Made available in DSpace on 2014-06-11T19:29:40Z (GMT). No. of bitstreams: 0 Previous issue date: 2010-02-22Bitstream added on 2014-06-13T20:39:07Z : No. of bitstreams: 1 oyama_ft_me_sjrp.pdf: 1107324 bytes, checksum: 0977db2af1589dece4aa46b5882d84d6 (MD5) / O crescente avanço e a disponibilidade de recursos computacionais viabilizam o armazenamento e a manipulação de grandes bases de dados. As técnicas típicas de mineração de dados possibilitam a extração de padrões desde que os dados estejam armazenados em uma única tabela. A mineração de dados multirrelacional, por sua vez, apresenta-se como uma abordagem mais recente que permite buscar padrões provenientes de múltiplas tabelas, sendo indicada para a aplicação em bases de dados relacionais. No entanto, os algoritmos multirrelacionais de mineração de regras de associação existentes tornam-se impossibilitados de efetuar a tarefa de mineração em grandes volumes de dados, uma vez que a quantia de memória exigida para a conclusão do processamento ultrapassa a quantidade disponível. O objetivo do presente trabalho consiste em apresentar um algoritmo multirrelacional de extração de regras de associação com o foco na aplicação em grandes bases de dados relacionais. Para isso, o algoritmo proposto, MR-RADIX, apresenta uma estrutura denominada Radix-tree que representa comprimidamente a base de dados em memória. Além disso, o algoritmo utiliza-se do conceito de particionamento para subdividir a base de dados, de modo que cada partição possa ser processada integralmente em memória. Os testes realizados demonstram que o algoritmo MR-RADIX proporciona um desempenho superior a outros algoritmos correlatos e, ainda, efetua com êxito, diferentemente dos demais, a mineração de regras de associação em grandes bases de dados. / The increasing spread and availability of computing resources make feasible storage and handling of large databases. Traditional techniques of data mining allows the extraction of patterns provided that data is stored in a single table. The multi- relational data mining presents itself as a more recent approach that allows search patterns from multiple tables, indicated for use in relational databases. However, the existing multi-relational association rules mining algorithms become unable to make mining task in large data, since the amount of memory required for the completion of processing exceed the amount available. The goal of this work is to present a multi- relational algorithm for extracting association rules with focus application in large relational databases. For this the proposed algorithm MR-RADIX presents a structure called Radix-tree that represents compressly the database in memory. Moreover, the algorithm uses the concept of partitioning to subdivide the database, so that each partition can be processed entirely in memory. The tests show that the MR-RADIX algorithm provides better performance than other related algorithms, and also performs successfully, unlike others, the association rules mining in large databases.

Orientador: Carlos Roberto Valêncio / Banca: Cristina Dutra de Aguiar Ciferri / Banca: Rogéria Cristiane Gratão de Souza / Resumo: O crescente avanço e a disponibilidade de recursos computacionais viabilizam o armazenamento e a manipulação de grandes bases de dados. As técnicas típicas de mineração de dados possibilitam a extração de padrões desde que os dados estejam armazenados em uma única tabela. A mineração de dados multirrelacional, por sua vez, apresenta-se como uma abordagem mais recente que permite buscar padrões provenientes de múltiplas tabelas, sendo indicada para a aplicação em bases de dados relacionais. No entanto, os algoritmos multirrelacionais de mineração de regras de associação existentes tornam-se impossibilitados de efetuar a tarefa de mineração em grandes volumes de dados, uma vez que a quantia de memória exigida para a conclusão do processamento ultrapassa a quantidade disponível. O objetivo do presente trabalho consiste em apresentar um algoritmo multirrelacional de extração de regras de associação com o foco na aplicação em grandes bases de dados relacionais. Para isso, o algoritmo proposto, MR-RADIX, apresenta uma estrutura denominada Radix-tree que representa comprimidamente a base de dados em memória. Além disso, o algoritmo utiliza-se do conceito de particionamento para subdividir a base de dados, de modo que cada partição possa ser processada integralmente em memória. Os testes realizados demonstram que o algoritmo MR-RADIX proporciona um desempenho superior a outros algoritmos correlatos e, ainda, efetua com êxito, diferentemente dos demais, a mineração de regras de associação em grandes bases de dados. / Abstract: The increasing spread and availability of computing resources make feasible storage and handling of large databases. Traditional techniques of data mining allows the extraction of patterns provided that data is stored in a single table. The multi- relational data mining presents itself as a more recent approach that allows search patterns from multiple tables, indicated for use in relational databases. However, the existing multi-relational association rules mining algorithms become unable to make mining task in large data, since the amount of memory required for the completion of processing exceed the amount available. The goal of this work is to present a multi- relational algorithm for extracting association rules with focus application in large relational databases. For this the proposed algorithm MR-RADIX presents a structure called Radix-tree that represents compressly the database in memory. Moreover, the algorithm uses the concept of partitioning to subdivide the database, so that each partition can be processed entirely in memory. The tests show that the MR-RADIX algorithm provides better performance than other related algorithms, and also performs successfully, unlike others, the association rules mining in large databases. / Mestre

