CaJaDE: Explaining Query Results by Augmenting Provenance with Context
Summary: CaJaDE augments provenance with context from related tables to explain query result differences. It enumerates join-augmented provenance patterns for two results and presents concise explanations with an interactive UI for exploration. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Chenjie Li
- 2. Juseung Lee
- 3. Zhengjie Miao
- 4. Boris Glavic
- 5. Sudeepa Roy
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 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 |
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Outgoing Citations (Sorted by Pagerank)
Showing 7 of 7 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 214 | Scorpion: Explaining Away Outliers in Aggregate Queries | 2013 | VLDB | 0.0003363692 |
| 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 |
| 1,187 | JOSIE: Overlap Set Similarity Search for Finding Joinable Tables in Data Lakes | 2019 | SIGMOD | 0.00013443639 |
| 5,191 | Going Beyond Provenance: Explaining Query Answers with Pattern-based Counterbalances | 2019 | SIGMOD | 5.6378768e-05 |
| 5,691 | Putting Things into Context: Rich Explanations for Query Answers using Join Graphs | 2021 | SIGMOD | 5.3684557e-05 |
| 6,475 | Explain3D: Explaining Disagreements in Disjoint Datasets | 2019 | VLDB | 5.0497183e-05 |
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,649 | Explaining Query Answers with Explanation-Ready Databases | 2016 | VLDB | 8.3719123e-05 |
| 652 | On the Provenance of Non-Answers to Queries over Extracted Data | 2008 | VLDB | 0.00018634477 |
| 11,733 | Provenance Summaries for Answers and Non-Answers | 2018 | VLDB | 4.1945683e-05 |
| 7,482 | Provenance-Enabled Explainable AI | 2024 | SIGMOD | 4.7180617e-05 |
| 6,429 | ShapGraph: An Holistic View of Explanations through Provenance Graphs and Shapley Values | 2022 | SIGMOD | 5.0666822e-05 |
| 2,402 | Causality and Explanations in Databases | 2014 | VLDB | 8.8928361e-05 |
| 11,392 | Automated Relational Data Explanation using External Semantic Knowledge | 2022 | VLDB | 4.1945683e-05 |
| 4,851 | Provenance for Natural Language Queries | 2017 | VLDB | 5.8768322e-05 |
| 7,556 | Interactive Query Explanations Using Fine Grained Provenance | 2022 | SIGMOD | 4.7117814e-05 |
| 5,691 | Putting Things into Context: Rich Explanations for Query Answers using Join Graphs | 2021 | SIGMOD | 5.3684557e-05 |