References
- Böhm, C. Similarity Search and Data Mining: Database Techniques Supporting Next Decade’s Applications. – Unit for Database Systems, University for Health Informatics and Technology.
- Foster, C., B. Sevilmis, B. Kimia. Generalized Relative Neighborhood Graph (GRNG) for Similarity Search. – In: Proc. of International Conference on Similarity Search and Applications, Cham: Springer International Publishing, September 2022, pp. 133-149.
- Gedik, B. Auto-Tuning Similarity Search Algorithms on Multi-Core Architectures. – International Journal of Parallel Programming, Vol. 41, 2013, No 5, pp. 595-620.
- Khorshidi, M. S., N. Yazdanjue, H. Gharoun, D. Yazdani, M. R. Nikoo, F. Chen, A. H. Gandomi. Semantic-Preserving Feature Partitioning for Multi-View Ensemble Learning. arXiv Preprint arXiv:2401.06251, 2024.
- Köppen, M. The Curse of Dimensionality. – In: Proc. of 5th Online World Conference on Soft Computing in Industrial Applications (WSC5’00), Vol. 1, September 2000, pp. 4-8.
- Jégou, H., M. Douze, C. Schmid. Product Quantization for Nearest Neighbor Search. – IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 33, 2011, No 1, pp. 117-128. https://doi.org/10.1109/TPAMI.2010.57
- Malkov, Y. A., D. A. Yashunin. Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs. – IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 42, 2020, No 4, pp. 824-836. https://doi.org/10.1109/TPAMI.2018.2889473
- Sturmfels, B. Voronoi Cells. University of California, Berkeley, 2023 (Last accessed: 26.07.2024). https://math.berkeley.edu/~bernd/wednesday.pdf
- Echihabi, K., K. Zoumpatianos, T. Palpanas. High-Dimensional Similarity Search for Scalable Data Science. – In: Proc. of 37th IEEE International Conference on Data Engineering (ICDE’21), April 2021, pp. 2369-2372.
- Baranchuk, D., A. Babenko, Y. Malkov. Revisiting the Inverted Indices for Billion-Scale Approximate Nearest Neighbors. – In: Proc. of European Conference on Computer Vision (ECCV’18), 2018, pp. 202-216.
- Johnson, J., M. Douze, H. Jégou. Billion-Scale Similarity Search with GPUs. – IEEE Transactions on Big Data, Vol. 7, 2019, No 3, pp. 535-547.
- Yang, K., H. Wang, M. Du, Z. Wang, Z. Tan, Y. Xiao. Hierarchical Link and Code: Efficient Similarity Search for Billion-Scale Image Sets. – In: PG (Short Papers, Posters, and Work-in-Progress Papers), 2021, pp. 81-86.
- Zhang, P., Z. Liu, S. Xiao, Z. Dou, J. Yao. Hybrid Inverted Index Is a Robust Accelerator for Dense Retrieval. – In: Proc. of 2023 Conference on Empirical Methods in Natural Language Processing, December 2023, pp. 1877-1888.
- Gollapudi, S., N. Karia, V. Sivashankar, R. Krishnaswamy, N. Begwani, S. Raz, Y. Lin, Y. Zhang, N. Mahapatro, P. Srinivasan, A. Singh. Filtered-DiskANN: Graph Algorithms for Approximate Nearest Neighbor Search with Filters. – In: Proc. of ACM Web Conference 2023, April 2023, pp. 3406-3416.
- Jayaram Subramanya, S., F. Devvrit, H. V. Simhadri, R. Krishnawamy, R. Kadekodi. DiskANN: Fast Accurate Billion-Point Nearest Neighbor Search on a Single Node. – Advances in Neural Information Processing Systems, 2019, 32.
- Al-Dujaili, M. J., H. Jabar Sabat Ahily. A New Hybrid Model to Predict Human Age Estimation from Face Images Based on Supervised Machine Learning Algorithms. – Cybernetics and Information Technologies, Vol. 23, 2023, No 2, pp. 20-33.
- Singh, A., S. J. Subramanya, R. Krishnaswamy, H. V. Simhadri. FreshDiskANN: A Fast and Accurate Graph-Based ANN Index for Streaming Similarity Search. arXiv Preprint arXiv:2105.09613, 2021.
