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

Abstract

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.

Data Analytics and Techniques

“…The results demonstrate the approach's superiority over conventional methods, with a reduction in RMSE of up to 99.20% (Kumar, Singh and Buyya, 2020). Whereas in 2021 a new framework based on features modeling and ensemble learning to predict query performance was proposed by Zaghloul, Salem and Ali-Eldin (2021) using Machine learning algorithm attempting to predict a performance metric based on the amount of time elapsed and ensemble learning (Zaghloul, Salem and Ali-Eldin, 2021).…”

Assemble the shallow or integrate a deep? Toward a lightweight solution for glyph-aware Chinese text classification

“…XGBoost uses a decision tree to fit the last prediction residual and improves the model's performance through iteration. Using XGBoost to integrate network-based base learners has been proven to be an efficient and effective metalearner in many studies [22,44]. The process of using XGBoost with a stacking ensemble strategy is illustrated in Fig 3 . It results in a final prediction by integrating character features and radical features produced by several parallel shallow neural networks.…”