2 citations · 7 across the 10 of their papers we have counts for
11 papers
FROG: Efficient Range-Filtering Approximate Nearest Neighbor Search on GPUs
Xiaokun Cui, Pengbo Liu, Jiadong Xie +4
Range-filtering approximate nearest neighbor search (RFANNS) is a fundamental operation in modern vector databases. Given a query vector and a numerical range predicate, RFANNS…
Efficient Privacy-Preserving Range Filtered Approximate Nearest Neighbor Search
Haoyu Wang, Yandi Zhang, Jiadong Xie +4
Range-filtered approximate nearest neighbor search (RFANNS) is an important primitive for vector databases; it retrieves vectors that are similar to a query and satisfy a numerical…
Efficient Coreset Selection via K-Nearest Neighbor Graphs
Yingfan Liu, Leiyu Zhang, Jiadong Xie +3
Coreset selection reduces the cost of model training by replacing a large training set with a small representative subset. Existing gradient-approximation coreset methods such as C…
Generalized Range Filtering Approximate Nearest Neighbor Search: Containment and Overlap [Technical Report]
Yingfan Liu, Tong Wu, Jiadong Xie +3
Approximate nearest neighbor (ANN) search with range filters has recently garnered significant attention. This paper delves into a generalized form of this problem, i.e., ANN searc…
RISK: Efficiently processing rich spatial-keyword queries on encrypted geo-textual data
Zhen Lv, Cong Cao, Hongwei Huo +4
Symmetric searchable encryption (SSE) for geo-textual data has attracted significant attention. However, existing schemes rely on task-specific, incompatible indices for isolated s…
Fast Tuning the Index Construction Parameters of Proximity Graphs in Vector Databases
Wenyang Zhou, Jiadong Xie, Yingfan Liu +6
k-approximate nearest neighbor search (k-ANNS) in high-dimensional vector spaces is a fundamental problem across many fields. With the advent of vector databases and retrieval-augm…
Privacy-Preserving Approximate Nearest Neighbor Search on High-Dimensional Data
Yingfan Liu, Yandi Zhang, Jiadong Xie +3
In the era of cloud computing and AI, data owners outsource ubiquitous vectors to the cloud, which furnish approximate -nearest neighbors (-ANNS) services to users. To protec…
KBest: Efficient Vector Search on Kunpeng CPU
Kaihao Ma, Meiling Wang, Senkevich Oleg +19
Vector search, which returns the vectors most similar to a given query vector from a large vector dataset, underlies many important applications such as search, recommendation, and…
Revisiting the Index Construction of Proximity Graph-Based Approximate Nearest Neighbor Search
Shuo Yang, Jiadong Xie, Yingfan Liu +5
Proximity graphs (PG) have gained increasing popularity as the state-of-the-art solutions to -approximate nearest neighbor (-ANN) search on high-dimensional data, which serve…
One Size Cannot Fit All: a Self-Adaptive Dispatcher for Skewed Hash Join in Shared-nothing RDBMSs
Jinxin Yang, Hui Li, Yiming Si +6
Shared-nothing architecture has been widely adopted in various commercial distributed RDBMSs. Thanks to the architecture, query can be processed in parallel and accelerated by scal…
Revisiting -Nearest Neighbor Graph Construction on High-Dimensional Data : Experiments and Analyses
Liu Yingfan, Cheng Hong, Cui Jiangtao
The -nearest neighbor graph (KNNG) on high-dimensional data is a data structure widely used in many applications such as similarity search, dimension reduction and clustering. D…