Artificial Intelligence for Autonomous Database Management in Saudi Arabia: Predictive Performance Monitoring and Intelligent Incident Prevention for Vision 2030

References

[1] S. Ahmed, M. Singh, B. Doherty, E. Ramlan, K. Harkin, and D. Coyle, “AI for Information Technology Operation (AIOps): A Review of IT Incident Risk Prediction,” in Proc. 2022 9th Int. Conf. Soft Computing & Machine Intelligence (ISCMI), pp. 253–257, 2023, doi: 10.1109/ISCMI56532.2022.10068482. Crossref

[2] J. Audibert, P. Michiardi, F. Guyard, S. Marti, and M. A. Zuluaga, “USAD: UnSupervised Anomaly Detection on Multivariate Time Series,” in Proc. 26th ACM SIGKDD Conf. Knowledge Discovery and Data Mining, pp. 3395–3404, 2020, doi: 10.1145/3394486.3403392. ACM

[3] J. Bogatinovski, S. Nedelkoski, A. Acker, F. Schmidt, T. Wittkopp, S. Becker, J. Cardoso, and O. Kao, “Artificial Intelligence for IT Operations (AIOPS) Workshop White Paper,” arXiv preprint arXiv:2101.06054, 2021, doi: 10.48550/arXiv.2101.06054. arXiv

[4] Y. Cao, Y. Ma, Y. Zhu, and K. M. Ting, “Revisiting streaming anomaly detection: benchmark and evaluation,” Artificial Intelligence Review, vol. 58, Art. no. 8, 2025, doi: 10.1007/s10462-024-10995-w. Springer

[5] J. Diaz-De-Arcaya, A. I. Torre-Bastida, G. Zárate, R. Miñón, and A. Almeida, “A Joint Study of the Challenges, Opportunities, and Roadmap of MLOps and AIOps: A Systematic Survey,” ACM Computing Surveys, vol. 56, no. 4, Art. no. 84, pp. 1–30, 2024, doi: 10.1145/3625289. ACM

[6] Digital Government Authority, Effective Integration of Cloud Computing in Digital Transformation of the Education Sector, Version 1.0. Riyadh, Saudi Arabia: Digital Government Authority, 2025. Digital Government Authority Accessed: Sep. 3, 2026.

[7] E. Dritsas and M. Trigka, “Machine learning in modern database systems: Techniques, architectures, and deployment challenges,” Engineering Applications of Artificial Intelligence, vol. 170, Art. no. 114225, 2026, doi: 10.1016/j.engappai.2026.114225. ScienceDirect

[8] Gao, P. Yuan, S. Han, Y. Liu, X. Xia, H. Zhang, J. Cui, H. Li, and K. Zhao, “Insights into KPI-based performance anomaly detection in database systems: A comprehensive study,” Expert Systems with Applications, vol. 285, Art. no. 127959, 2025, doi: 10.1016/j.eswa.2025.127959. ScienceDirect

[9] H. Guo, S. Yuan, and X. Wu, “LogBERT: Log Anomaly Detection via BERT,” in Proc. 2021 Int. Joint Conf. Neural Networks (IJCNN), pp. 1–8, 2021, doi: 10.1109/IJCNN52387.2021.9534113. Crossref

[10] A. Jamarani, S. Haddadi, R. Sarvizadeh, M. Haghi Kashani, M. Akbari, and S. Moradi, “Big data and predictive analytics: A systematic review of applications,” Artificial Intelligence Review, vol. 57, Art. no. 176, 2024, doi: 10.1007/s10462-024-10811-5. Springer

[11] Z. Li, Y. Zhu, and M. van Leeuwen, “A Survey on Explainable Anomaly Detection,” ACM Transactions on Knowledge Discovery from Data, vol. 18, no. 1, Art. no. 23, pp. 1–54, 2024, doi: 10.1145/3609333. ACM

[12] Liu, Z. Yin, C. Zhao, C. Ge, L. Chen, Y. Gao, D. Li, Z. Wang, G. Liang, J. Tan, and F. Li, “PinSQL: Pinpoint Root Cause SQLs to Resolve Performance Issues in Cloud Databases,” in Proc. 2022 IEEE 38th Int. Conf. Data Engineering (ICDE), pp. 2549–2561, 2022, doi: 10.1109/ICDE53745.2022.00236. IEEE

[13] B. Milicevic and Z. Babovic, “A systematic review of deep learning applications in database query execution,” Journal of Big Data, vol. 11, Art. no. 173, 2024, doi: 10.1186/s40537-024-01025-1. Springer

[14] National Transformation Program, Annual Report 2025. Riyadh, Saudi Arabia: Saudi Vision 2030, 2026. Saudi Vision 2030 Accessed: Sep. 3, 2026.

[15] P. Notaro, J. Cardoso, and M. Gerndt, “A Survey of AIOps Methods for Failure Management,” ACM Transactions on Intelligent Systems and Technology, vol. 12, no. 6, Art. no. 81, 2021, doi: 10.1145/3483424. ACM

[16] Oracle, Autonomous AI Database Documentation. Oracle Cloud Infrastructure, 2026. Oracle Accessed: Sep. 3, 2026.

[17] Oracle, Oracle Autonomous Health Framework User’s Guide, Release 26.3. Redwood Shores, CA, USA: Oracle, 2026. Oracle Accessed: Sep. 3, 2026.

