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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)

Paper ID
11999
Venue
VLDB
Year
2019
Pagerank
5.9422515e-05
Overall Rank
4,758 | 66.91%
DOI
10.14778/3275536.3275542

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 7 of 7 citing papers.

Rank Citing Paper Year Venue Pagerank
1,993 Automatically Generating Data Exploration Sessions Using Deep Reinforcement Learning 2020 SIGMOD 9.8453334e-05
3,142 Active Learning for ML Enhanced Database Systems 2020 SIGMOD 7.4815444e-05
4,540 Automating Exploratory Data Analysis via Machine Learning: An Overview 2020 SIGMOD 6.1033443e-05
7,222 Guided Exploration of Data Summaries 2022 VLDB 4.797186e-05
8,009 CAMAL: Optimizing LSM-trees via Active Learning 2024 SIGMOD 4.6066863e-05
9,830 Towards Autonomous, Hands-Free Data Exploration 2020 CIDR 4.2751057e-05
11,474 Exploring Ratings in Subjective Databases 2021 SIGMOD 4.1945683e-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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