A Demonstration of QueryArtisan: Real-Time Data Lake Analysis via Dynamically Generated Data Manipulation Code
Summary: QueryArtisan uses LLMs to compile natural-language queries into just-in-time, dataset-specific data-manipulation code plus heterogeneous operators to process raw data-lake modalities. Includes cost-based optimization and dynamic multi-agent instantiation for efficient in‑situ analytics. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Wenhao Liu
- 2. Xiu Tang
- 3. Sai Wu
- 4. Chang Yao
- 5. Gongsheng Yuan
- 6. Gang Chen
Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 939 | Data Lake Management: Challenges and Opportunities | 2019 | VLDB | 0.00015187344 |
| 1,664 | On Multi-Column Foreign Key Discovery | 2010 | VLDB | 0.00010976887 |
| 6,165 | When the Web is your Data Lake: Creating a Search Engine for Datasets on the Web | 2020 | SIGMOD | 5.1728052e-05 |
| 9,961 | QueryArtisan: Generating Data Manipulation Codes for Ad-hoc Analysis in Data Lakes | 2025 | VLDB | 4.2294678e-05 |
Previous
Page 1 / 1
Next