Hypothetical Reasoning via Provenance Abstraction
Summary: Provenance abstraction via user-defined trees coarsens provenance variables for hypothetical scenarios with reduced storage. Formalizes size-granularity tradeoff, analyzes complexity, and offers algorithms/heuristics with speedups and accuracy loss. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Daniel Deutch (Tel Aviv University)
- 2. Yuval Moskovitch (Tel Aviv University)
- 3. Noam Rinetzky (Tel Aviv University)
BibTeX Citation
@inproceedings{deutch_sigmod19,
title = {{Hypothetical Reasoning via Provenance Abstraction}},
author = {Deutch, Daniel and Moskovitch, Yuval and Rinetzky, Noam},
series = {{SIGMOD} '19},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3299869.3300084},
url = {https://dl.acm.org/doi/10.1145/3299869.3300084},
year = {2019}
}Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,566 | HYPER: Hypothetical Reasoning With What-If and How-To Queries Using a Probabilistic Causal Approach | 2022 | SIGMOD | 6.5310568e-05 |
| 8,495 | Provenance-based Data Skipping | 2022 | VLDB | 5.3304338e-05 |
| 10,987 | GDPView: An Interactive Interface that Unifies Variants of Deletion Propagation (DP) | 2026 | VLDB | 4.9793485e-05 |
| 11,975 | On Optimizing the Trade-off between Privacy and Utility in Data Provenance | 2021 | SIGMOD | 4.9793485e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 19 of 19 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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