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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
0.00010912672
Overall Rank
1,363 | 90.85%
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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Showing 14 of 14 cited papers.
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Pagerank
105
The MADlib Analytics Library or MAD Skills, the SQL
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2010
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SystemML: Declarative Machine Learning on Spark
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Materialization Optimizations for Feature Selection Workloads
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