Ce travail introduit un ensemble de solutions pour extraire des graphes à partir des données de l'entreprise et pour aussi faciliter le processus de recherche d'information dans ces graphes. Premièrement, nous avons défini un nouveau modèle de données appelé SPIDER-Graph permettant de modéliser des objets complexes et de définir des graphes hétérogènes. Puis, nous avons développé un ensemble d'algorithmes pour extraire le contenu des bases de données de l'entreprise et les transformer suivant ce nouveau modèle de graphe. Cette représentation permet de mettre à jour des relations non explicites entre objets, relations existantes mais non visibles dans le modèle relationnel. Par ailleurs, pour unifier la représentation de toutes les données dans l'entreprise, nous avons développé, dans une deuxième approche, une méthode de constitution d’une ontologie d'entreprise contenant les concepts et les relations les plus importantes d'une entreprise, et ceci, à partir de l’extraction des données non structurés de cette même entreprise. Ensuite, après le processus d'extraction des différents graphes de données l'entreprise, nous avons proposé une approche qui permettent d'extraire des graphes d'interactions entre des objets hétérogènes modélisant l'entreprise. Cette approche permet d'extraire des graphes de réseaux sociaux ou des graphes d'interactions. Ensuite, nous avons proposé un nouveau langage d'interrogation visuel appelé GraphVQL ( Graph Visual Query Langauge) qui permet aux utilisateurs non experts de poser leurs requêtes visuellement sous forme de patron de graphe. Ce langage propose plusieurs types de requêtes de la simple sélection et agrégation jusqu'à l'analyse des réseaux sociaux. Il permet aussi d'interroger différent type de graphes SPIDER-Graph, RDF ou GraphML en se basant sur des algorithmes de pattern matching ou de translation des requêtes sous forme de SPARQL. / The present work introduces a set of solutions to extract graphs from enterprise data and facilitate the process of information search on these graphs. First of all we have defined a new graph model called the SPIDER-Graph, which models complex objects and permits to define heterogeneous graphs. Furthermore, we have developed a set of algorithms to extract the content of a database from an enterprise and to represent it in this new model. This latter representation allows us to discover relations that exist in the data but are hidden due to their poor compatibility with the classical relational model. Moreover, in order to unify the representation of all the data of the enterprise, we have developed a second approach which extracts from unstructured data an enterprise's ontology containing the most important concepts and relations that can be found in a given enterprise. Having extracted the graphs from the relational databases and documents using the enterprise ontology, we propose an approach which allows the users to extract an interaction graph between a set of chosen enterprise objects. This approach is based on a set of relations patterns extracted from the graph and the enterprise ontology concepts and relations. Finally, information retrieval is facilitated using a new visual graph query language called GraphVQL, which allows users to query graphs by drawing a pattern visually for the query. This language covers different query types from the simple selection and aggregation queries to social network analysis queries.

As informações imprecisas e vagas, comumente encontradas na modelagem de problemas do mundo real, muitas vezes não são manipuladas de forma adequada por meio das consultas convencionais aos bancos de dados. Alternativamente, a teoria de conjuntos fuzzy tem sido considerada uma ferramenta bem promissora para tratamento destas informações consideradas imprecisas e, em determinados casos, até mesmo ambíguas. Esse trabalho utiliza a linguagem SQL padrão para apresentar uma abordagem fuzzy de consultas a bancos de dados relacionais. Estudos de casos referentes à aplicabilidade do método desenvolvido são apresentados a fim de mostrar as suas potencialidades em relação aos métodos tradicionais de consultas. / Often, the imprecise and vague information, commonly found in the modeling of real world problems, are not dealt in an appropriate way through conventional queries used in databases. Alternatively, the fuzzy set theory has been considered a very promising tool to treat imprecise and ambiguous information. This work uses the standard SQL language and fuzzy set theory to develop a fuzzy query method for relational databases. Simulation examples are presented to illustrate its potentialities in relation to the traditional query methods.

