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Estatística
Título: ONTOLOGY-BASED DATABASE TUNING: THE CASE OF MATERIALIZED VIEWS
Autor: RAFAEL PEREIRA DE OLIVEIRA
Colaborador(es): SERGIO LIFSCHITZ - Orientador
Catalogação: 04/NOV/2015 Língua(s): PORTUGUESE - BRAZIL
Tipo: TEXT Subtipo: THESIS
Notas: [pt] Todos os dados constantes dos documentos são de inteira responsabilidade de seus autores. Os dados utilizados nas descrições dos documentos estão em conformidade com os sistemas da administração da PUC-Rio.
[en] All data contained in the documents are the sole responsibility of the authors. The data used in the descriptions of the documents are in conformity with the systems of the administration of PUC-Rio.
Referência(s): [pt] https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/ETDs/consultas/conteudo.php?strSecao=resultado&nrSeq=25429&idi=1
[en] https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/ETDs/consultas/conteudo.php?strSecao=resultado&nrSeq=25429&idi=2
DOI: https://doi.org/10.17771/PUCRio.acad.25429
Resumo:
The Outer-tuning framework may be used to support automatic (or not) database tuning, particularity index. It is an approach that offers transparency about the available alternatives to feasible tuning scenarios, making it possible to combine either independent strategies or allow discussion of justifications for actions performed in order to obtain better performances. Using a specific ontology for fine tuning relational databases, we add semantics to the process with the understanding of the concepts involved and generate (semi)automatic new tuning actions, which can be inferred from existing practices or new rules and concepts that arise in the future. This research presents as an initial contribution the actual design and implementation of the Outer-tuning framework through the formalization of a software architecture that meets the specified functional requirements. This work also contributes with the extension of the domain ontology and the inclusion of new heuristics to a task ontology, in order to accomplish fine tuning solutions with the use of materialized views. Thus, it becomes possible to propose the use of tuning heuristics for indexes as well as for materialized views.
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