[18] A. Ouared, M. Amrani, and P.-Y. Schobbens, “Explainable AI for DBA: Bridging the DBA’s experience and machine learning in tuning database systems,” Concurrency and Computation: Practice and Experience, vol. 35, no. 21, Art. no. e7698, 2023, doi: 10.1002/cpe.7698. Wiley

[19] G. Pang, C. Shen, L. Cao, and A. van den Hengel, “Deep Learning for Anomaly Detection: A Review,” ACM Computing Surveys, vol. 54, no. 2, Art. no. 38, pp. 1–38, 2021, doi: 10.1145/3439950. ACM

[20] V. G. S. Ruffo, D. M. B. Lent, M. Komarchesqui, V. F. Schiavon, M. V. O. de Assis, L. F. Carvalho, and M. L. Proença Jr., “Anomaly and intrusion detection using deep learning for software-defined networks: A survey,” Expert Systems with Applications, vol. 256, Art. no. 124982, 2024, doi: 10.1016/j.eswa.2024.124982. ScienceDirect

[21] Saudi Data & AI Authority (SDAIA), AI Ethics Principles, Version 1.0. Riyadh, Saudi Arabia: SDAIA, 2023. SDAIA Accessed: Sep. 3, 2026.

[22] Saudi Data & AI Authority (SDAIA), AI Adoption Framework. Riyadh, Saudi Arabia: SDAIA, Sep. 2024. SDAIA Accessed: Sep. 3, 2026.

[23] Saudi Data & AI Authority (SDAIA), National AI Risk Management Framework. Riyadh, Saudi Arabia: SDAIA, Apr. 1, 2026. SDAIA Accessed: Sep. 3, 2026.

[24] Saudi Data & AI Authority (SDAIA), National Strategy for Data & AI. SDAIA Accessed: Sep. 3, 2026.

[25] Saudi Data & AI Authority (SDAIA), “Strategic Objectives.” SDAIA Accessed: Sep. 3, 2026.

[26] Saudi Vision 2030, Vision 2030 Annual Report 2025. Riyadh, Saudi Arabia: Kingdom of Saudi Arabia, 2026. Saudi Vision 2030 Accessed: Sep. 3, 2026.

[27] S. Schmidl, P. Wenig, and T. Papenbrock, “Anomaly Detection in Time Series: A Comprehensive Evaluation,” Proceedings of the VLDB Endowment, vol. 15, no. 9, pp. 1779–1797, 2022, doi: 10.14778/3538598.3538602. VLDB

[28] P. Wenig, S. Schmidl, and T. Papenbrock, “TimeEval: A Benchmarking Toolkit for Time Series Anomaly Detection Algorithms,” Proceedings of the VLDB Endowment, vol. 15, no. 12, pp. 3678–3681, 2022, doi: 10.14778/3554821.3554873. VLDB

[29] X. Zhou, C. Chai, G. Li, and J. Sun, “Database Meets Artificial Intelligence: A Survey,” IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 3, pp. 1096–1116, 2022, doi: 10.1109/TKDE.2020.2994641. Crossref

[30] B. Zou, J. You, Q. Wang, X. Wen, and L. Jia, “Survey on Learnable Databases: A Machine Learning Perspective,” Big Data Research, vol. 27, Art. no. 100304, 2022, doi: 10.1016/j.bdr.2021.100304. ScienceDirect

@article{1723189,
      author = {Mohammed Afzal Shareef},
      title = {Artificial Intelligence for Autonomous Database Management in Saudi Arabia: Predictive Performance Monitoring and Intelligent Incident Prevention for Vision 2030},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {3},
      pages = {2531-2544},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1723189.pdf},
      abstract = {Saudi Arabia’s Vision 2030 is accelerating data-intensive digital government, cloud adoption, artificial intelligence (AI), cybersecurity, and highly available national platforms. As mission-critical database estates grow, conventional administration based on static thresholds, manual diagnosis, and reactive incident handling becomes difficult to scale. This paper examines how AI can support autonomous database management in Saudi Arabia through predictive performance monitoring, anomaly detection, root-cause analysis, and guardrailed incident prevention. A structured narrative review of 30 core sources published mainly between 2020 and 2026 synthesizes peer-reviewed database and AIOps research, selected industry documentation, and official Saudi digital and AI policy. The review shows that effective autonomy requires more than anomaly detection: heterogeneous telemetry must be transformed into contextual features, models must adapt to workload drift, anomalies must be linked to probable causes, and remediation must be constrained by explainability, policy, rollback, security, and human oversight. Building on these findings, the paper proposes a six-layer Saudi Autonomous Database Operations Framework spanning observability, operational data engineering, AI analytics, contextual diagnosis, decision orchestration, and governance. A five-stage maturity model guides progression from observable operations to policy-governed closed-loop automation. The framework aligns with Saudi priorities for data and AI adoption, advanced digital infrastructure, reliability, cybersecurity, responsible AI, and efficient digital services. The paper concludes that AI can strengthen database resilience when autonomy is introduced incrementally and governed by trustworthy telemetry, validated models, architecture-aware diagnosis, and controlled automation.},
      keywords = {autonomous database management; artificial intelligence; AIOps; anomaly detection; predictive monitoring; root-cause analysis; database reliability; Saudi Vision 2030; digital transformation; incident prevention},
      month = {September},
  }