Back to authors
Wan Shen Lim
- Author ID
- o0000-0003-1508-2080
- ORCID
-
0000-0003-1508-2080
- Links
-
(found by gpt-5.6-luna on jul 24 2026)
- Most Frequent Institution
- Carnegie Mellon University
- Pagerank
- 0.090278267
- Overall Rank
- 782 | 96.39%
- Paper Count
- 12
Affiliation Timeline
Incoming Non-self Citations Over Time
Total yearly non-self incoming citations across all papers by this author.
Publications by Paper Pagerank
Showing all 12 publications. Total citations include self and non-self citations.
| Rank |
Title |
Year |
Venue |
Total Citations |
Pagerank |
| 3,588 |
Make Your Database System Dream of Electric Sheep: Towards Self-Driving Operation |
2021 |
VLDB |
19 |
7.1841858e-05 |
| 3,964 |
MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems |
2021 |
SIGMOD |
22 |
6.889374e-05 |
| 5,548 |
Tigger: A Database Proxy That Bounces With User-Bypass |
2023 |
VLDB |
7 |
6.0857218e-05 |
| 6,300 |
Mainlining Databases: Supporting Fast Transactional Workloads on Universal Columnar Data File Formats |
2021 |
VLDB |
11 |
5.8174924e-05 |
| 6,619 |
Everything is a Transaction: Unifying Logical Concurrency Control and Physical Data Structure Maintenance in Database Management Systems |
2021 |
CIDR |
7 |
5.7310081e-05 |
| 7,915 |
Tastes Great! Less Filling! High Performance and Accurate Training Data Collection for Self-Driving Database Management Systems |
2022 |
SIGMOD |
10 |
5.4258674e-05 |
| 7,981 |
The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions |
2024 |
VLDB |
6 |
5.4117272e-05 |
| 9,030 |
BPF-DB: A Kernel-Embedded Transactional Database Management System For eBPF Applications |
2025 |
SIGMOD |
2 |
5.2325065e-05 |
| 9,134 |
Hit the Gym: Accelerating Query Execution to Efficiently Bootstrap Behavior Models for Self-Driving Database Management Systems |
2024 |
VLDB |
6 |
5.2223611e-05 |
| 9,711 |
Database Gyms |
2023 |
CIDR |
7 |
5.1352441e-05 |
| 10,296 |
This is Going to Sound Crazy, But What If We Used Large Language Models to Boost Automatic Database Tuning Algorithms By Leveraging Prior History? We Will Find Better Configurations More Quickly Than Retraining From Scratch! |
2026 |
SIGMOD |
1 |
5.0407989e-05 |
| 11,148 |
Automated Database Tuning vs. Human-Based Tuning in a Simulated Stressful Work Environment: A Demonstration of the Database Gym |
2025 |
SIGMOD |
0 |
4.9769913e-05 |
Frequent Co-authors
Co-authored at least 5 papers.