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Putting Things into Context: Rich Explanations for Query Answers using Join Graphs
Summary: Proposes rich explanations for query results by augmenting traditional data provenance with contextual information from related tables via join graphs. Optimizations, real-data experiments, and a user study validate meaningful, efficient explanations.
(summarized by gpt-5-nano on Feb 09 2026)
- Paper ID
- 6271
- Venue
- SIGMOD
- Year
- 2021
- Pagerank
- 5.3684557e-05
- Overall Rank
- 5,691 | 60.41%
- DOI
-
10.1145/3448016.3459246
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 14 of 14 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
| 5,313 |
XInsight: eXplainable Data Analysis Through The Lens of Causality |
2023 |
SIGMOD |
5.573009e-05 |
| 5,826 |
Why Not Yet: Fixing a Top-k Ranking that Is Not Fair to Individuals |
2023 |
VLDB |
5.3124507e-05 |
| 6,565 |
Toward Interpretable and Actionable Data Analysis with Explanations and Causality |
2022 |
VLDB |
5.0081626e-05 |
| 7,172 |
Summarized Causal Explanations For Aggregate Views |
2024 |
SIGMOD |
4.8114797e-05 |
| 8,388 |
FEDEX: An Explainability Framework for Data Exploration Steps |
2022 |
VLDB |
4.5297787e-05 |
| 9,644 |
Fair and Actionable Causal Prescription Ruleset |
2025 |
SIGMOD |
4.3109001e-05 |
| 9,703 |
CaJaDE: Explaining Query Results by Augmenting Provenance with Context |
2022 |
VLDB |
4.3005882e-05 |
| 9,766 |
DPXPlain: Privately Explaining Aggregate Query Answers |
2023 |
VLDB |
4.2856106e-05 |
| 10,147 |
Causal Explanations for Disparate Trends: Where and Why? |
2026 |
SIGMOD |
4.1945683e-05 |
| 10,429 |
CauSumX: Summarized Causal Explanations For Group-By-Average Queries |
2025 |
SIGMOD |
4.1945683e-05 |
| 10,740 |
Finding Convincing Views to Endorse a Claim |
2025 |
VLDB |
4.1945683e-05 |
| 10,875 |
SDEcho: Efficient Explanation of Aggregated Sequence Difference |
2025 |
VLDB |
4.1945683e-05 |
| 10,910 |
Postulates for Provenance: Instance-based provenance for first-order logic |
2024 |
PODS |
4.1945683e-05 |
| 11,054 |
Enriching Relations with Additional Attributes for ER |
2024 |
VLDB |
4.1945683e-05 |
Outgoing Citations (Sorted by Pagerank)
Showing 27 of 27 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 |
| 214 |
Scorpion: Explaining Away Outliers in Aggregate Queries |
2013 |
VLDB |
0.0003363692 |
| 487 |
Why Not? |
2009 |
SIGMOD |
0.00022050218 |
| 818 |
Finding Related Tables |
2012 |
SIGMOD |
0.00016311524 |
| 903 |
To Join or Not to Join? Thinking Twice about Joins before Feature Selection |
2016 |
SIGMOD |
0.0001547016 |
| 942 |
A Formal Approach to Finding Explanations for Database Queries |
2014 |
SIGMOD |
0.00015155714 |
| 1,099 |
Interpretable and Informative Explanations of Outcomes |
2015 |
VLDB |
0.00014096312 |
| 1,106 |
Provenance for Aggregate Queries |
2011 |
PODS |
0.0001398766 |
| 1,187 |
JOSIE: Overlap Set Similarity Search for Finding Joinable Tables in Data Lakes |
2019 |
SIGMOD |
0.00013443639 |
| 1,445 |
Diversifying Top-K Results |
2012 |
VLDB |
0.00011945231 |
| 1,463 |
ARDA: Automatic Relational Data Augmentation for Machine Learning |
2020 |
VLDB |
0.00011869295 |
| 1,861 |
Efficient Provenance Storage |
2008 |
SIGMOD |
0.00010287053 |
| 1,970 |
Approximate Lineage for Probabilistic Databases |
2008 |
VLDB |
9.896375e-05 |
| 2,154 |
DIFF: A Relational Interface for Large-Scale Data Explanation |
2019 |
VLDB |
9.4208667e-05 |
| 2,173 |
Querying Data Provenance |
2010 |
SIGMOD |
9.3676609e-05 |
| 2,280 |
SMOKE: Fine-grained Lineage at Interactive Speed |
2018 |
VLDB |
9.1111033e-05 |
| 2,649 |
Explaining Query Answers with Explanation-Ready Databases |
2016 |
VLDB |
8.3719123e-05 |
| 3,735 |
Auto-Join: Joining Tables by Leveraging Transformations |
2017 |
VLDB |
6.8061318e-05 |
| 4,129 |
Are Key-Foreign Key Joins Safe to Avoid when Learning High-Capacity Classifiers? |
2018 |
VLDB |
6.428887e-05 |
| 4,614 |
Interactive Summarization and Exploration of Top Aggregate Query Answers |
2018 |
VLDB |
6.0467204e-05 |
| 4,850 |
SEMA-JOIN: Joining Semantically-Related Tables Using Big Table Corpora |
2015 |
VLDB |
5.8768452e-05 |
| 4,851 |
Provenance for Natural Language Queries |
2017 |
VLDB |
5.8768322e-05 |
| 5,191 |
Going Beyond Provenance: Explaining Query Answers with Pattern-based Counterbalances |
2019 |
SIGMOD |
5.6378768e-05 |
| 5,418 |
High-Level Why-Not Explanations using Ontologies |
2015 |
PODS |
5.5178123e-05 |
| 6,475 |
Explain3D: Explaining Disagreements in Disjoint Datasets |
2019 |
VLDB |
5.0497183e-05 |
| 6,696 |
Approximate Summaries for Why and Why-not Provenance |
2020 |
VLDB |
4.9581958e-05 |
| 9,622 |
NLProv: Natural Language Provenance |
2016 |
VLDB |
4.3163112e-05 |
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