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Graph data is becoming more and more pervasive. Indeed, services such as Social Networks or the Semantic Web can no longer rely on the traditional relational model, as its structure is somewhat too rigid for the applications they have in mind. For this reason we have seen a continuous shift towards more non-standard models. First it was the semi-structured data in the 1990s and XML in 2000s, but even such models seem to be too restrictive for new applications that require navigational properties naturally modelled by graphs. Social networks fit into the graph model by their very design: users are nodes and their connections are specified by graph edges. The W3C committee, on the other hand, describes RDF, the model underlying the Semantic Web, by using graphs. The situation is quite similar with crime detection networks and tracking workflow provenance, namely they all have graphs inbuilt into their definition. With pervasiveness of graph data the important question of querying and maintaining it has emerged as one of the main priorities, both in theoretical and applied sense. Currently there seem to be two approaches to handling such data. On the one hand, to extract the actual data, practitioners use traditional relational languages that completely disregard various navigational patterns connecting the data. What makes this data interesting in modern applications, however, is precisely its ability to compactly represent intricate topological properties that envelop the data. To overcome this issue several languages that allow querying graph topology have been proposed and extensively studied. The problem with these languages is that they concentrate on navigation only, thus disregarding the data that is actually stored in the database. What we propose in this thesis is the ability to do both. Namely, we will study how query languages can be designed to allow specifying not only how the data is connected, but also how data changes along paths and patterns connecting it. To this end we will develop several query languages and show how adding different data manipulation capabilities and different navigational features affects the complexity of main reasoning tasks. The story here is somewhat similar to the early success of the relational data model, where theoretical considerations led to a better understanding of what makes certain tasks more challenging than others. Here we aim for languages that are both efficient and capable of expressing a wide variety of queries of interest to several groups of practitioners. To do so we will analyse how different requirements affect the language at hand and at the end provide a good base of primitives whose inclusion into a language should be considered, based on the applications one has in mind. Namely, we consider how adding a specific operation, mechanism, or capability to the language affects practical tasks that such an addition plans to tackle. In the end we arrive at several languages, all of them with their pros and cons, giving us a good overview of how specific capabilities of the language affect the design goals, thus providing a sound basis for practitioners to choose from, based on their requirements.

Although approximate query processing is a prominent way to cope with the requirements of data analysis applications, current database systems do not provide integrated and comprehensive support for these techniques. To improve this situation, we propose an SQL extension---called SQL/S---for approximate query answering using random samples, and present a prototypical implementation within the engine of the open-source database system Derby---called Derby/S. Our approach significantly reduces the required expert knowledge by enabling the definition of samples in a declarative way; the choice of the specific sampling scheme and its parametrization is left to the system. SQL/S introduces new DDL commands to easily define and administrate random samples subject to a given set of optimization criteria. Derby/S automatically takes care of sample maintenance if the underlying dataset changes. Finally, samples are transparently used during query processing, and error bounds are provided. Our extensions do not affect traditional queries and provide the means to integrate sampling as a first-class citizen into a DBMS.

Query equivalence is a fundamental problem within database theory. The correctness of all forms of logical query rewriting—join minimization, view flattening, rewriting over materialized views, various semantic optimizations that exploit schema dependencies, federated query processing and other forms of data integration—requires proving that the final executed query is equivalent to the original user query. Hence, advances in the theory of query equivalence enable advances in query processing and optimization. In this thesis we address the problem of deciding query equivalence between conjunctive SQL queries containing aggregation operators that may be nested. Our focus is on understanding the interaction between nested aggregation operators and the other parts of the query body, and so we model aggregation functions simply as abstract collection constructors. Hence, the precise language that we study is a conjunctive algebraic language that constructs complex objects from databases of flat relations. Using an encoding of complex objects as flat relations, we reduce the query equivalence problem for this algebraic language to deciding equivalence between relational encodings output by traditional conjunctive queries (not containing aggregation). This encoding-equivalence cleanly unifies and generalizes previous results for deciding equivalence of conjunctive queries evaluated under various processing semantics. As part of our study of aggregation operators that can construct empty sub-collections—so-called “scalar” aggregation—we consider query equivalence for conjunctive queries extended with a left outer join operator, a very practical class of queries for which the general equivalence problem has never before been analyzed. Although we do not completely solve the equivalence problem for queries with outer joins or with scalar aggregation, we do propose useful sufficient conditions that generalize previously known results for restricted classes of queries. Overall, this thesis offers new insight into the fundamental principles governing the behaviour of nested aggregation.

