Optimization for Active Learning-based Interactive Database Exploration
Summary: Active-learning based explore-by-example for interactive DB exploration, turning user labels into an evolving interest model. New optimizations curb convergence lag, delivering responsive performance and converting the model into a query that retrieves all relevant tuples. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Enhui Huang
- 2. Liping Peng
- 3. Luciano Di Palma
- 4. Ahmed Abdelkafi
- 5. Anna Liu
- 6. Yanlei Diao
Incoming Citations (Sorted by Pagerank)
Showing 7 of 7 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,992 | Automatically Generating Data Exploration Sessions Using Deep Reinforcement Learning | 2020 | SIGMOD | 9.8415851e-05 |
| 3,144 | Active Learning for ML Enhanced Database Systems | 2020 | SIGMOD | 7.4844943e-05 |
| 4,540 | Automating Exploratory Data Analysis via Machine Learning: An Overview | 2020 | SIGMOD | 6.0978937e-05 |
| 7,200 | Guided Exploration of Data Summaries | 2022 | VLDB | 4.7980895e-05 |
| 8,011 | CAMAL: Optimizing LSM-trees via Active Learning | 2024 | SIGMOD | 4.6022693e-05 |
| 9,829 | Towards Autonomous, Hands-Free Data Exploration | 2020 | CIDR | 4.2710095e-05 |
| 11,478 | Exploring Ratings in Subjective Databases | 2021 | SIGMOD | 4.1905499e-05 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 14 of 14 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
Previous
Page 1 / 1
Next