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Optimization for Active Learning-based Interactive Database Exploration

Summary: Frames explore-by-example as an active-learning database service, exploiting query properties for tailored exploration algorithms. Optimizations overcome active learning’s slow convergence, delivering substantially higher accuracy and interactive efficiency than prior methods on real data. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
hb04521470a975d90
Venue
VLDB
Year
2019
Pagerank
6.5684897e-05
Overall Rank
4,509 | 69.70%
DOI
10.14778/3275536.3275542
PDF
Download (CC BY-NC-ND 4.0)

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{huang_vldb19,
        title = {{Optimization for Active Learning-based Interactive Database Exploration}},
        author = {Huang, Enhui and Peng, Liping and Di Palma, Luciano and Abdelkafi, Ahmed and Liu, Anna and Diao, Yanlei},
        journal = {PVLDB},
        series = {{VLDB} '19},
        volume = {12},
        number = {1},
        pages = {71--84},
        doi = {10.14778/3275536.3275542},
        url = {https://doi.org/10.14778/3275536.3275542},
        year = {2019}
}

Incoming Citations (Sorted by Pagerank)

Showing 7 of 7 citing papers.

Rank Citing Paper Year Venue Pagerank
2,050 Automatically Generating Data Exploration Sessions Using Deep Reinforcement Learning 2020 SIGMOD 9.1186729e-05
2,278 Active Learning for ML Enhanced Database Systems 2020 SIGMOD 8.7057608e-05
4,499 Automating Exploratory Data Analysis via Machine Learning: An Overview 2020 SIGMOD 6.5731941e-05
7,148 Guided Exploration of Data Summaries 2022 VLDB 5.5967192e-05
7,909 CAMAL: Optimizing LSM-trees via Active Learning 2024 SIGMOD 5.4266123e-05
10,175 Towards Autonomous, Hands-Free Data Exploration 2020 CIDR 5.0658661e-05
11,985 Exploring Ratings in Subjective Databases 2021 SIGMOD 4.9769913e-05
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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.

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