Interactive Query Explanations Using Fine Grained Provenance
Summary: Fine-grained provenance accelerates interactive query explanations by testing counterfactual interventions without re-running pipelines. Compared with IVM and tree-based methods, it avoids materialization, scales to large deletions, and better aligns with relational plans to improve data locality. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Alexander Yao (Columbia University)
BibTeX Citation
@inproceedings{yao_sigmod22,
title = {{Interactive Query Explanations Using Fine Grained Provenance}},
author = {Yao, Alexander},
series = {{SIGMOD} '22},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3514221.3520251},
url = {https://dl.acm.org/doi/10.1145/3514221.3520251},
year = {2022}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,354 | On Data-Aware Global Explainability of Graph Neural Networks | 2023 | VLDB | 5.9027055e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 11 of 11 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 17 | Provenance Semirings | 2007 | PODS | 0.00059843817 |
| 191 | Scorpion: Explaining Away Outliers in Aggregate Queries | 2013 | VLDB | 0.00026096009 |
| 438 | DBToaster: Higher-order Delta Processing for Dynamic, Frequently Fresh Views | 2012 | VLDB | 0.00018471721 |
| 620 | On Propagation of Deletions and Annotations Through Views | 2002 | PODS | 0.00015703409 |
| 663 | A Formal Approach to Finding Explanations for Database Queries | 2014 | SIGMOD | 0.00015174751 |
| 960 | Incremental Query Evaluation in a Ring of Databases | 2010 | PODS | 0.00012945163 |
| 1,798 | SMOKE: Fine-grained Lineage at Interactive Speed | 2018 | VLDB | 9.7361937e-05 |
| 1,965 | ProvSQL: Provenance and Probability Management in PostgreSQL | 2018 | VLDB | 9.3852716e-05 |
| 2,161 | DIFF: A Relational Interface for Large-Scale Data Explanation | 2019 | VLDB | 9.0606664e-05 |
| 2,195 | Explaining Query Answers with Explanation-Ready Databases | 2016 | VLDB | 8.9713779e-05 |
| 2,584 | Complaint-driven Training Data Debugging for Query 2.0 | 2020 | SIGMOD | 8.3783546e-05 |
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Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 4,602 | Provenance for Natural Language Queries | 2017 | VLDB |
| 2 | 8,145 | You Say ‘What’, I Hear ‘Where’ and ‘Why’ — (Mis-)Interpreting SQL to Derive Fine-Grained Provenance | 2018 | VLDB |
| 3 | 9,241 | Provenance for SQL through Abstract Interpretation: Value-less, but Worthwhile | 2015 | VLDB |
| 4 | 1,912 | Querying Data Provenance | 2010 | SIGMOD |
| 5 | 8,509 | Hypothetical Reasoning via Provenance Abstraction | 2019 | SIGMOD |
| 6 | 8,950 | OneProvenance: Efficient Extraction of Dynamic Coarse-Grained Provenance From Database Query Event Logs | 2023 | VLDB |
| 7 | 663 | A Formal Approach to Finding Explanations for Database Queries | 2014 | SIGMOD |
| 8 | 628 | On the Provenance of Non-Answers to Queries over Extracted Data | 2008 | VLDB |
| 9 | 2,195 | Explaining Query Answers with Explanation-Ready Databases | 2016 | VLDB |
| 10 | 5,396 | Putting Things into Context: Rich Explanations for Query Answers using Join Graphs | 2021 | SIGMOD |