Submitted by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2017-08-22T19:40:10Z No. of bitstreams: 2 Tese - Péricles Silva de Oliveira.pdf: 1875380 bytes, checksum: 014ba89b7fe1929a1461c9d8d3959416 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2017-08-22T19:40:26Z (GMT) No. of bitstreams: 2 Tese - Péricles Silva de Oliveira.pdf: 1875380 bytes, checksum: 014ba89b7fe1929a1461c9d8d3959416 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2017-08-22T19:40:44Z (GMT) No. of bitstreams: 2 Tese - Péricles Silva de Oliveira.pdf: 1875380 bytes, checksum: 014ba89b7fe1929a1461c9d8d3959416 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2017-08-22T19:40:44Z (GMT). No. of bitstreams: 2 Tese - Péricles Silva de Oliveira.pdf: 1875380 bytes, checksum: 014ba89b7fe1929a1461c9d8d3959416 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2017-04-28 / Several systems proposed for processing keyword queries over relational databases rely on the generation and evaluation of Candidate Networks (CNs), i.e., networks of joined database relations that, when processed as SQL queries, provide a relevant answer to the input keyword query. Although the evaluation of CNs has been extensively addressed in the literature, problems related to efficiently generating meaningful CNs have received much less attention. To generate useful CNs is necessary to automatically locating, given a handful of keywords, relations in the database that may contain relevant pieces of information, and determining suitable ways of joining these relations to satisfy the implicit information need expressed by a user when formulating her query. In this thesis, we present two main contributions related to the processing of Candidate Networks. As our first contribution, we present a novel approach for generating CNs, in which possible matchings of the query in database are efficiently enumerated at first. These query matches are then used to guide the CN generation process, avoiding the exhaustive search procedure used by current state-of-art approaches. We show that our approach allows the generation of a compact set of CNs that leads to superior quality answers, and that demands less resources in terms of processing time and memory. As our second contribution, we initially argue that the number of possible Candidate Networks that can be generated by any algorithm is usually very high, but that, in fact, only very few of them produce answers relevant to the user and are indeed worth processing. Thus, there is no point in wasting resources processing useless CNs. Then, based on such an argument, we present an algorithm for ranking CNs, based on their probability of producing relevant answers to the user. This relevance is estimated based on the current state of the underlying database using a probabilistic Bayesian model we have developed. By doing so we are able do discard a large number of CNs, ultimately leading to better results in terms of quality and performance. Our claims and proposals are supported by a comprehensive set of experiments we carried out using several query sets and datasets used in previous related work and whose results we report and analyse here. / Sem resumo.

Context. PHP framework Laravel offers three techniques to interact with databases, Eloquent ORM, Query builder and Raw SQL. It is important to select the right database technique when developing a web application because there are pros and cons with each approach.  Objectives. In this thesis we will measure the performance of Raw SQL and Eloquent ORM, there is little research on which technique is faster. Intuitively, Raw SQL should be faster than Eloquent ORM, but exactly how much faster needs to be researched.   Methods. To measure the performance of both techniques, we developed a blog application and we ran database operations select, insert and update in both techniques.   Conclusions. Results indicated that overall Raw SQL performed better than Eloquent ORM in our database operations. There was a noticeable difference of average response time between Raw SQL and Eloquent ORM in all database operations. We can conclude that Eloquent ORM is good for building small to medium sized applications, where simple CRUD operations are used for the small amount of data. Typically for tasks like inserting a single row into a database or retrieving few rows from the database. Raw SQL is preferable for the applications that are dealing with huge amount of data, bulk data loads and complex queries.

In the planning stage of web and software development, it is important to select the right tool for the job. When selecting the database to use, relational databases like MySQL is a popular choice. However, relational databases suffer by object-relational impedance mismatch. In this work we will explore the response time of relational and object-oriented databases and the overhead of ORM frameworks. This will be done by creating a web application that receives data about flights and airports from a client, which measures the response time of the databases and the entire request. It was found that MySQL has the lowest response time, while the ORM framework Hibernate adds an overhead on some of the tests while performing similar to MySQL. Db4o had the highest response time in a majority of the tests. In future works, this study could be extended by other tests or by other type of databases.

Magister Scientiae - MSc / The completion of the Human Genome Project, a remarkable feat by any measure, has provided over three billion bases of reference nucleotides for comparative studies. The next, and perhaps more challenging step is to analyse sequence variation and relate this information to important phenotypes. Most human sequence variations are characterized by structural complexity and, are hence, associated with abnormal functional dynamics. This thesis covers the assembly of a computational platform for verifying these variations, based on accurate, published, experimental data. / South Africa

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The purpose of this project is to create a design and subsequently the web application for a database management which uses the system catalogue of the Microsoft SQL database system. The introduction deals with the principle of the relational databases and includes the list of the most widespread database systems. The next chapter of the theoretical part describes the syntax of the SQL query language, and the next chapter deals with the properties of the system catalogue which were utilized. Finally, the last of the theoretical chapters is centred on the .NET platform and its technologies – particularly the ASP.NET and the ADO.NET technologies. The former is used for creating dynamical web pages, and the latter is an object library used for the data access. The practical part of this work comprises the web application scheme on which is the final web application based. The most emphasized issues in this part are the security and the supposed program design of the web application. The final part of the work contains a description of the finished web application, especially the configuration and the functions which the application provides to a user. And finally, the whole project is briefly evaluated in the conclusion.