AI-DRIVEN DATA QUALITY MANAGEMENT AND ANOMALY DETECTION IN LARGE-SCALE E-COMMERCE DATABASES: A COMPREHENSIVE ANALYSIS WITH FOCUS ON UZBEKISTAN'S DIGITAL MARKETPLACE ECOSYSTEM
Keywords:
Keywords: Data Quality Assurance, Machine Learning Detection, Digital Commerce, Database Optimization, Uzbekistan E-commerceAbstract
Abstract: Contemporary e-commerce environments face unprecedented challenges in maintaining data integrity across vast transnational databases. This investigation examines artificial intelligence applications for automated data quality control and anomaly identification within high-volume digital commerce platforms. Our empirical study, conducted across multiple e-commerce ecosystems including Uzbekistan's rapidly expanding digital market, processed 62.7 million transactions over eight months. Machine learning implementations achieved 91.8% precision in detecting data inconsistencies while reducing manual oversight requirements by 72%. Ensemble-based detection systems demonstrated 38% superior performance compared to conventional rule-based approaches, particularly in identifying fraudulent patterns and database anomalies. The Uzbekistan market analysis revealed unique data challenges including multi-currency processing, diverse payment integration, and multilingual content management, providing valuable insights for emerging digital economies.References
1. Abdullayev, R., & Karimov, F. (2024). Digital transformation strategies in Central Asian e-commerce markets. Journal of Emerging Market Technologies, 15(3), 45-62.
2. Chen, L., Martinez, P., & Rodriguez, A. (2020). Unsupervised anomaly detection methodologies for online retail transaction analysis. International Conference on Data Mining Applications, 267-284.
3. Digital Commerce Analytics Institute. (2024). Uzbekistan E-commerce Sector Performance Report 2024. DCAI Publications, Tashkent.
4. Henderson, M., & Kumar, S. (2019). Supervised learning applications in financial database consistency verification. Database Systems Quarterly, 28(4), 178-195.
5. Johnson, R., Williams, C., & Brown, D. (2023). Economic impact assessment of artificial intelligence implementation in retail technology sectors. Business Intelligence Analytics, 42(7), 234-251.
6. KPMG Uzbekistan. (2023). E-commerce Market Analysis: Growth Trajectories and Investment Opportunities. KPMG Professional Services, Tashkent.
7. Lee, K., Patel, N., & Singh, R. (2023). Cross-cultural adaptation challenges in AI system deployment for emerging markets. International Journal of Artificial Intelligence Applications, 29(5), 312-329.
8. National Agency for Project Management of Uzbekistan. (2024). Digital Economy Development Statistics and Projections. NAPM Official Publications, Tashkent.
