How to Price Shared Optimizations in the Cloud
Summary: Introduces a mechanism-design approach to select and price shared cloud optimizations (indexes, materialized views) in data-management services, using a Shapley Value Mechanism to share costs among selfish users. Extends to offline additive and online substitutive settings, proves truthfulness and cost-recovery, and shows empirical gains over regret-based approaches. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
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Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,298 | Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms | 2019 | VLDB | 0.00012758104 |
| 2,743 | Toward Practical Query Pricing with QueryMarket | 2013 | SIGMOD | 8.1897331e-05 |
| 7,932 | P-Shapley: Shapley Values on Probabilistic Classifiers | 2024 | VLDB | 4.613363e-05 |
| 9,444 | Online Optimization and Fair Costing for Dynamic Data Sharing in a Cloud Data Market | 2014 | SIGMOD | 4.3408772e-05 |
| 12,045 | COCCUS: Self-Configured Cost-Based Query Services in the Cloud | 2013 | SIGMOD | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 1 of 1 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,207 | Predicting Cost Amortization for Query Services | 2011 | SIGMOD | 7.3818982e-05 |
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