OnDBTuning is a relational database (automatic) tuning ontology. Ontologies are software artifacts that represent specific domain knowledge and can infer new knowledge. However, most cases involve only a formal and static description of concepts. Moreover, as database tuning involves many rules-ofthumb and black-box algorithms, it becomes challenging to describe these inference procedures. This research work first presents the OnDBTuning ontology solution focusing on the inference of tuning actions. Next, we provide an actual implementation using SPARQL Inferencing Notation (SPIN). Finally, we discuss a practical evaluation for index recommendation.
In most scenarios, different information sources coexist and their content overlap, thus requiring domain knowledge to discover, understand and integrate information. In general, information sources are not designed for integration and their descriptive metadata do not suffice to enable IIS to consistently and unambiguously discover which information sources contain the required data to be integrated. This paper proposes an architecture to discover information sources through the use of semantic search techniques on top of corporative metadata repositories. Our experiments using a prototype of the proposed architecture obtained positive results with regard to precision and recall.
Quem@PUC is an Information Retrieval System available on the Web that allows searching for researchers and professors based on a keyword list of research related terms. It publicizes research and teaching activities from the PUC-Rio community to society in general. The idea is to integrate information from professors from administrative systems, courses offered, and researchers’ Lattes CVs. Data sources are converted to RDF format using domain ontologies, then stored in a NoSQL database that supports native free-text indexing on triple objects. Search results include names, academic papers, teaching activities, and contact links.
Este trabalho apresenta o projeto e a construção de Sistemas de Recuperação de Informações que permitem a identificação de projetos de pesquisa e/ou desenvolvimento, e as competências existentes em laboratórios e departamentos, coordenados por integrantes do quadro de professores-pesquisadores da PUC-Rio, a partir da busca por uma ou uma lista de palavras-chave. As fontes de informação que compõem o banco de dados do projeto são convertidas para o formato RDF usando ontologias de domínio, e são armazenadas em uma base NoSQL que suporta indexação de texto livre nativamente. Os resultados da busca incluem nomes, produções científicas diversas, atividades de ensino e links para contato. Ilustramos nossa solução com dois sistemas em desenvolvimento: Busc@NIMA e Quem@PUC.
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