Outer-Tuning: an integration of rules, ontology and RDBMS

  • Rafael Pereira de Oliveira Pontifical Catholic University of Rio de Janeiro - PUC-Rio
  • Fernanda Baião Pontifical Catholic University of Rio de Janeiro - PUC-Rio
  • Ana Carolina Almeida State University of Rio de Janeiro - UERJ
  • Daniel Schwabe Pontifical Catholic University of Rio de Janeiro - PUC-Rio
  • Sérgio Lifschitz Pontifical Catholic University of Rio de Janeiro - PUC-Rio

Resumo


Database tuning is a crucial task to address the performance of information systems that deal with a considerable amount of information stored in databases. Current tuning tools are very platform-specific and do not provide adequate support for the database administrator to reason about performance improvement suggestions. In this paper, we discuss several architectural and implementation decisions of Outer-Tuning, our framework that supports database tuning. Outer-Tuning follows a model-driven development and a modular architecture design, which enabled several benefits. This paper contributes with: (i) the architectural design model adopted in Outer-Tuning, which combines imperative and declarative programming; (ii) the discussions and steps to integrate several software components; and (iii) the actual framework implementation. We assess our framework with an experiment using the TPC-H benchmark. The results evidence that Outer-Tuning infers useful tuning actions and supports the DBA by providing a more semantic environment to create and adapt tuning heuristics using concepts closer to his/her domain, and also relevant information on the rationale of the tuning actions through a friendly web interface.
Palavras-chave: components, frameworks, tuning, database system
Publicado
20/05/2019
DE OLIVEIRA, Rafael Pereira; BAIÃO, Fernanda; ALMEIDA, Ana Carolina; SCHWABE, Daniel; LIFSCHITZ, Sérgio. Outer-Tuning: an integration of rules, ontology and RDBMS. In: SIMPÓSIO BRASILEIRO DE SISTEMAS DE INFORMAÇÃO (SBSI), 15. , 2019, Aracajú. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2019 . p. 471-478.