19 citations · 23 across the 6 of their papers we have counts for
6 papers
Don't Forget Range Delete! Enhancing LSM-based Key-Value Stores with More Compatible Lookups and Deletes
Fan Wang, Dingheng Mo, Siqiang Luo
LSM-trees are featured by out-of-place updates, where key deletion is handled by inserting a tombstone to mark its staleness instead of removing it in place. This defers actual rem…
LSM-Vec: A Memory-Efficient Disk-Based System for Large-Scale Dynamic Vector Search
Shurui Zhong, Dingheng Mo, Siqiang Luo +1
RAG and AI retrieval have made vector approximate nearest-neighbor (ANN) search a key retrieval operation in modern data systems. These workloads are both large and dynamic. Vector…
How to Grow an LSM-tree? Towards Bridging the Gap Between Theory and Practice
Dingheng Mo, Siqiang Luo, Stratos Idreos
LSM-tree based key-value stores are widely adopted as the data storage backend in modern big data applications. The LSM-tree grows with data ingestion, by either adding levels with…
Aster: Enhancing LSM-structures for Scalable Graph Database
Dingheng Mo, Junfeng Liu, Fan Wang +1
There is a proliferation of applications requiring the management of large-scale, evolving graphs under workloads with intensive graph updates and lookups. Driven by this challenge…
Learning to Optimize LSM-trees: Towards A Reinforcement Learning based Key-Value Store for Dynamic Workloads
Dingheng Mo, Fanchao Chen, Siqiang Luo +1
LSM-trees are widely adopted as the storage backend of key-value stores. However, optimizing the system performance under dynamic workloads has not been sufficiently studied or eva…
SCARA: Scalable Graph Neural Networks with Feature-Oriented Optimization
Ningyi Liao, Dingheng Mo, Siqiang Luo +2
Recent advances in data processing have stimulated the demand for learning graphs of very large scales. Graph Neural Networks (GNNs), being an emerging and powerful approach in sol…