AI-Driven Query Optimization Techniques as a Predictor of Database Systems Performance among Database Administrators in Federal Universities in South-South, Nigeria

The purpose of this study was to determine the influence of influence of AI-driven query optimization techniques as predictors of database systems performance among Database Administrators in Federal Universities in South-South Nigeria. Two specific objectives were identified, two research questions were raised and two null hypotheses were formulated to guide this study. The study adopted a descriptive survey design and was conducted in the South-South geopolitical zone of Nigeria. The population of the study was 58 Database Administrators. The total population of 58 Database Administrators was used as a sample size because the population was in a manageable size and purposive sampling technique was used. The researcher developed thirty (30) items research instrument titled “AI-Driven Query Optimization Techniques Questionnaires” (AIDQOTQ) for data collection. The instrument was subjected to face validation. The researcher with the aid of one research assistants who were briefed on the purpose of this study administered the 58 copies of questionnaire to the respondents at various institutions. Out of 58 copies of questionnaire distributed, 54 copies were returned with valid data. This gave a response rate of 93.10 percent and the attrition rate of 6.9 percent. Linear regression analysis was used to answer the research questions and also test null hypotheses at 0.5 level of significance. The findings of the study revealed that AI-Driven query optimization techniques had a strong positive influence on database systems performance among Database Administrators in Federal Universities in South-South Nigeria. It was further revealed that the ANOVA results indicated that predictive query optimization, automated index recommendation and adaptive query processing techniques had a statistically significant influence on database systems performance among Database Administrators in Federal Universities in South-South Nigeria. Based on the finding of this study, it was concluded that AI-driven query optimization techniques play a significant role in enhancing database systems performance among Database Administrators in Federal Universities in South-South Nigeria. Specifically, predictive query optimization, automated index recommendation and adaptive query processing techniques were found to have strong positive and statistically significant influences on database systems performance. Based on the conclusion, it was recommended that Database Administrators in Federal Universities in South-South Nigeria should adopt and effectively utilize AI-driven query optimization techniques to improve database performance.

Keywords: AI-Driven Query Optimization, Predictive Query Optimization, Automated Index Recommendation, Adaptive Query Processing, Database Systems Performance and Database Administrators.