Query equivalence is a fundamental problem within database theory. The correctness of all forms of logical query rewriting—join minimization, view flattening, rewriting over materialized views, various semantic optimizations that exploit schema dependencies, federated query processing and other forms of data integration—requires proving that the final executed query is equivalent to the original user query. Hence, advances in the theory of query equivalence enable advances in query processing and optimization. In this thesis we address the problem of deciding query equivalence between conjunctive SQL queries containing aggregation operators that may be nested. Our focus is on understanding the interaction between nested aggregation operators and the other parts of the query body, and so we model aggregation functions simply as abstract collection constructors. Hence, the precise language that we study is a conjunctive algebraic language that constructs complex objects from databases of flat relations. Using an encoding of complex objects as flat relations, we reduce the query equivalence problem for this algebraic language to deciding equivalence between relational encodings output by traditional conjunctive queries (not containing aggregation). This encoding-equivalence cleanly unifies and generalizes previous results for deciding equivalence of conjunctive queries evaluated under various processing semantics. As part of our study of aggregation operators that can construct empty sub-collections—so-called “scalar” aggregation—we consider query equivalence for conjunctive queries extended with a left outer join operator, a very practical class of queries for which the general equivalence problem has never before been analyzed. Although we do not completely solve the equivalence problem for queries with outer joins or with scalar aggregation, we do propose useful sufficient conditions that generalize previously known results for restricted classes of queries. Overall, this thesis offers new insight into the fundamental principles governing the behaviour of nested aggregation.

The problem motivating this study is that database query facilities are not effectively meeting the needs of casual users. A solution to this problem is especially important due to the increasing number of potential casual users. There is considerable controversy revolving around the question of which elements and/or which combination of elements within the casual users' environment are necessary to provide an effective man/machine interface. The purpose of the study is to extend the basic knowledge relating to the effect of using different data models, the effect of cognitive style differences, and the interaction effects of these two independent variables on casual users' performance and confidence in writing database queries. The data models being investigated are the relational, hierarchical, and network models. The cognitive style classifications used are two of the Jungian dimensions, Sensing/Intuition and Thinking/Feeling.

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.

High performance computer clusters have become popular as they are more scalable, affordable and reliable than their centralized counterparts. Database management systems are particularly suitable for distributed architectures / however distributed DBMS are still not used widely because of the design difficulties. In this study, we aim to help overcome these difficulties by implementing a simulation testbed for a distributed query plan processor. This testbed works on our departmental HPC cluster machine and is able to perform select, project and join operations. A data generation module has also been implemented which preserves the foreign key and primary key constraints in the database schema. The testbed has capability to measure, simulate and estimate the response time of a given query execution plan using specified communication network parameters. Extensive experimental work is performed to show the correctness of the produced results. The estimated execution time costs are also compared with the actual run-times obtained from the testbed to verify the proposed estimation functions. Thus, we make sure that these estimation iv functions can be used in distributed database query optimization and distributed database design tools.

Submitted by Franciele Moreira (francielemoreyra@gmail.com) on 2018-01-26T12:51:05Z No. of bitstreams: 2 Dissertação - Walisson Pereira de Sousa - 2017.pdf: 2525793 bytes, checksum: 0717fb8c52cc2e89d38f1e7c4a763ec1 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2018-01-29T10:41:52Z (GMT) No. of bitstreams: 2 Dissertação - Walisson Pereira de Sousa - 2017.pdf: 2525793 bytes, checksum: 0717fb8c52cc2e89d38f1e7c4a763ec1 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2018-01-29T10:41:52Z (GMT). No. of bitstreams: 2 Dissertação - Walisson Pereira de Sousa - 2017.pdf: 2525793 bytes, checksum: 0717fb8c52cc2e89d38f1e7c4a763ec1 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2017-12-11 / Retrieving information using words from a natural language is a simple and already consolidated way to access data on the Web. It would be highly desirable that a similar method could be used to submit queries on databases, thereby freeing the user from learning a query language and knowing the searched database structure. In this sense, a great research effort has been dedicated by the database community in order to develop more efficient query keywords techniques for database access. However, a keyword query can result in a large number of SQL interpretations, most of them irrelevant for the initial query. This work carry out a study of different query interpretations ranking techniques and, finally, proposes a ranking methodology that maximizes the amount of relevant results for keyword queries submitted to relational databases. / Recuperar informações utilizando palavras de uma linguagem natural é uma maneira simples e já consolidada para acessar dados na Web. Seria altamente desejável que um método semelhante fosse utilizado para executar consultas em bancos de dados, liberando assim o usuário do aprendizado de uma linguagem de consulta e o conhecimento da estrutura do banco de dados a ser consultado. Nesse sentido, um grande esforço de pesquisa vem sendo dedicado pela comunidade de Banco de dados, a fim de desenvolver técnicas de consultas com palavras-chave mais eficientes para acesso a bancos de dados. No entanto, uma consulta com palavras-chave pode originar uma grande quantidade de interpretações SQL, boa parte delas resultando em dados irrelevantes para a consulta inicial. Este trabalho realiza um estudo de diferentes técnicas para ranqueamento de interpretações de consultas e, ao final, propõe uma metodologia de ranqueamento que maximiza a quantidade de resultados relevantes para consultas com palavras-chave submetidas a bancos de dados relacionais.