11 papers
A GPU-Accelerated Framework for Multi-Attribute Range Filtered Approximate Nearest Neighbor Search
Zhonggen Li, Haoran Yu, Zixuan Xu +2
Range-filtered approximate nearest neighbor search (RFANNS) is increasingly critical for modern vector databases. However, existing solutions suffer from severe index inflation and…
Accelerating High-Dimensional Nearest Neighbor Search with Dynamic Query Preference
Yifan Zhu, Ruijie Zhao, Zhonggen Li +4
Approximate Nearest Neighbor Search (ANNS) has emerged as an essential operation in modern database and AI systems. While graph-based methods like NSG demonstrate state-of-the-art…
PiLLar: Matching for Pivot Table Schema via LLM-guided Monte-Carlo Tree Search
Yunjun Gao, Chuangyu Ouyang, Congcong Ge +1
Pivot tables are ubiquitous in data lakes of modern data ecosystems, making accurate schema matching over pivot tables a key prerequisite for data integration. In this paper, we fo…
Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration
Zhonggen Li, Xiangyu Ke, Yifan Zhu +2
Graph embeddings map graph nodes to continuous vectors and are foundational to community detection, recommendation, and many scientific applications. At billion-scale, however, exi…
All-in-one Graph-based Indexing for Hybrid Search on GPUs
Zhonggen Li, Yougen Li, Yifan Zhu +3
Hybrid search has emerged as a promising paradigm that combines lexical and semantic retrieval, enhancing accuracy for applications such as recommendations, information retrieval,…
Balancing the Blend: An Experimental Analysis of Trade-offs in Hybrid Search
Mengzhao Wang, Boyu Tan, Yunjun Gao +5
Hybrid search, the integration of lexical and semantic retrieval, has become a cornerstone of modern information retrieval systems, driven by demanding applications like Retrieval-…