Frequency-aware Graph Construction And Search For Dynamic Vector Databases

Abstract

Approximate Nearest Neighbor Search (ANNS) is a crucial operation in databases and artificial intelligence. While graph-based ANNS methods like HNSW and NSG excel in performance, they assume uniform query distribution. However, in real-world scenarios, user preferences and temporal dynamics often result in certain data points being queried more frequently than others, and these query patterns can change over time. To better leverage such characteristics, we propose DQF, a novel Dual-Index Query Framework. This framework features a dual-layer index structure and a dynamic search strategy based on a decision tree. The dual-layer index includes a hot index for high-frequency nodes and a full index covering the entire dataset, allowing for the separate management of hot and cold queries. Furthermore, we propose a dynamic search strategy that employs a decision tree to determine whether a query is of the high-frequency type, avoiding unnecessary searches in the full index through early term

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