A new framework based on features modeling and ensemble learning to predict query performance.

A query optimizer attempts to predict a performance metric based on the amount of time elapsed. Theoretically, this would necessitate the creation of a significant overhead on the core engine to provide the necessary query optimizing statistics. Machine learning is increasingly being used to improve...

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Autores principales:	Mohamed Zaghloul, Mofreh Salem, Amr Ali-Eldin
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/93a82078903a4abc81004fe9f2cc23c4
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id	oai:doaj.org-article:93a82078903a4abc81004fe9f2cc23c4
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spelling	oai:doaj.org-article:93a82078903a4abc81004fe9f2cc23c42021-12-02T20:16:50ZA new framework based on features modeling and ensemble learning to predict query performance.1932-620310.1371/journal.pone.0258439https://doaj.org/article/93a82078903a4abc81004fe9f2cc23c42021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0258439https://doaj.org/toc/1932-6203A query optimizer attempts to predict a performance metric based on the amount of time elapsed. Theoretically, this would necessitate the creation of a significant overhead on the core engine to provide the necessary query optimizing statistics. Machine learning is increasingly being used to improve query performance by incorporating regression models. To predict the response time for a query, most query performance approaches rely on DBMS optimizing statistics and the cost estimation of each operator in the query execution plan, which also focuses on resource utilization (CPU, I/O). Modeling query features is thus a critical step in developing a robust query performance prediction model. In this paper, we propose a new framework based on query feature modeling and ensemble learning to predict query performance and use this framework as a query performance predictor simulator to optimize the query features that influence query performance. In query feature modeling, we propose five dimensions used to model query features. The query features dimensions are syntax, hardware, software, data architecture, and historical performance logs. These features will be based on developing training datasets for the performance prediction model that employs the ensemble learning model. As a result, ensemble learning leverages the query performance prediction problem to deal with missing values. Handling overfitting via regularization. The section on experimental work will go over how to use the proposed framework in experimental work. The training dataset in this paper is made up of performance data logs from various real-world environments. The outcomes were compared to show the difference between the actual and expected performance of the proposed prediction model. Empirical work shows the effectiveness of the proposed approach compared to related work.Mohamed ZaghloulMofreh SalemAmr Ali-EldinPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10, p e0258439 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mohamed Zaghloul Mofreh Salem Amr Ali-Eldin A new framework based on features modeling and ensemble learning to predict query performance.
description	A query optimizer attempts to predict a performance metric based on the amount of time elapsed. Theoretically, this would necessitate the creation of a significant overhead on the core engine to provide the necessary query optimizing statistics. Machine learning is increasingly being used to improve query performance by incorporating regression models. To predict the response time for a query, most query performance approaches rely on DBMS optimizing statistics and the cost estimation of each operator in the query execution plan, which also focuses on resource utilization (CPU, I/O). Modeling query features is thus a critical step in developing a robust query performance prediction model. In this paper, we propose a new framework based on query feature modeling and ensemble learning to predict query performance and use this framework as a query performance predictor simulator to optimize the query features that influence query performance. In query feature modeling, we propose five dimensions used to model query features. The query features dimensions are syntax, hardware, software, data architecture, and historical performance logs. These features will be based on developing training datasets for the performance prediction model that employs the ensemble learning model. As a result, ensemble learning leverages the query performance prediction problem to deal with missing values. Handling overfitting via regularization. The section on experimental work will go over how to use the proposed framework in experimental work. The training dataset in this paper is made up of performance data logs from various real-world environments. The outcomes were compared to show the difference between the actual and expected performance of the proposed prediction model. Empirical work shows the effectiveness of the proposed approach compared to related work.
format	article
author	Mohamed Zaghloul Mofreh Salem Amr Ali-Eldin
author_facet	Mohamed Zaghloul Mofreh Salem Amr Ali-Eldin
author_sort	Mohamed Zaghloul
title	A new framework based on features modeling and ensemble learning to predict query performance.
title_short	A new framework based on features modeling and ensemble learning to predict query performance.
title_full	A new framework based on features modeling and ensemble learning to predict query performance.
title_fullStr	A new framework based on features modeling and ensemble learning to predict query performance.
title_full_unstemmed	A new framework based on features modeling and ensemble learning to predict query performance.
title_sort	new framework based on features modeling and ensemble learning to predict query performance.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/93a82078903a4abc81004fe9f2cc23c4
work_keys_str_mv	AT mohamedzaghloul anewframeworkbasedonfeaturesmodelingandensemblelearningtopredictqueryperformance AT mofrehsalem anewframeworkbasedonfeaturesmodelingandensemblelearningtopredictqueryperformance AT amralieldin anewframeworkbasedonfeaturesmodelingandensemblelearningtopredictqueryperformance AT mohamedzaghloul newframeworkbasedonfeaturesmodelingandensemblelearningtopredictqueryperformance AT mofrehsalem newframeworkbasedonfeaturesmodelingandensemblelearningtopredictqueryperformance AT amralieldin newframeworkbasedonfeaturesmodelingandensemblelearningtopredictqueryperformance
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A new framework based on features modeling and ensemble learning to predict query performance.

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