Intelligent Agents for Data Exploration
Summary: Surveys intelligent agents for conversational data exploration, spanning task-specific RL policies over OLAP/text operators to flexible LLM planners. Highlights the specificity–generality trade-off: RL requires retraining, while LLMs offer cross-task exploration but raise control and automation challenges. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Sihem Amer-Yahia (National Centre for Scientific Research; Université Grenoble Alpes)
BibTeX Citation
@article{ameryahia_vldb24,
title = {{Intelligent Agents for Data Exploration}},
author = {Amer-Yahia, Sihem},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {12},
pages = {4521--4530},
doi = {10.14778/3685800.3685913},
url = {https://doi.org/10.14778/3685800.3685913},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,362 | Exploring Exploratory Querying | 2025 | VLDB | 5.1868213e-05 |
| 11,379 | Towards Automated Cross-domain Exploratory Data Analysis through Large Language Models | 2025 | VLDB | 4.9793485e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,048 | Automatically Generating Data Exploration Sessions Using Deep Reinforcement Learning | 2020 | SIGMOD | 9.1229916e-05 |
| 5,223 | Approximation Schemes for Many-Objective Query Optimization | 2014 | SIGMOD | 6.2212838e-05 |
| 6,162 | Guided Exploration of User Groups | 2020 | VLDB | 5.8644841e-05 |
| 7,146 | Guided Exploration of Data Summaries | 2022 | VLDB | 5.5993699e-05 |
| 11,900 | EDA4SUM: Guided Exploration of Data Summaries | 2022 | VLDB | 4.9793485e-05 |
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