11 papers
AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments
Kefu Chen, Xin Ai, Qiange Wang +2
Graph Neural Networks (GNNs) have achieved remarkable success in various applications. Sampling-based GNN training, which conducts mini-batch training on sampled subgraphs, has bec…
DGAI: Decoupled On-Disk Graph-Based ANN Index for Efficient Updates and Queries
Jiahao Lou, Shufeng Gong, Quan Yu +6
On-disk graph-based indexes are favored for billion-scale Approximate Nearest Neighbor Search (ANNS) due to their high performance and cost-efficiency. However, existing systems ty…
ATCC: Adaptive Concurrency Control for Unforeseen Agentic Transactions
Weixing Zhou, Zhiyou Wang, Zeshun Peng +3
Data agents, empowered by Large Language Models (LLMs), introduce a new paradigm in transaction processing. Unlike traditional applications with fixed patterns, data agents run onl…
Towards Autonomous Graph Data Analytics with Analytics-Augmented Generation
Qiange Wang, Chaoyi Chen, Jingqi Gao +3
This paper argues that reliable end-to-end graph data analytics cannot be achieved by retrieval- or code-generation-centric LLM agents alone. Although large language models (LLMs)…
AdaptOrch: Task-Adaptive Multi-Agent Orchestration in the Era of LLM Performance Convergence
Geunbin Yu
As large language models from diverse providers converge toward comparable benchmark performance, the traditional paradigm of selecting a single best model per task yields diminish…
GeoLayer: Towards Low-Latency and Cost-Efficient Geo-Distributed Graph Stores with Layered Graph
Feng Yao, Xiaokang Yang, Shufeng Gong +3
The inherent connectivity and dependency of graph-structured data, combined with its unique topology-driven access patterns, pose fundamental challenges to conventional data replic…