Outer-Tuning: an Ontology-based Extensible Framework for Supporting Database Automatic Tuning
Keywords:database tuning, docker, ontology, semantic framework
This paper presents the Outer-Tuning framework, which aims to support the (semi) automatic tuning of relational database systems through a domain-specific ontology. Ontologies have shown themselves to be increasingly promising, adding semantics and standardizing the different terms used in a domain. Thereby, our framework seeks to explain and make explicit the tuning heuristics reasoning while enabling the evaluation of new ontology-inferred methods. In this paper we focus on the main aspects of the Outer-Tuning component-based architecture. We also give an overview of our tool in practice. Finally, we show two useful extensions, concerning new DBMSs and a way of dockerizing into a container.
Alhadi, N. and Ahmad, K. Query tuning in oracle database. Journal of Computer Science 8 (11): 1889, 2012.
Almeida, A. C.,Baião, F.,Lifschitz, S.,Schwabe, D.,and Campos, M. L. M. Tun-ocm: A model-driven approach to support database tuning decision making. Decision Support Systems vol. 145, pp. 113538, 2021.
Almeida, A. C.,Campos, M. L. M.,Baião, F.,Lifschitz, S.,de Oliveira, R. P.,and Schwabe, D. An ontological perspective for database tuning heuristics. In International Conference on Conceptual Modeling. Springer,Springer, pp. 240–254, 2019.
Almeida, A. C.,Haeusler, E. H.,Lifschitz, S.,de Oliveira, R. P.,and Schwabe, D. Outer-tuning: Auto-matic self-tuning based on ontology (in portuguese). In Demos Session, Proceedings of the Brazilian Symposium on Databases (SBBD). SBC, pp. 29–34, 2018.
Bruno, N. Automated Physical Database Design and Tuning. CRC Press, 2011.
Carvalho, A. W.Automatic Creation of Materialized Views in Relational DBMSs (In Portuguese). M.S. thesis,PUC-Rio, Brazil, 2011.
de Oliveira, J.,Loja, L. F.,da Costa, S. L.,and Neto, V. G. An information systems component for business process management (in portuguese). In Proceedings of the VII Brazilian Symposium on Information Systems (SBSI). SBC, Porto Alegre, RS, Brasil, pp. 250–261, 2011.
de Oliveira, R. P. Ontology-Based Tuning: The Case of Materialized Views (In Portuguese). M.S. thesis, PUC-Rio,Brazil, 2015.
de Oliveira, R. P.,Baião, F.,Almeida, A. C.,Schwabe, D.,and Lifschitz, S. Outer-tuning: an integration of rules, ontology and rdbms. In Proceedings of the XV Brazilian Symposium on Information Systems. ACM, pp. 1–8,2019.
Dias, K.,Ramacher, M.,Shaft, U.,Venkataramani, V.,and Wood, G. Automatic performance diagnosis and tuning in oracle. In CIDR. www.cidrdb.org, pp. 84–94, 2005.
Fedosseeva, A.Speeding Up Mysql Using Materialized Views. https://email@example.com/speeding-up-mysql-by-using-materialized-views-282ecbd3a53f, 2017.
Gamma, E.,Johnson, R.,Helm, R.,Johnson, R. E.,Vlissides, J.,et al. Design patterns: elements of reusable object-oriented software. Pearson Deutschland GmbH, 1995.
Goasdoué, F.,Karanasos, K.,Leblay, J.,and Manolescu, I. View selection in semantic web databases. Proc. VLDB Endow.5 (2): 97–108, Oct., 2012.
Horrocks, I.,Patel-Schneider, P. F.,Boley, H.,Tabet, S.,Grosof, B.,Dean, M.,et al. Swrl: A semantic web rule language combining owl and ruleml. W3C Member submission21 (79): 1–31, 2004.
Khosla, R. and Ichalkaranje, N. Design of intelligent multi-agent systems: human-centredness, architectures, learning and adaptation. Springer-Verlag Berlin Heidelberg, 2005.
Morelli, E.,Almeida, A.,Lifschitz, S.,Monteiro, J. M.,and Machado, J. Autonomous re-indexing. In Proceedings of the 27th Annual ACM Symposium on Applied Computing. ACM, pp. 893–897, 2012.
Shasha, D. and Bonnet, P.Database tuning: principles, experiments, and troubleshooting techniques. Elsevier, 2002.
Sonatype.Maven: The Definitive Guide. " O’Reilly Media, Inc.", 2008.
Zhang, B.,Van Aken, D.,Wang, J.,Dai, T.,Jiang, S.,Lao, J.,Sheng, S.,Pavlo, A.,and Gordon, G. J.A demonstration of the ottertune automatic database management system tuning service. Proceedings of the VLDB Endowment 11 (12): 1910–1913, 2018.
Zhang, J.,Liu, Y.,Zhou, K.,Li, G.,Xiao, Z.,Cheng, B.,Xing, J.,Wang, Y.,Cheng, T.,Liu, L.,et al.Anend-to-end automatic cloud database tuning system using deep reinforcement learning. InProceedings of the 2019 International Conference on Management of Data. ACM, pp. 415–432, 2019.