xDBTune: eXplainable Database Tuning Framework 


Vol. 14,  No. 9, pp. 704-712, Sep.  2025
https://doi.org/10.3745/TKIPS.2025.14.9.704


  Abstract

Database systems serve as critical infrastructure for efficiently processing large-scale data across various industries such as finance and manufacturing. However, as data complexity increases and query workloads become more diverse, database performance optimization has emerged as an essential task to ensure system stability and service quality. Recently, with the advancement of artificial intelligence, machine learning–based automated database tuning techniques have been proposed as alternatives. Nevertheless, most existing approaches rely on black-box models, leading to a fundamental limitation in the interpretability of tuning results. To address this issue, this study proposes xDBTune, a database tuning framework that integrates explainable artificial intelligence (XAI) techniques. xDBTune automatically recommends optimized tuning parameter configurations based on machine learning–based performance prediction, and employs the SHAP (Shapley Additive Explanations) algorithm to visually and quantitatively explain the impact of each parameter on performance. This enables both DBAs and general users to clearly understand and verify the tuning results with confidence. The proposed framework was evaluated using the TPC-H benchmark dataset in a MySQL environment. Experimental results show that the recommended tuning configuration reduced the average query execution time by approximately 22.2% compared to the default setting. Furthermore, SHAP-based visualizations effectively conveyed the contribution of each tuning parameter, demonstrating the framework’s ability to improve both interpretability and user trust in database tuning.

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  Cite this article

[IEEE Style]

O. Choi and W. Shin, "xDBTune: eXplainable Database Tuning Framework," The Transactions of the Korea Information Processing Society, vol. 14, no. 9, pp. 704-712, 2025. DOI: https://doi.org/10.3745/TKIPS.2025.14.9.704.

[ACM Style]

Okjoo Choi and Wonsun Shin. 2025. xDBTune: eXplainable Database Tuning Framework. The Transactions of the Korea Information Processing Society, 14, 9, (2025), 704-712. DOI: https://doi.org/10.3745/TKIPS.2025.14.9.704.

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