Environmental data hold important information regarding humanity’s past, present, and future, and are managed in various ways. The database structure most commonly used in contemporary applications is the relational database. Its usage in the scientific world for managing environmental data is not as popular as in businesses enterprises. Attention is caught by the diverse nature and rapidly growing volume of environmental data that has been increasing substantially in recent. Environmental data for the Chukchi Sea, with its embedded potential oil resources, have become important for characterizing the physical, chemical, and biological environment. Substantive data have been collected recently by researchers from the Chukchi Sea Offshore Monitoring in the Drilling Area: Chemical and Benthos (COMIDA CAB) project. A modified Observations Data Model was employed for storing, retrieving, visualizing and sharing data. Throughout the project-based study, the processes of environmental data heterogeneity reconciliation and relational database model modification and implementation were carried out. Data were transformed into shareable information, which improves data interoperability between different software applications (e.g. ArcGIS and SQL server). The results confirm the feasibility and extendibility of employing relational databases for environmental data management. / text

Per pastaruosius metus ontologijų kūrimas- tikslių formalių specifikacijos terminų ir specifikacijų sąryšių dalykinėje srityje, pradėjo plėstis nuo dirbtinio intelekto srities laboratorijų iki dalykinių sričių ekspertų darbalaukių. Ontologijos tapo dažnos pasauliniame žinių tinkle. Ontologijos naudojamos internete nuo didelių sistematikos klasifikavimo puslapių iki tokių kaip „Yahoo“ ,iki internetinių prekių klasifikavimo ir jų savybių klasifikavimo pardavimui tinklapių, tokių kaip „Amazon.com“. Jeigu informacija yra vienodos struktūros, t.y. visi terminai naudojami tie patys, automatinės paieškos sistemos gali sujungti informacija iš skirtingų šaltinių ir pateikti vartotojui kaip visumą. Ontologija apibrėžia dažnai naudojamą žodyną tyrinėtojams, kuriems reikia dalintis informacija dalykinėje srityje. Į tai įeina dalykinėje srityje kompiuterių interpretuojami apibrėžimai apie pagrindines sąvokas, sąryšius tarp jų. Informacija, žodynai tyrinėtojams, kompiuterių interpretuojami apibrėžimai ir kitą yra saugomi reliacinėse duomenų bazėse. Darbo tikslas: padidinti ontologijų išgavimo iš reliacinių duomenų bazių galimybes sukuriant ir realizuojant tam skirtą algoritmą, leidžiantį atstatyti ontologiją iš duomenų bazės be informacinių nuostolių. / In recent years the development of ontologines- formal specifications of the terms in the domain and relations among them has been expanding from the Artificial-Intelligence laboratories to the desktops of domain experts. Ontologies have become common on the World-Wide Web. The ontologies on the Web range from large taxonomies categorizing Web sites such as on „Yahoo“ to categorizations of products for sale and their features such as on „Amazon.com“. It is a language for encoding knowledge on Web pages to make it understandable to electronic agents searching for information. An ontology defines a common vocabulary for researchers who need to share information in a domain. It includes machine interpretable definitions of basic concepts in the domain and relations among them. Encoded information, vocabulary for researchers, formal specifications of the terms and other are saved in relational databases. The aim of this research is to improve possibilities of querying ontologies when these are kept in relational databases by creating and realizating the algorithm, which allows to transform ontology from relational databases. Experiments have shown that the method works for relation databases which were created by OWL2toRDB algorithm.

<p>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.</p>

The problems associated with the effective design and uses of databases are increasing. The information contained in a database is becoming more complex and the size of the data is causing space problems. Technology must continually develop to accommodate this growing need. An inquiry was conducted in order to find effective guidelines that could support queries in general in terms of performance and productivity. Two database management systems were researched to compare die theoretical aspects with the techniques implemented in practice. Microsoft SQL Sewer and MySQL were chosen as the candidates and both were put under close scrutiny. The systems were researched to uncover the methods employed by each to manage queries. The query optimizer forms the basis for each of these systems and manages the parsing and execution of any query. The methods employed by each system for storing data were researched. The way that each system manages table joins, uses indices and chooses optimal execution plans were researched. Adjusted algorithms were introduced for various index processes like B+ trees and hash indexes. Guidelines were compiled that are independent of the database management systems and help to optimize relational databases. Practical implementations of queries were used to acquire and analyse the execution plan for both MySQL and SQL Sewer. This plan along with a few other variables such as execution time is discussed for each system. A model is used for both database management systems in this experiment. / Thesis (M.Sc. (Computer Science))--North-West University, Potchefstroom Campus, 2007.

Ontology, an explicit specification of a shared conceptualisation, captures knowledge about a specific domain of interest. The realisation of ontologies, revolutionised the way data stored in relational databases is accessed and manipulated through ontology and database integration. When integrating ontologies with relational databases, several choices exist regarding aspects such as database implementation, ontology language features, and mappings. However, it is unclear which aspects are relevant and when they affect specific choices. This imposes difficulties in deciding which choices to make and their implications on ontology and database integration solutions. Within this study, a decision-making tool that guides users when selecting a technology and developing a solution that integrates ontologies with relational databases is developed. A theory analysis is conducted to determine current status of technologies that integrate ontologies with databases. Furthermore, a theoretical study is conducted to determine important features affecting ontology and database integration, ontology language features, and choices that one needs to make given each technology. Based on the building blocks stated above, an artifact-building approach is used to develop the decision-making tool, and this tool is verified through a proof-of-concept to prove the usefulness thereof. Key terms: Ontology, semantics, relational database, ontology and database integration, mapping, Web information portal. / Information Science / M. Sc. (Information Systems)

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.