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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
- 6272
- Venue
- SIGMOD
- Year
- 2021
- Pagerank
- 5.3633001e-05
- Overall Rank
- 5,706 | 60.35%
- 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 |
| 5,321 |
XInsight: eXplainable Data Analysis Through The Lens of Causality |
2023 |
SIGMOD |
5.5676564e-05 |
| 5,839 |
Why Not Yet: Fixing a Top-k Ranking that Is Not Fair to Individuals |
2023 |
VLDB |
5.3073497e-05 |
| 6,565 |
Toward Interpretable and Actionable Data Analysis with Explanations and Causality |
2022 |
VLDB |
5.0033542e-05 |
| 7,172 |
Summarized Causal Explanations For Aggregate Views |
2024 |
SIGMOD |
4.8068645e-05 |
| 8,388 |
FEDEX: An Explainability Framework for Data Exploration Steps |
2022 |
VLDB |
4.525436e-05 |
| 9,644 |
Fair and Actionable Causal Prescription Ruleset |
2025 |
SIGMOD |
4.3067693e-05 |
| 9,702 |
CaJaDE: Explaining Query Results by Augmenting Provenance with Context |
2022 |
VLDB |
4.2964668e-05 |
| 9,768 |
DPXPlain: Privately Explaining Aggregate Query Answers |
2023 |
VLDB |
4.2815042e-05 |
| 10,147 |
Causal Explanations for Disparate Trends: Where and Why? |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,439 |
CauSumX: Summarized Causal Explanations For Group-By-Average Queries |
2025 |
SIGMOD |
4.1905499e-05 |
| 10,747 |
Finding Convincing Views to Endorse a Claim |
2025 |
VLDB |
4.1905499e-05 |
| 10,879 |
SDEcho: Efficient Explanation of Aggregated Sequence Difference |
2025 |
VLDB |
4.1905499e-05 |
| 10,914 |
Postulates for Provenance: Instance-based provenance for first-order logic |
2024 |
PODS |
4.1905499e-05 |
| 11,057 |
Enriching Relations with Additional Attributes for ER |
2024 |
VLDB |
4.1905499e-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 |
0.00078516827 |
| 213 |
Scorpion: Explaining Away Outliers in Aggregate Queries |
2013 |
VLDB |
0.0003371037 |
| 487 |
Why Not? |
2009 |
SIGMOD |
0.00022030123 |
| 814 |
Finding Related Tables |
2012 |
SIGMOD |
0.00016298739 |
| 901 |
To Join or Not to Join? Thinking Twice about Joins before Feature Selection |
2016 |
SIGMOD |
0.00015462938 |
| 943 |
A Formal Approach to Finding Explanations for Database Queries |
2014 |
SIGMOD |
0.00015140995 |
| 1,096 |
Interpretable and Informative Explanations of Outcomes |
2015 |
VLDB |
0.00014088686 |
| 1,106 |
Provenance for Aggregate Queries |
2011 |
PODS |
0.00013976386 |
| 1,185 |
JOSIE: Overlap Set Similarity Search for Finding Joinable Tables in Data Lakes |
2019 |
SIGMOD |
0.00013432692 |
| 1,435 |
Diversifying Top-K Results |
2012 |
VLDB |
0.00011981694 |
| 1,462 |
ARDA: Automatic Relational Data Augmentation for Machine Learning |
2020 |
VLDB |
0.00011866333 |
| 1,864 |
Efficient Provenance Storage |
2008 |
SIGMOD |
0.00010277171 |
| 1,972 |
Approximate Lineage for Probabilistic Databases |
2008 |
VLDB |
9.8937766e-05 |
| 2,158 |
DIFF: A Relational Interface for Large-Scale Data Explanation |
2019 |
VLDB |
9.4117885e-05 |
| 2,182 |
Querying Data Provenance |
2010 |
SIGMOD |
9.3596252e-05 |
| 2,286 |
SMOKE: Fine-grained Lineage at Interactive Speed |
2018 |
VLDB |
9.102574e-05 |
| 2,655 |
Explaining Query Answers with Explanation-Ready Databases |
2016 |
VLDB |
8.3638668e-05 |
| 3,738 |
Auto-Join: Joining Tables by Leveraging Transformations |
2017 |
VLDB |
6.8006812e-05 |
| 4,123 |
Are Key-Foreign Key Joins Safe to Avoid when Learning High-Capacity Classifiers? |
2018 |
VLDB |
6.4290005e-05 |
| 4,608 |
Interactive Summarization and Exploration of Top Aggregate Query Answers |
2018 |
VLDB |
6.046643e-05 |
| 4,849 |
SEMA-JOIN: Joining Semantically-Related Tables Using Big Table Corpora |
2015 |
VLDB |
5.872093e-05 |
| 4,853 |
Provenance for Natural Language Queries |
2017 |
VLDB |
5.8711821e-05 |
| 5,192 |
Going Beyond Provenance: Explaining Query Answers with Pattern-based Counterbalances |
2019 |
SIGMOD |
5.6324589e-05 |
| 5,428 |
High-Level Why-Not Explanations using Ontologies |
2015 |
PODS |
5.5125084e-05 |
| 6,468 |
Explain3D: Explaining Disagreements in Disjoint Datasets |
2019 |
VLDB |
5.0448709e-05 |
| 6,700 |
Approximate Summaries for Why and Why-not Provenance |
2020 |
VLDB |
4.9534371e-05 |
| 9,622 |
NLProv: Natural Language Provenance |
2016 |
VLDB |
4.3121745e-05 |
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