@inproceedings{OliveiraLKVLS20,
  author    = {Rafael Pereira de Oliveira and Sergio Lifschitz and Marcos Kalinowski and Marx Viana and Carlos Lucena and Marcos Vaz Salles},
  title     = {Evaluating Database Self-Tuning Strategies in a Common Extensible Framework},
  abstract  = {Database automatic tuning tools are an essential class of database applications for database administrators (DBAs) and researchers. These self-management systems involve recurring and ubiquitous tasks, such as data extraction for workload acquisition and more specific features that depend on the tuning strategy, such as the specification of tuning action types and heuristics. Given the variety of approaches and implementations, it would be desirable to evaluate existing database self-tuning strategies, particularly recent and new heuristics, in a standard testbed. In this paper, we propose a reuse-oriented framework approach towards assessing and comparing automatic relational database tuning strategies. We employ our framework to instantiate three customized automated database tuning tools extended from our framework kernel, employing strategies using combinations of different tuning actions (indexes, partial indexes, and materialized views) for various RDBMS. Finally, we evaluate the effectiveness of these tools using a known database benchmark. Our results show that the framework enabled instantiating useful self-tuning tools with a low effort by just extending well-defined framework hot-spots. Hence, sharing it with the database research community may facilitate evolving state of the art on self-tunning strategies by enabling to evaluate different strategies on different RDBMS, serving as a common and extensible testbed.},
  booktitle = {Proceedings of the 34th Brazilian Symposium on Databases, {SBBD'20}, Brazil, September 28th - October 1st},
  pages     = {1-12},
  note      = {},          
  year      = {2020},
  urlAuthor_version       = {http://www.inf.puc-rio.br/~kalinowski/publications/OliveiraLKVLS20.pdf},
  doi       = {10.5753/sbbd.2020.13628},
}

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V."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["Rafael","Pereira"],"propositions":["de"],"lastnames":["Oliveira"],"suffixes":[]},{"firstnames":["Sergio"],"propositions":[],"lastnames":["Lifschitz"],"suffixes":[]},{"firstnames":["Marcos"],"propositions":[],"lastnames":["Kalinowski"],"suffixes":[]},{"firstnames":["Marx"],"propositions":[],"lastnames":["Viana"],"suffixes":[]},{"firstnames":["Carlos"],"propositions":[],"lastnames":["Lucena"],"suffixes":[]},{"firstnames":["Marcos","Vaz"],"propositions":[],"lastnames":["Salles"],"suffixes":[]}],"title":"Evaluating Database Self-Tuning Strategies in a Common Extensible Framework","abstract":"Database automatic tuning tools are an essential class of database applications for database administrators (DBAs) and researchers. These self-management systems involve recurring and ubiquitous tasks, such as data extraction for workload acquisition and more specific features that depend on the tuning strategy, such as the specification of tuning action types and heuristics. Given the variety of approaches and implementations, it would be desirable to evaluate existing database self-tuning strategies, particularly recent and new heuristics, in a standard testbed. In this paper, we propose a reuse-oriented framework approach towards assessing and comparing automatic relational database tuning strategies. We employ our framework to instantiate three customized automated database tuning tools extended from our framework kernel, employing strategies using combinations of different tuning actions (indexes, partial indexes, and materialized views) for various RDBMS. Finally, we evaluate the effectiveness of these tools using a known database benchmark. Our results show that the framework enabled instantiating useful self-tuning tools with a low effort by just extending well-defined framework hot-spots. Hence, sharing it with the database research community may facilitate evolving state of the art on self-tunning strategies by enabling to evaluate different strategies on different RDBMS, serving as a common and extensible testbed.","booktitle":"Proceedings of the 34th Brazilian Symposium on Databases, SBBD'20, Brazil, September 28th - October 1st","pages":"1-12","note":"","year":"2020","urlauthor_version":"http://www.inf.puc-rio.br/~kalinowski/publications/OliveiraLKVLS20.pdf","doi":"10.5753/sbbd.2020.13628","bibtex":"@inproceedings{OliveiraLKVLS20,\r\n author = {Rafael Pereira de Oliveira and Sergio Lifschitz and Marcos Kalinowski and Marx Viana and Carlos Lucena and Marcos Vaz Salles},\r\n title = {Evaluating Database Self-Tuning Strategies in a Common Extensible Framework},\r\n abstract = {Database automatic tuning tools are an essential class of database applications for database administrators (DBAs) and researchers. These self-management systems involve recurring and ubiquitous tasks, such as data extraction for workload acquisition and more specific features that depend on the tuning strategy, such as the specification of tuning action types and heuristics. Given the variety of approaches and implementations, it would be desirable to evaluate existing database self-tuning strategies, particularly recent and new heuristics, in a standard testbed. In this paper, we propose a reuse-oriented framework approach towards assessing and comparing automatic relational database tuning strategies. We employ our framework to instantiate three customized automated database tuning tools extended from our framework kernel, employing strategies using combinations of different tuning actions (indexes, partial indexes, and materialized views) for various RDBMS. Finally, we evaluate the effectiveness of these tools using a known database benchmark. Our results show that the framework enabled instantiating useful self-tuning tools with a low effort by just extending well-defined framework hot-spots. Hence, sharing it with the database research community may facilitate evolving state of the art on self-tunning strategies by enabling to evaluate different strategies on different RDBMS, serving as a common and extensible testbed.},\r\n booktitle = {Proceedings of the 34th Brazilian Symposium on Databases, {SBBD'20}, Brazil, September 28th - October 1st},\r\n pages = {1-12},\r\n note = {}, \r\n year = {2020},\r\n urlAuthor_version = {http://www.inf.puc-rio.br/~kalinowski/publications/OliveiraLKVLS20.pdf},\r\n doi = {10.5753/sbbd.2020.13628},\r\n}\r\n\r\n","author_short":["de Oliveira, R. P.","Lifschitz, S.","Kalinowski, M.","Viana, M.","Lucena, C.","Salles, M. 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