A Comprehensive Study of Shapley Value in Data Analytics
Summary: First comprehensive study of Shapley value across the data-analytics workflow, formalizing DA-specific SV variants and key functionalities. Identifies four core challenges (efficiency, approximation, privacy, interpretability), analyzes solution techniques and trade-offs, and releases SVBench with empirical evaluations. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Hong Lin
- 2. Shixin Wan
- 3. Zhongle Xie
- 4. Ke Chen
- 5. Meihui Zhang
- 6. Lidan Shou
- 7. Gang Chen
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,891 | Towards Model-based Pricing for Machine Learning in a Data Marketplace | 2019 | SIGMOD | 0.00010194092 |
| 6,262 | Fast Shapley Value Computation in Data Assemblage Tasks as Cooperative Simple Games | 2024 | SIGMOD | 5.1349507e-05 |
| 6,263 | Equitable Data Valuation Meets the Right to Be Forgotten in Model Markets | 2023 | VLDB | 5.1349507e-05 |
| 7,321 | Counterfactual Explanation of Shapley Value in Data Coalitions | 2024 | VLDB | 4.7629325e-05 |
| 7,932 | P-Shapley: Shapley Values on Probabilistic Classifiers | 2024 | VLDB | 4.613363e-05 |
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