5 citations · 5 across the 11 of their papers we have counts for
8 papers · 1 filter
Efficient Vector Search in the Wild: One Model for Multi-K Queries
Yifan Peng, Jiafei Fan, Xingda Wei +7
Learned top-K search is a promising approach for serving vector queries with both high accuracy and performance. However, current models trained for a specific K value fail to gene…
Revisiting Graph Analytics Benchmark
Lingkai Meng, Yu Shao, Long Yuan +7
The rise of graph analytics platforms has led to the development of various benchmarks for evaluating and comparing platform performance. However, existing benchmarks often fall sh…
A Graph-native Optimization Framework for Complex Graph Queries
Bingqing Lyu, Xiaoli Zhou, Longbin Lai +4
This technical report extends the SIGMOD 2025 paper "A Modular Graph-Native Query Optimization Framework" by providing a comprehensive exposition of GOpt's advanced technical mecha…
LSMGraph: A High-Performance Dynamic Graph Storage System with Multi-Level CSR
Song Yu, Shufeng Gong, Qian Tao +9
The growing volume of graph data may exhaust the main memory. It is crucial to design a disk-based graph storage system to ingest updates and analyze graphs efficiently. However, e…
Towards a Converged Relational-Graph Optimization Framework
Yunkai Lou, Longbin Lai, Bingqing Lyu +5
The recent ISO SQL:2023 standard adopts SQL/PGQ (Property Graph Queries), facilitating graph-like querying within relational databases. This advancement, however, underscores a sig…
A Modular Graph-Native Query Optimization Framework
Bingqing Lyu, Xiaoli Zhou, Longbin Lai +4
Complex Graph Patterns (CGPs), which combine pattern matching with relational operations, are widely used in real-world applications. Existing systems rely on monolithic architectu…