Inferencing Relational Database Tuning Actions with OnDBTuning Ontology

• Luciana de Sá Silva Perciliano Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio)
• Veronica dos Santos Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio)
• Fernanda Baião Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio)
• Edward Hermann Haeusler Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio)
• Sérgio Lifschitz Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio)
• Ana Carolina Almeida Universidade do Estado do Rio de Janeiro (UERJ)

DOI: https://doi.org/10.5753/sbbd.2021.17874

Resumo

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.

Palavras-chave: Ontology, Tuning, SPIN, SPARQL

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