The Data Interaction Game
Summary: Data interaction as a two-agent identical-interest game between user and DBMS; RL learns and adapts to evolving user query strategies. scalable adaptations for large relational DBs; empirical results show superior effectiveness vs. state-of-the-art. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Ben McCamish (Oregon State University)
- 2. Vahid Ghadakchi (Oregon State University)
- 3. Arash Termehchy (Oregon State University)
- 4. Behrouz Touri (University of California San Diego)
- 5. Liang Huang (Oregon State University)
BibTeX Citation
@inproceedings{mccamish_sigmod18,
title = {{The Data Interaction Game}},
author = {McCamish, Ben and Ghadakchi, Vahid and Termehchy, Arash and Touri, Behrouz and Huang, Liang},
series = {{SIGMOD} '18},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3183713.3196899},
url = {https://dl.acm.org/doi/10.1145/3183713.3196899},
year = {2018}
}
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,594 | Exploratory Training: When Annotators Learn About Data | 2023 | SIGMOD | 5.4053095e-05 |
| 9,734 | Optimizing Dataflow Systems for Scalable Interactive Visualization | 2024 | SIGMOD | 5.227679e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 15 of 15 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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