Scalable Cloud Vector Database Architectures for Intelligent Enterprise AI Retrieval
DOI:
https://doi.org/10.15662/IJRAI.2024.0704001Keywords:
Vector Databases, Enterprise AI, Semantic Search, Retrieval-Augmented Generation (RAG), Scalability, Data Governance, Embedding Models, Approximate Nearest Neighbor Search (ANNS)Abstract
Scalable vector databases have emerged as a cornerstone for enterprise-scale AI retrieval, enabling efficient storage, management, and querying of high-dimensional vector embeddings derived from unstructured data. These databases facilitate semantic search, recommendation systems, and retrieval-augmented generation (RAG) by transforming complex data into numerical representations. This paper examines the evolution, architecture, and performance of scalable vector databases, highlighting their significance in enterprise AI applications. We explore the challenges associated with scalability, consistency, and data governance, and propose solutions to address these issues. Through comparative analysis of leading vector database systems, we provide insights into their capabilities and limitations. The findings underscore the critical role of scalable vector databases in unlocking the potential of AI-driven enterprise solutions.
References
1. Raja, G. V. (2023). Modernizing Enterprise Systems using AI with Machine Learning and Cloud Computing for Intelligent Systems. International Journal of Future Innovative Science and Technology (IJFIST), 6(6), 11713.
2. Guo, Y., et al. (2020). Milvus: A cloud-native vector database. Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data.
3. Johnson, J., Douze, M., & Jégou, H. (2019). Billion-scale similarity search with GPUs. IEEE Transactions on Big Data, 7(3), 535-547.
4. Malkov, Y. A., & Yashunin, D. A. (2018). Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(4), 824-836.
5. Li, X., et al. (2020). Vector search engines: A tutorial and survey. ACM Computing Surveys.
6. Zhou, Y., et al. (2021). Neural index for billion-scale vector search. Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining.
7. Chen, J., et al. (2018). Learning to hash for scalable vector search. ACM Computing Surveys, 50(3), 1-36.
8. Charikar, M. (2002). Similarity estimation techniques from rounding algorithms. Proceedings of the 34th Annual ACM Symposium on Theory of Computing, 380-388.
9. Gionis, A., Indyk, P., & Motwani, R. (1999). Similarity search in high dimensions via hashing. Proceedings of the 25th International Conference on Very Large Data Bases (VLDB), 518-529.
10. Tschannen, M., Bachem, O., & Lucic, M. (2019). Recent advances in autoencoder-based representation learning. arXiv preprint arXiv:1812.05069.
11. Gurram, S. (2023). Why Data Engineering, Not Model Scale, Became the True Bottleneck in Generative AI. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(4), 9028-9036.
12. Soundappan, S. J. (2021). DataOps: Orchestrating Reliable ML Data Pipelines. International Journal of Research and Applied Innovations, 4(4), 5533-5537.
13. Yamsani, N. (2022). Applying Machine Learning for Automated Data Quality and Anomaly Detection in Enterprise Data Pipelines. International Journal of Research and Applied Innovations, 5(1), 9457-9466.
14. Gopinathan, V. R. (2023). Cloud-first AI security architecture for protecting enterprise digital ecosystems and financial networks. International Journal of Research and Applied Innovations, 6(6), 10031-10039.
15. Adepu, R. (2022). Building secure multi-cloud infrastructure for mission-critical enterprise workloads. The International Journal of Research Publications in Engineering, Technology and Management, 5(5), 14–32.
16. Narayanan, S. (2023). Operationalizing AI risk frameworks in financial services: A second line of defense perspective. World Journal of Advanced Research and Reviews, 20(1), 1436–1446.
17. Parupalli, A., & Pandya, S. (2022). Compliance-Driven Data Governance: A Survey on GDPR and HIPAA in Cloud Databases, 12, 828-836.
18. Bellundagi, M. (2023). Integrating Machine Learning with Business Rule Management Systems for Adaptive Enterprise. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(1), 8023-8039.
