AI-Augmented Transaction Management in High-Throughput Databasesfor Conflict Resolution and Latency Reduction
Keywords:
High-throughput databases, transaction management, AI augmentation, conflict resolution, latency optimization, concurrency controlAbstract
High-throughput databases are increasingly central to real-time applications, ranging from financial systems to large-scale e-commerce platforms. However, as workloads intensify, managing transactional conflicts and minimizing latency becomes significantly more complex. This paper proposes a novel AI-augmented transaction management framework designed to dynamically predict and resolve transactional conflicts, while also optimizing system latency in high-volume database environments. By integrating machine learning models into transaction scheduling and conflict detection mechanisms, we demonstrate how predictive analytics can proactively minimize lock contention and improve throughput. Simulations and case studies show promising reductions in abort rates and response times, signaling the viability of AI-enhanced mechanisms in production-scale database systems.
References
Bernstein, Philip A., Vassos Hadzilacos, and Nathan Goodman. Concurrency Control and Recovery in Database Systems. Addison-Wesley, 1987.
Pulivarthy, P. (2024). Research on Oracle database performance optimization in IT-based university educational management system. FMDB Transactions on Sustainable Computing Systems, 2(2), 84-95.
Kraska, Tim, et al. “The Case for Learned Index Structures.” Proceedings of the 2018 International Conference on Management of Data (SIGMOD), ACM, 2018, pp. 489–504.
Pavlo, Andrew, et al. “Self-Driving Database Management Systems.” Proceedings of the Conference on Innovative Data Systems Research (CIDR), 2019.
Pulivarthy, P. (2024). Gen AI Impact on the Database Industry Innovations. International Journal of Advances in Engineering Research (IJAER), 28(III), 1–10.
Zhang, Hao, et al. “Predictive Scheduling in Cloud-Scale Transactional Databases.” Proceedings of the VLDB Endowment, vol. 13, no. 12, 2020, pp. 2953–2965.
Fekete, Alan, et al. “Making Snapshot Isolation Serializable.” ACM Transactions on Database Systems (TODS), vol. 30, no. 2, 2005, pp. 492–528.
Pulivarthy, P. (2024). Optimizing Large Scale Distributed Data Systems Using Intelligent Load Balancing Algorithms. AVE Trends in Intelligent Computing Systems, 1(4), 219–230.
Dean, Jeffrey, and Sanjay Ghemawat. “MapReduce: Simplified Data Processing on Large Clusters.” Communications of the ACM, vol. 51, no. 1, 2008, pp. 107–113.
Stonebraker, Michael, et al. “The End of an Architectural Era: (It’s Time for a Complete Rewrite).” Proceedings of the VLDB Endowment, vol. 1, no. 2, 2007, pp. 1150–1160.
Mao, Yifan, Eddie Kohler, and Robert Morris. “CacheCraft: A Performance-Oriented Data Store Based on Predictive Caching.” Proceedings of the 9th USENIX Conference on Operating Systems Design and Implementation (OSDI), 2010.
Chib, S., Devarajan, H. R., Chundru, S., Pulivarthy, P., Isaac, R. A., & Oku, K. (2025). Standardized post-quantum cryptography and recent developments in quantum computers. In Proceedings of the 2025 First International Conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT) (pp. 1018-1023). https://doi.org/10.1109/CE2CT64011.2025.10939125
Ding, Bolin, et al. “ALEX: An Updatable Adaptive Learned Index.” Proceedings of the ACM SIGMOD International Conference on Management of Data, 2020, pp. 969–984.
Lin, Jimmy, and Alek Kolcz. “Large-Scale Machine Learning at Twitter.” Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, 2012, pp. 793–804.
Menon, Ankur, et al. “Learning to Optimize Join Queries with Deep Reinforcement Learning.” arXiv preprint, arXiv:1906.00377, 2019.
Wang, Lidan, et al. “Auto-tuning Performance in Cloud Databases with Reinforcement Learning.” Proceedings of the ACM Symposium on Cloud Computing (SoCC), 2018, pp. 164–176.
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Copyright (c) 2025 G. Megan (Author)
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