Demo Data from EDBT 2026 / reviewers in the wild / expert
Dhruv Relwani
dblp:407/9945
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Software engineering, system software, and programming languages
1 paper |
Software maintenance and evolution · 77% Software testing · 23% | |
| Databases, data mining, and information retrieval
1 paper |
Data models and query languages · 100% |
Topics — the 1 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Software maintenance and evolution › software reengineering › software modernization › software migration
code migration |
0.9 | 1 | 2025 | Horizon: Robust Checks for SQL Migration Using LLMs · Proc. VLDB Endow. 2025 |
Methods — techniques the papers use, named apart from their topics
large language model · 1.7iterative query refinement · 1.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Horizon: Robust Checks for SQL Migration Using LLMsabstractLarge language models (LLMs) have recently demonstrated strong capabilities in code migration across languages, making them promising for SQL schema migration. However, achieving reliable and accurate SQL migration with LLMs remains a challenge. This paper presents the first comprehensive approach for practical and effective SQL schema migration using LLMs. We highlight the necessity of robust evaluation and iterative query refinement to achieve highly accurate migrations. Building on traditional database tools along with LLMs, we introduce novel checks to guide LLMs towards syntactically complete and functionally equivalent translations. Our approach supports all schema object types, including complex procedural constructs. Our demonstrations offer audience opportunities to explore our system using a variety of configurations, datasets and custom inputs, providing useful insights into the underlying techniques, their strengths, and limitations. K. Venkatesh Emani, Wenjing Wang 0005, Neel Ball, Kumaraswamy Boora, Carlo Curino, Avrilia Floratou, Manan Goenka, Paridhi Gupta, Katherine Lin, Nick Litombe, Jared Meade, Suryakant Mutnal, Raghu Ramakrishnan 0001, Sudhir Raparla, Dhruv Relwani, Shyam Sai, Vaibhave Sekar, Roneet Shaw, Harmeet Singh, Prasanna Sridharan, Sunidhi Tiwari |
Proc. VLDB Endow. | 18 |