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Dynamic Approximate Nearest Neighbor Search in High-Dimensional Spaces: A Hybrid Graph-Tree Approach | ||
| Journal of Algorithms and Computation | ||
| دوره 58، شماره 1، مهر 2026، صفحه 60-81 اصل مقاله (1.27 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/jac.2026.407468.1249 | ||
| نویسنده | ||
| Amirali Ghajari* | ||
| Islamic Azad University Central Tehran Branch | ||
| چکیده | ||
| State-of-the-art static Approximate Nearest Neighbor (ANN) search methods, like HNSW, are inefficient for dynamic environments due to costly index rebuilds. This paper addresses this gap by proposing the Hybrid Graph-Tree (HGT), a novel data structure for high-performance ANN search on streaming data. HGT synergistically combines a global navigational tree for rapid search space pruning with localized navigable graphs at its leaves for accurate local search. A key feature is an efficient leaf-splitting mechanism that maintains index balance and performance during continuous insertions without global reconstruction. Our extensive experiments demonstrate that HGT achieves query performance competitive with static HNSW while offering orders-of-magnitude faster insertions. The structure’s ability to maintain stable query latency and high recall under dynamic workloads establishes it as a robust solution for next-generation vector databases and real-time AI systems, bridging the critical gap between static index performance and dynamic data requirements. | ||
| کلیدواژهها | ||
| Vector Databases؛ Streaming Data؛ Computational Geometry؛ Curse of Dimensionality؛ Vector Embeddings؛ Incremental Indexing؛ Metric Indexing؛ Low-Latency Updates | ||
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آمار تعداد مشاهده مقاله: 100 تعداد دریافت فایل اصل مقاله: 42 |
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