62 citations · 93 across the 19 of their papers we have counts for
10 papers · 1 filter
Reveal Hidden Pitfalls and Navigate Next Generation of Vector Similarity Search from Task-Centric Views
Tingyang Chen, Cong Fu, Jiahua Wu +6
Vector Similarity Search (VSS) in high-dimensional spaces is rapidly emerging as core functionality in next-generation database systems for numerous data-intensive services -- from…
Beyond Relational: Semantic-Aware Multi-Modal Analytics with LLM-Native Query Optimization
Junhao Zhu, Lu Chen, Xiangyu Ke +4
Multi-modal analytical processing has the potential to transform applications in e-commerce, healthcare, entertainment, and beyond. However, real-world adoption remains elusive due…
Scalable Graph Indexing using GPUs for Approximate Nearest Neighbor Search
Zhonggen Li, Xiangyu Ke, Yifan Zhu +3
Approximate nearest neighbor search (ANNS) in high-dimensional vector spaces has a wide range of real-world applications. Numerous methods have been proposed to handle ANNS efficie…
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-…
OneDB: A Distributed Multi-Metric Data Similarity Search System
Tang Qian, Yifan Zhu, Lu Chen +5
Increasingly massive volumes of multi-modal data are being accumulated in many {real world} settings, including in health care and e-commerce. This development calls for effective…
Empowering Graph-based Approximate Nearest Neighbor Search with Adaptive Awareness Capabilities
Jiancheng Ruan, Tingyang Chen, Renchi Yang +2
Approximate Nearest Neighbor Search (ANNS) in high-dimensional spaces finds extensive applications in databases, information retrieval, recommender systems, etc. While graph-based…
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…
Stitching Inner Product and Euclidean Metrics for Topology-aware Maximum Inner Product Search
Tingyang Chen, Cong Fu, Xiangyu Ke +3
Maximum Inner Product Search (MIPS) is a fundamental challenge in machine learning and information retrieval, particularly in high-dimensional data applications. Existing approache…
Maximum Inner Product is Query-Scaled Nearest Neighbor
Tingyang Chen, Cong Fu, Kun Wang +5
Maximum Inner Product Search (MIPS) for high-dimensional vectors is pivotal across databases, information retrieval, and artificial intelligence. Existing methods either reduce MIP…
Accelerating Graph Indexing for ANNS on Modern CPUs
Mengzhao Wang, Haotian Wu, Xiangyu Ke +3
In high-dimensional vector spaces, Approximate Nearest Neighbor Search (ANNS) is a key component in database and artificial intelligence infrastructures. Graph-based methods, parti…