Provenance Summaries for Answers and Non-Answers
Summary: Provenance capture limited to explanations for a specific (missing) result, addressing why-not and why provenance scalability. PUG applies sampling-based summarization to produce compact explanations for (non)answers, enabling scalable, actionable insights on real datasets. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Seokki Lee
- 2. Bertram Ludäscher
- 3. Boris Glavic
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,696 | Approximate Summaries for Why and Why-not Provenance | 2020 | VLDB | 4.9581958e-05 |
| 8,886 | Provenance-based Data Skipping | 2022 | VLDB | 4.4279829e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 31 | Provenance Semirings | 2007 | PODS | 0.0007857786 |
| 1,099 | Interpretable and Informative Explanations of Outcomes | 2015 | VLDB | 0.00014096312 |
| 1,119 | The Complexity of Causality and Responsibility for Query Answers and non-Answers | 2011 | VLDB | 0.0001386199 |
| 2,562 | Explaining Missing Answers to SPJUA Queries | 2010 | VLDB | 8.5386194e-05 |
| 4,851 | Provenance for Natural Language Queries | 2017 | VLDB | 5.8768322e-05 |
| 6,662 | Selective Provenance for Datalog Programs Using Top-K Queries | 2015 | VLDB | 4.9704872e-05 |
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,186 | On Provenance Minimization | 2011 | PODS | 5.166082e-05 |
| 10,922 | Below and Above Why-Provenance for Datalog Queries | 2024 | PODS | 4.1945683e-05 |
| 8,394 | Hypothetical Reasoning via Provenance Abstraction | 2019 | SIGMOD | 4.527807e-05 |
| 8,125 | The Complexity of Why-Provenance for Datalog Queries | 2024 | PODS | 4.5797807e-05 |
| 7,556 | Interactive Query Explanations Using Fine Grained Provenance | 2022 | SIGMOD | 4.7117814e-05 |
| 6,975 | NLProveNAns: Natural Language Provenance for Non-Answers | 2018 | VLDB | 4.8772572e-05 |
| 5,691 | Putting Things into Context: Rich Explanations for Query Answers using Join Graphs | 2021 | SIGMOD | 5.3684557e-05 |
| 4,851 | Provenance for Natural Language Queries | 2017 | VLDB | 5.8768322e-05 |
| 652 | On the Provenance of Non-Answers to Queries over Extracted Data | 2008 | VLDB | 0.00018634477 |
| 6,696 | Approximate Summaries for Why and Why-not Provenance | 2020 | VLDB | 4.9581958e-05 |