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Towards Model-based Pricing for Machine Learning in a Data Marketplace
Summary: Proposes a model-based pricing (MBP) framework pricing ML models not data in a data marketplace. Adds a noise-injection MBP with arbitrage-free pricing; enables revenue optimization under market scenarios and shows buyer affordability and low runtime.
(summarized by gpt-5-nano on Feb 09 2026)
Paper ID
h9fa214dba7dcf42f
Venue
SIGMOD
Year
2019
Pagerank
0.00010876597
Overall Rank
1,369 | 90.83%
DOI
10.1145/3299869.3300078
Incoming Non-self Citations Over Time
BibTeX Citation
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@inproceedings{chen_sigmod19,
title = {{Towards Model-based Pricing for Machine Learning in a Data Marketplace}},
author = {Chen, Lingjiao and Koutris, Paraschos and Kumar, Arun},
series = {{SIGMOD} '19},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3299869.3300078},
url = {https://dl.acm.org/doi/10.1145/3299869.3300078},
year = {2019}
}
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Outgoing Citations (Sorted by Pagerank)
Showing 14 of 14 cited papers.
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Venue
Pagerank
105
The MADlib Analytics Library or MAD Skills, the SQL
2012
VLDB
0.00033605348
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Boosting the Accuracy of Differentially Private Histograms Through Consistency
2010
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415
SystemML: Declarative Machine Learning on Spark
2016
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Materialization Optimizations for Feature Selection Workloads
2014
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Data Management Challenges in Production Machine Learning
2017
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Data Markets in the Cloud: An Opportunity for the Database Community
2011
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2014
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2018
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2012
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2013
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MLog: Towards Declarative In-Database Machine Learning
2017
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4,819
Optimal Random Perturbation at Multiple Privacy Levels
2009
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Differential Privacy in the Wild: A Tutorial on Current Practices & Open Challenges
2017
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2012
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