- Sundaram, N., A. Turmukhametova, N. Satish, T. Mostak, P. Indyk, S. Madden, P. Dubey. Streaming Similarity Search over One Billion Tweets Using Parallel Locality-Sensitive Hashing. – In: Proc. of VLDB Endowment, Vol. 6, 2013, No 14, pp. 1930-1941.
- Pganalyze Blog. 5mins on Postgres: Vectors with PGvector. 2021 (Last accessed: 26.07.2024). https://pganalyze.com/blog/5mins-postgres-vectors-pgvector
- Alibaba Cloud. AnalyticDB: Real-time OLAP Database. 2021 (Last accessed: 26.07.2024). https://www.alibabacloud.com/product/analyticdb
- PostgreSQL Documentation: 16: 73.2. TOAST (Last accessed: 26.07.2024). https://www.postgresql.org/docs/current/storage-toast.html
- NVIDIA Developer Blog. Accelerating Vector Search Using GPU-Powered Indexes with RAPIDS Raft. 2021 (Last accessed: 26.07.2024). https://developer.nvidia.com/blog/accelerating-vector-search-using-gpu-powered-indexes-with-rapids-raft/
- NVIDIA Developer Blog. Accelerated Vector Search: Approximating with RAPIDS raft IVF-Flat. 2021 (Last accessed: 26.07.2024). https://developer.nvidia.com/blog/accelerated-vector-search-approximating-with-rapids-raft-ivf-flat/
- Doshi, I., D. Das, A. Bhutani, R. Kumar, R. Bhatt, N. Balasubramanian. LANNS: A Web-Scale Approximate Nearest Neighbor Lookup System. – Proc. of VLDB Endowment, Vol. 15, 2021, pp. 850-858.
- Schäfer, P., M. Högqvist. SFA: A Symbolic Fourier Approximation and Index for Similarity Search in High Dimensional Datasets. – In: Proc. of 15th International Conference on Extending Database Technology, March 2012, pp. 516-527.
- Douze, M., A. Guzhva, C. Deng, J. Johnson, G. Szilvasy, P. E. Mazaré, M. Lomeli, L. Hosseini, H. Jégou. The FAISS Library. arXiv Preprint arXiv:2401.08281, 2024.
- Pgvector (Last accessed: 26.07.2024). https://github.com/pgvector/pgvector
- Pgvectorscale (Last accessed: 16.10.2024). https://github.com/timescale/pgvectorscale
- Emanuilov, S. pgvectorscale – Accelerating AI Development with High-Performance Vector Search (Last accessed: 16.10.2024). https://unfoldai.com/pgvectorscale-extension-for-ai-apps/
- Scikit Learn, MiniBatchKMeans API Reference and Documentation (Last accessed: 26.07.2024). https://scikit-learn.org/stable/modules/generated/sklearn.cluster.MiniBatchKMeans.html
- Gupta, G., T. Medini, A. Shrivastava, A. J. Smola. Bliss: A Billion Scale Index Using Iterative Re-Partitioning. – In: Proc. of 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, August 2022, pp. 486-495.
- Schuhmann, C., R. Vencu, R. Beaumont, R. Kaczmarczyk, C. Mullis, A. Katta, T. Coombes, J. Jitsev, A. Komatsuzaki. LAION-5B: An Open Large-Scale Dataset for Training Next-Generation Image-Text Models. arXiv Preprint arXiv:2210.08402, 2022.
- Radford, A., J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, G. Krueger. Learning Transferable Visual Models from Natural Language Supervision. – In: Proc. of International Conference on Machine Learning, July 2021, pp. 8748-8763.
- Beaumont, R. Semantic Search at Billions Scale (Last accessed: 26.07.2024). https://rom1504.medium.com/semantic-search-at-billions-scale-95f21695689a
- Azad, A., O. Selvitopi, M. T. Hussain, J. R. Gilbert, A. Buluç. Combinatorial BLAS 2.0: Scaling Combinatorial Algorithms on Distributed-Memory Systems. – IEEE Transactions on Parallel and Distributed Systems, Vol. 33, 2021, No 4, pp. 989-1001.
