Data Acquisition for Improving Machine Learning Models

Summary: Formalizes a data-market framework for acquiring training data to boost ML accuracy, with buyer–provider dynamics. Proposes EA and SPS, strategies balancing exploration and exploitation to improve model accuracy; validated on real datasets. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 12367
Venue: VLDB
Year: 2021
Pagerank: 6.7895763e-05
Overall Rank: 3,750 | 73.92%
DOI: 10.14778/3467861.3467872

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 13 of 13 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
4,138	Protecting Data Markets from Strategic Buyers	2022	SIGMOD	6.4175758e-05
4,863	Data-Sharing Markets: Model, Protocol, and Algorithms to Incentivize the Formation of Data-Sharing Consortia	2023	SIGMOD	5.8697471e-05
5,024	Towards Distribution-aware Query Answering in Data Markets	2022	VLDB	5.7535043e-05
5,381	Selective Data Acquisition in the Wild for Model Charging	2022	VLDB	5.5399508e-05
5,976	Responsible Data Integration: Next-generation Challenges	2022	SIGMOD	5.245976e-05
6,077	The Fast and the Private: Task-based Dataset Search	2024	CIDR	5.2229324e-05
7,227	Data and AI Model Markets: Opportunities for Data and Model Sharing, Discovery, and Integration	2023	VLDB	4.7952919e-05
7,491	Saibot: A Differentially Private Data Search Platform	2023	VLDB	4.7180617e-05
7,932	P-Shapley: Shapley Values on Probabilistic Classifiers	2024	VLDB	4.613363e-05
8,116	LakeBench: A Benchmark for Discovering Joinable and Unionable Tables in Data Lakes	2024	VLDB	4.581507e-05
8,281	Optimizing Data Acquisition to Enhance Machine Learning Performance	2024	VLDB	4.5435639e-05
8,696	Effective Entity Augmentation By Querying External Data Sources	2023	VLDB	4.4660032e-05
10,955	Data Acquisition for Improving Model Confidence	2024	SIGMOD	4.1945683e-05

Outgoing Citations (Sorted by Pagerank)

Showing 10 of 10 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
903	To Join or Not to Join? Thinking Twice about Joins before Feature Selection	2016	SIGMOD	0.0001547016
1,463	ARDA: Automatic Relational Data Augmentation for Machine Learning	2020	VLDB	0.00011869295
1,660	Data Markets in the Cloud: An Opportunity for the Database Community	2011	VLDB	0.00010979534
1,771	On Arbitrage-free Pricing for General Data Queries	2014	VLDB	0.00010617356
1,891	Towards Model-based Pricing for Machine Learning in a Data Marketplace	2019	SIGMOD	0.00010194092
2,359	Data Market Platforms: Trading Data Assets to Solve Data Problems	2020	VLDB	8.9607667e-05
3,142	Active Learning for ML Enhanced Database Systems	2020	SIGMOD	7.4815444e-05
3,954	Efficiently Approximating Selectivity Functions using Low Overhead Regression Models	2020	VLDB	6.5926838e-05
4,129	Are Key-Foreign Key Joins Safe to Avoid when Learning High-Capacity Classifiers?	2018	VLDB	6.428887e-05
4,279	Revenue Maximization for Query Pricing	2020	VLDB	6.2953388e-05

Semantically Similar Papers

Overall Rank	Paper	Year	Venue	Pagerank
5,024	Towards Distribution-aware Query Answering in Data Markets	2022	VLDB	5.7535043e-05
11,003	Performance-Based Pricing for Federated Learning via Auction	2024	VLDB	4.1945683e-05
9,219	Intelligent Agents for Data Exploration	2024	VLDB	4.3702863e-05
9,351	On Efficient Approximate Queries over Machine Learning Models	2023	VLDB	4.3524472e-05
8,989	Stochastic Data Acquisition for Answering Queries as Time Goes by	2017	VLDB	4.413361e-05
1,891	Towards Model-based Pricing for Machine Learning in a Data Marketplace	2019	SIGMOD	0.00010194092
5,963	Automatic Data Acquisition for Deep Learning	2021	VLDB	5.2526794e-05
5,381	Selective Data Acquisition in the Wild for Model Charging	2022	VLDB	5.5399508e-05
8,281	Optimizing Data Acquisition to Enhance Machine Learning Performance	2024	VLDB	4.5435639e-05
10,955	Data Acquisition for Improving Model Confidence	2024	SIGMOD	4.1945683e-05