Enhancing SQL Query Performance via XAI: A Dual SHAP–LIME Framework for Accurate Response Time Prediction and Optimization in RDBMS (Journal of the College of Basic Education, 2026)
Optimization of queries in RDBMSs have been one of the most important roadblocks as the rule-based optimizer s fail to scale to the non-linear nature of the new analytical workloads. Although Machine Learning (ML) models have demonstrated good latency predictability, their poor interpret-ability creates a Trust Gap that restricts their use. This paper introduces XAIExplainer, a dual-explanation framework that combines a three-model ensemble predictor (Lasso Regression, Extremely Randomized Trees, and a Keras-based plan-structured residual network) with SHAP and LIME along with the Spearman rank-correlation protocol is used. When measured on three real-world Oracle SQL queries, with varying levels of complexity, XAIExplainer achieved a significant latency speedup of 35, 72, and 31 with high predictive accuracy (R 2 = 0.9712, MAE = 18.4 ms) and a 91.2% SHAPLIME agreement rate. XAIExplainer successfully addresses the interpretability gap by converting the quantitative attributions into the form of the DBA-understandable narratives, SQL rewrite strategies, and automatic index suggestions.