activity
20162024
most citedUpdatable Learned Indexes Meet Disk-Resident DBMS -- From Evaluations to Design Choices

26 citations · 41 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2024

Semantic-Enhanced Representation Learning for Road Networks with Temporal Dynamics

Yile Chen, Xiucheng Li, Gao Cong +2

In this study, we introduce a novel framework called Toast for learning general-purpose representations of road networks, along with its advanced counterpart DyToast, designed to e…

cs.LG20239 cited

Towards Data-centric Graph Machine Learning: Review and Outlook

Xin Zheng, Yixin Liu, Zhifeng Bao +4

Data-centric AI, with its primary focus on the collection, management, and utilization of data to drive AI models and applications, has attracted increasing attention in recent yea…

cs.DB20234 cited

A Simple Yet High-Performing On-disk Learned Index: Can We Have Our Cake and Eat it Too?

Hai Lan, Zhifeng Bao, J. Shane Culpepper +2

While in-memory learned indexes have shown promising performance as compared to B+-tree, most widely used databases in real applications still rely on disk-based operations. Based…

cs.DB202326 cited

Updatable Learned Indexes Meet Disk-Resident DBMS -- From Evaluations to Design Choices

Hai Lan, Zhifeng Bao, J. Shane Culpepper +1

Although many updatable learned indexes have been proposed in recent years, whether they can outperform traditional approaches on disk remains unknown. In this study, we revisit an…

cs.DB20162 cited

Monitoring the Top-m Aggregation in a Sliding Window of Spatial Queries

Farhana M. Choudhury, Zhifeng Bao, J. Shane Culpepper +1

In this paper, we propose and study the problem of top-m rank aggregation of spatial objects in streaming queries, where, given a set of objects O, a stream of spatial queries (kNN…