97 citations · 127 across the 5 of their papers we have counts for
7 papers
LEARNT: A Practical Estimator for Cardinality of LIKE Queries with Formal Accuracy Guarantees
Hai Lan, Zhifeng Bao, Divesh Srivastava +3
We study the problem of cardinality estimation for LIKE queries on string data, focusing on the most common patterns in real workloads: prefix, suffix, and substring queries. We pr…
AgenticScholar: Agentic Data Management with Pipeline Orchestration for Scholarly Corpora
Hai Lan, Tingting Wang, Zhifeng Bao +7
Managing the rapidly growing scholarly corpus poses significant challenges in representation, reasoning, and efficient analysis. An ideal system should unify structured knowledge m…
Decomposition-Driven Multi-Table Retrieval and Reasoning for Numerical Question Answering
Feng Luo, Hai Lan, Hui Luo +4
In this paper, we study the problem of numerical multi-table question answering (MTQA) over large-scale table collections (e.g., online data repositories). This task is essential i…
Benchmarking RL-Enhanced Spatial Indices Against Traditional, Advanced, and Learned Counterparts
Guanli Liu, Renata Borovica-Gajic, Hai Lan +1
Reinforcement learning has recently been used to enhance index structures, giving rise to reinforcement learning-enhanced spatial indices (RLESIs) that aim to improve query efficie…
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…
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…