar suporte à modelos de custo que utilizem estimativas sobre distribuições de distância. Toda a investigação aqui conduzida se concentra em cinco contribuições. Primeiro, foi criada uma nova sinopse para distribuições de distância, o Histograma Compactado de Distância (CDH), de onde é possível inferir valores de seletividade e raios para consultas por similaridade. Uma comparação experimental permitiu mostrar os ganhos das estimativas da sinopse CDH com relação à diversos competidores. Também foi proposto um modelo de custo baseado na sinopse CDH, o modelo Stockpile, cujas estimativas se mostraram mais precisas na comparação com outros modelos. Os Histogramas-Omni são apresentados como a terceira contribuição desta tese. Estas estruturas de indexação, construídas a partir de restrições de particionamento de histogramas, permitem a execução otimizada de consultas que mesclam comparações por similaridade, identidade e ordem. A quarta contribuição de nossa investigação se refere ao modelo RVRM, que é capaz de indicar quanto é possível empregar as estimativas das sinopses de distância para otimizar consultas por similaridade em conjuntos de dados de alta dimensionalidade. O modelo RVRM se mostrou capaz de identificar intervalos de dimensões para os quais essas consultas podem ser executadas eficientes. Finalmente, a última contribuição desta tese propõe a integração das sinopses e modelos revisados em um sistema com sintaxe de alto nível que pode ser acoplado em um otimizador de consultas. / Similarity searching is a foundational paradigm for many modern computer applications, such as clustering, classification and information retrieval. Within this context, the meaning of similarity is related to the distance between objects, which can be formally expressed by the Metric Spaces Theory. Many studies have focused on the inclusion of similarity search into Database Management Systems (DBMSs) for (i) enabling similarity comparisons to be combined with the DBMSs identity and order comparisons and (ii) providing scalability for very large databases. As a step further, we propose the extension of the DBMS Query Optimizer and, particularly, the extension of two modules of the Query Optimizer, namely Data Distribution Space and Cost Model modules. Although the Data Distribution Space enables representations of stored data, such representations are unsuitable for modeling the behavior of similarity comparisons, which requires the extension of the module to support distance distributions. Likewise, the Cost Model module must be extended to support cost models that depend on distance distributions. Our study is based on five contributions. A new synopsis for distance distributions, called Compact-Distance Histogram (CDH), is proposed and enables radius and selectivity estimation for similarity searching. An experimental comparison showed the gains of the estimates drawn from CDH in comparison to several competitors. A cost model based on the CDH synopsis and with accurate estimates, called Stockpile, is also proposed. Omni-Histograms are presented as the third contribution of the thesis. Such indexing structures are constructed according to histogram partition constraints and enable the optimization of queries that combine similarity, identity and order comparisons. The fourth contribution refers to the model RVRM, which indicates the possible use of the estimates obtained from distance-based synopses for the query optimization of high-dimensional datasets and identifies intervals of dimensions where similarity searching can be efficiently executed. Finally, the thesis proposes the integration of the reviewed synopses and cost models into a single system with a high-level language that can be coupled to a DBMS Query Optimizer.

XML stream applications bring the challenge of efficiently processing queries on sequentially accessible token-based data streams. The automaton paradigm is naturally suited for pattern retrieval on tokenized XML streams, but requires patches for implementing the filtering or restructuring functionalities common for the XML query languages. In contrast, the algebraic paradigm is well-established for processing self-contained tuples. However, it does not traditionally support token inputs. This dissertation proposes a framework called Raindrop, which accommodates both the automaton and algebra paradigms to take advantage of both. First, we propose an architecture for Raindrop. Raindrop is an algebra framework that models queries at different abstraction levels. We represent the token-based automaton computations as an algebraic subplan at the high level while exposing the automaton details at the low level. The algebraic subplan modeling automaton computations can thus be integrated with the algebraic subplan modeling the non-automaton computations. Second, we explore a novel optimization opportunity. Other XML stream processing systems always retrieve all the patterns in a query in the automaton. In contrast, Raindrop allows a plan to retrieve some of the pattern retrieval in the automaton and some out of the automaton. This opens up an automaton-in-or-out optimization opportunity. We study this optimization in two types of run-time environments, one with stable data characteristics and one with fluctuating data characteristics. We provide search strategies catering to each environment. We also describe how to migrate from a currently running plan to a new plan at run-time. Third, we optimize the automaton computations using the schema knowledge. A set of criteria are established to decide what schema constraints are useful to a given query. Optimization rules utilizing different types of schema constraints are proposed based on the criteria. We design a rule application algorithm which ensures both completeness (i.e., no optimization is missed) and minimality (i.e., no redundant optimization is introduced). The experimentations on both real and synthetic data illustrate that these techniques bring significant performance improvement with little overhead.

While data mining techniques such as frequent itemset and sequence mining are well established as powerful pattern discovery tools in domains from science, medicine to business, a detriment is the lack of support for interactive exploration of high numbers of patterns generated with diverse parameter settings and the relationships among the mined patterns. To enhance the user experience, real-time query turnaround times and improved support for interactive mining are desired. There is also an increasing interest in applying data mining solutions for mobile data. Patterns mined over mobile data may enable context-aware applications ranging from automating frequently repeated tasks to providing personalized recommendations. Overall, this dissertation addresses three problems that limit the utility of data mining, namely, (a.) lack of interactive exploration tools for mined patterns, (b.) insufficient support for mining localized patterns, and (c.) high computational mining requirements prohibiting mining of patterns on smaller compute units such as a smartphone. This dissertation develops interactive frameworks for the guided exploration of mined patterns and their relationships. Contributions include the PARAS pre- processing and indexing framework; enabling analysts to gain key insights into rule relationships in a parameter space view due to the compact storage of rules that enables query-time reconstruction of complete rulesets. Contributions also include the visual rule exploration framework FIRE that presents an interactive dual view of the parameter space and the rule space, that together enable enhanced sense-making of rule relationships. This dissertation also supports the online mining of localized association rules computed on data subsets by selectively deploying alternative execution strategies that leverage multidimensional itemset-based data partitioning index. Finally, we designed OLAPH, an on-device context-aware service that learns phone usage patterns over mobile context data such as app usage, location, call and SMS logs to provide device intelligence. Concepts introduced for modeling mobile data as sequences include compressing context logs to intervaled context events, adding generalized time features, and identifying meaningful sequences via filter expressions.