CHALLENGES AND SOLUTIONS IN TROUBLESHOOTING DATABASE SYSTEMS FOR MODERN ENTERPRISES

Authors

  • Sagar Vishnubhai Sheta Software Developer, Lathia Investment LLC, USA. Author

Keywords:

Database Troubleshooting, Performance Bottlenecks, Security Protocols, Diagnostic Tools, Automated Solutions

Abstract

Database systems are fundamental to the operational efficiency of modern enterprises, yet they present numerous challenges in performance, data integrity, and security. This paper explores common issues in database troubleshooting, including performance bottlenecks, data consistency, and access control vulnerabilities. It examines effective solutions, such as diagnostic and monitoring tools, automated troubleshooting, robust backup and recovery mechanisms, and advanced security protocols. Through detailed case studies, the paper illustrates practical applications of these solutions, demonstrating how tailored approaches can significantly improve database resilience and efficiency. As database demands continue to rise, the adoption of innovative technologies, including artificial intelligence, offers promising advancements in predictive and automated troubleshooting, setting a pathway for future improvements in database management.

References

Qu, L., et al. (2022). Application-oriented workload generation for transactional database performance evaluation. In 2022 IEEE 38th International Conference on Data Engineering (ICDE), Kuala Lumpur, Malaysia, pp. 420–432. https://doi.org/10.1109/ICDE53745.2022.00036

Chen, J., Ding, Y., Liu, Y., Li, F., Zhang, L., et al. (2022). ByteHTAP: bytedance's HTAP system with high data freshness and strong data consistency. The VLDB Journal, 15(12). https://doi.org/10.14778/3554821.3554832

V. Sokolov, F. Kipchuk, P. Skladannyi, O. Zhyltsov and D. Ageyev, "Method for Increasing the Various Sources Data Consistency for IoT Sensors," 2022 IEEE 9th International Conference on Problems of Infocommunications, Science and Technology (PIC S&T), Kharkiv, Ukraine, 2022, pp. 522-526, doi: 10.1109/PICST57299.2022.10238518.

Dorsey, L.C., Wang, B., Grabowski, M., Merrick, J., Harrald, J.R., et al. (2020). Self-healing databases for predictive risk analytics in safety-critical systems. Journal of Loss Prevention in the Process Industries, 63, 104014. https://doi.org/10.1016/j.jlp.2019.104014

Hidayat, K., Arifudin, R., & Alamsyah, A. (2018). Genetic Algorithm for Relational Database Optimization in Reducing Query Execution Time. Scientific Journal of Informatics, 5(1), 27. doi:https://doi.org/10.15294/sji.v5i1.12720

Wang, J., Trummer, I., Basu, D., et al. (2021). UDO: universal database optimization using reinforcement learning. Proceedings of the VLDB Endowment, 14(13), 3402–3414. https://doi.org/10.14778/3484224.3484236

Darabant, A.S., Varga, V., Tambulea, L., et al. (2017). A linear approach to distributed database optimization using data reallocation. In 2017 25th International Conference on Software, Telecommunications and Computer Networks (SoftCOM), Split, Croatia, pp. 1–6. https://doi.org/10.23919/SOFTCOM.2017.8115503

Villani, V., Sabattini, L., Battilani, N., Fantuzzi, C., et al. (2016). Smartwatch-enhanced interaction with an advanced troubleshooting system for industrial machines. IFAC-PapersOnLine, 49(19), 277–282. https://doi.org/10.1016/j.ifacol.2016.10.547

Niu, Z., Martin, R.R., Langbein, F.C., Sabin, M.A., et al. (2015). Rapidly finding CAD features using database optimization. Computer-Aided Design, 69, 35–50. https://doi.org/10.1016/j.cad.2015.08.001

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Published

2024-02-14

How to Cite

Sagar Vishnubhai Sheta. (2024). CHALLENGES AND SOLUTIONS IN TROUBLESHOOTING DATABASE SYSTEMS FOR MODERN ENTERPRISES. INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ENGINEERING AND TECHNOLOGY (IJARET), 15(1), 53-66. https://lib-index.com/index.php/IJARET/article/view/IJARET_15_01_003