19. Lanka, S. (2023). Blurring boundaries where artificial intelligence ends and human potential begins. International Journal of Computer Technology and Electronics Communication, 6(4), 7331–7341.
20. Rao, G. R. (2023). Hidden Trade-Offs in Modern Frontend Architecture. International Journal of Computer Technology and Electronics Communication, 6(5), 7615-7625.
21. Vankayala, S. C. (2020). Reinventing test automation reliability: Adaptive locator intelligence and self-healing execution pipelines for enterprise QA. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 6(1), 226–242.
22. Hema Latha Boddupally. (2019). Designing End-to-End Observability Architectures For High-Reliability .NET Cloud Applications In Production Environments. International Journal of Scientific Research & Engineering Trends, 5(6).
23. Mallireddy, S. (2023). How Servicenow Impacted Accelerating Clinical Trials. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(6), 1-7.
24. Mathew, A., & Alex, H. (2023). From Code to Cure: The Role of AI in Accelerating Drug Discovery. Advances and Challenges in Science and Technology, 2, 94-102.
25. Sugumar, R. (2024). Quantum-Resilient Cryptographic Protocols for the Next-Generation Financial Cybersecurity Landscape. International Journal of Humanities and Information Technology, 6(02), 89-105.
26. Niture, N. (2023). Machine Learning and Cryptographic Algorithms--Analysis and Design in Ransomware and Vulnerabilities Detection. Authorea Preprints.
27. Murugeshwari, B., Selvaraj, D., Sudharson, K., & Radhika, S. (2023). Data Mining with Privacy Protection Using Precise Elliptical Curve Cryptography. Intelligent Automation & Soft Computing, 35(1).
28. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273-287.
29. Sabin Begum, R., & Sugumar, R. (2019). Novel entropy-based approach for cost-effective privacy preservation of intermediate datasets in cloud. Cluster Computing, 22(Suppl 4), 9581-9588.
30. Mathew, A., & Mai, C. (2018). Study of Various Data Recovery and Data Back Up Techniques in Cloud Computing & Their Comparison. In 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) (pp. 2021-2024).
31. Adepu, G. (2022). Machine learning-driven environmental monitoring systems for real-time regulatory compliance and risk detection. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(2), 22–37.
32. Macha, Y., & Pulichikkunnu, S. K. (2023). An Explainable AI System for Fraud Identification in Insurance Claims via Machine-Learning Methods. International Journal of Advanced Research in Science Communication and Technology, 3(3), 1391-1400.
33. Kiela, D., et al. (2021). Supervised multimodal bitransformers for classifying images and text. Proceedings of the 37th International Conference on Machine Learning.
34. Deivendran, P., Babu, P. S., Malathi, G., Anbazhagan, K., & Kumar, R. S. (2023). Emotion Recognition for Challenged People Facial Appearance in Social using Neural Network. arXiv preprint arXiv:2305.06842.
35. Vinurajkumar, S., Bobby, J. S., Thiyam, D. B., & Rajasekar, M. (2023). Optimized Feature Selection for Brain Cancer Detection. In 2023 International Conference on Energy, Materials and Communication Engineering (ICEMCE) (pp. 1-6). IEEE.
36. Revathi, K. G., Ananth, B. J., Saravanan, M. L., & Kumar, A. R. (2021). GPS enabled vehicle location identification using GSM and fare collection using smart card. Turkish Journal of Computer and Mathematics Education, 12(10), 2657-2668.
37. Mannanuddin, K., Vimal, V. R., Srinivas, A., Uma Mageswari, S. D., Mahendran, G., Ramya, J., et al. (2023). RETRACTED: Enhancing medical image analysis: A fusion of fully connected neural network classifier with CNN-VIT for improved retinal disease detection. Journal of Intelligent & Fuzzy Systems, 45(6), 12313-12328.
