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Intelligent Agents for Data Exploration

Summary: Surveys RL-based agents for incremental data exploration—operators, hand-crafted rewards, and task-specific successes. Positions LLM-powered agents vs RL on an automation–control spectrum, argues LLMs may reduce retraining and enable more general planner-like exploration while highlighting open tradeoffs. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13697
Venue
VLDB
Year
2024
Pagerank
4.366098e-05
Overall Rank
9,222 | 35.91%
DOI
10.14778/3685800.3685913

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
10,790 Towards Automated Cross-domain Exploratory Data Analysis through Large Language Models 2025 VLDB 4.1905499e-05
10,864 Exploring Exploratory Querying 2025 VLDB 4.1905499e-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
1,992 Automatically Generating Data Exploration Sessions Using Deep Reinforcement Learning 2020 SIGMOD 9.8415851e-05
4,876 Approximation Schemes for Many-Objective Query Optimization 2014 SIGMOD 5.8544467e-05
5,481 Guided Exploration of User Groups 2020 VLDB 5.483544e-05
7,200 Guided Exploration of Data Summaries 2022 VLDB 4.7980895e-05
11,396 EDA4SUM: Guided Exploration of Data Summaries 2022 VLDB 4.1905499e-05
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