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 (Illinois Institute of Technology)
- 2. Juseung Lee (Illinois Institute of Technology)
- 3. Zhengjie Miao (Duke University)
- 4. Boris Glavic (Illinois Institute of Technology)
- 5. Sudeepa Roy (Duke University)
BibTeX Citation
@article{li_vldb22,
title = {{CaJaDE: Explaining Query Results by Augmenting Provenance with Context}},
author = {Li, Chenjie and Lee, Juseung and Miao, Zhengjie and Glavic, Boris and Roy, Sudeepa},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {12},
pages = {3594--3597},
doi = {10.14778/3554821.3554852},
url = {https://doi.org/10.14778/3554821.3554852},
year = {2022}
}Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,353 | Why Not Yet: Fixing a Top-k Ranking that Is Not Fair to Individuals | 2023 | VLDB | 6.1637765e-05 |
| 6,699 | Toward Interpretable and Actionable Data Analysis with Explanations and Causality | 2022 | VLDB | 5.7058728e-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 |
|---|---|---|---|---|
| 189 | Scorpion: Explaining Away Outliers in Aggregate Queries | 2013 | VLDB | 0.00025840026 |
| 670 | A Formal Approach to Finding Explanations for Database Queries | 2014 | SIGMOD | 0.00014954494 |
| 694 | JOSIE: Overlap Set Similarity Search for Finding Joinable Tables in Data Lakes | 2019 | SIGMOD | 0.00014727089 |
| 878 | Interpretable and Informative Explanations of Outcomes | 2015 | VLDB | 0.00013302631 |
| 4,739 | Going Beyond Provenance: Explaining Query Answers with Pattern-based Counterbalances | 2019 | SIGMOD | 6.4441962e-05 |
| 5,529 | Putting Things into Context: Rich Explanations for Query Answers using Join Graphs | 2021 | SIGMOD | 6.0935553e-05 |
| 6,600 | Explain3D: Explaining Disagreements in Disjoint Datasets | 2019 | VLDB | 5.7403306e-05 |
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Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 2,222 | Explaining Query Answers with Explanation-Ready Databases | 2016 | VLDB |
| 2 | 622 | On the Provenance of Non-Answers to Queries over Extracted Data | 2008 | VLDB |
| 3 | 12,237 | Provenance Summaries for Answers and Non-Answers | 2018 | VLDB |
| 4 | 8,076 | Provenance-Enabled Explainable AI | 2024 | SIGMOD |
| 5 | 5,055 | ShapGraph: An Holistic View of Explanations through Provenance Graphs and Shapley Values | 2022 | SIGMOD |
| 6 | 1,981 | Causality and Explanations in Databases | 2014 | VLDB |
| 7 | 11,898 | Automated Relational Data Explanation using External Semantic Knowledge | 2022 | VLDB |
| 8 | 4,687 | Provenance for Natural Language Queries | 2017 | VLDB |
| 9 | 7,703 | Interactive Query Explanations Using Fine Grained Provenance | 2022 | SIGMOD |
| 10 | 5,529 | Putting Things into Context: Rich Explanations for Query Answers using Join Graphs | 2021 | SIGMOD |