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 (Illinois Institute of Technology)
- 2. Bertram Ludäscher (University of Illinois Urbana-Champaign)
- 3. Boris Glavic (Illinois Institute of Technology)
BibTeX Citation
@article{lee_vldb18,
title = {{Provenance Summaries for Answers and Non-Answers}},
author = {Lee, Seokki and Ludäscher, Bertram and Glavic, Boris},
journal = {PVLDB},
series = {{VLDB} '18},
volume = {11},
number = {12},
pages = {1954--1957},
doi = {10.14778/3229863.3236233},
url = {https://doi.org/10.14778/3229863.3236233},
year = {2018}
}Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,360 | Approximate Summaries for Why and Why-not Provenance | 2020 | VLDB | 5.9009081e-05 |
| 8,889 | Provenance-based Data Skipping | 2022 | VLDB | 5.3512428e-05 |
Previous
Page 1 / 1
Next
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 |
|---|---|---|---|---|
| 17 | Provenance Semirings | 2007 | PODS | 0.00059843817 |
| 605 | The Complexity of Causality and Responsibility for Query Answers and non-Answers | 2011 | VLDB | 0.00015839628 |
| 858 | Interpretable and Informative Explanations of Outcomes | 2015 | VLDB | 0.0001356511 |
| 2,032 | Explaining Missing Answers to SPJUA Queries | 2010 | VLDB | 9.2828822e-05 |
| 4,602 | Provenance for Natural Language Queries | 2017 | VLDB | 6.6107638e-05 |
| 6,587 | Selective Provenance for Datalog Programs Using Top-K Queries | 2015 | VLDB | 5.8333865e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 6,358 | On Provenance Minimization | 2011 | PODS |
| 2 | 8,509 | Hypothetical Reasoning via Provenance Abstraction | 2019 | SIGMOD |
| 3 | 11,142 | Below and Above Why-Provenance for Datalog Queries | 2024 | PODS |
| 4 | 8,100 | The Complexity of Why-Provenance for Datalog Queries | 2024 | PODS |
| 5 | 7,977 | Interactive Query Explanations Using Fine Grained Provenance | 2022 | SIGMOD |
| 6 | 7,175 | NLProveNAns: Natural Language Provenance for Non-Answers | 2018 | VLDB |
| 7 | 5,396 | Putting Things into Context: Rich Explanations for Query Answers using Join Graphs | 2021 | SIGMOD |
| 8 | 4,602 | Provenance for Natural Language Queries | 2017 | VLDB |
| 9 | 628 | On the Provenance of Non-Answers to Queries over Extracted Data | 2008 | VLDB |
| 10 | 6,360 | Approximate Summaries for Why and Why-not Provenance | 2020 | VLDB |