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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
h5de16d5cd26eb331
Venue
SIGMOD
Year
2021
Pagerank
6.0906707e-05
Overall Rank
5,533 | 62.82%
DOI
10.1145/3448016.3459246
Incoming Non-self Citations Over Time
BibTeX Citation
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@inproceedings{li_sigmod21,
title = {{Putting Things into Context: Rich Explanations for Query Answers using Join Graphs}},
author = {Li, Chenjie and Miao, Zhengjie and Zeng, Qitian and Glavic, Boris and Roy, Sudeepa},
series = {{SIGMOD} '21},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3448016.3459246},
url = {https://dl.acm.org/doi/10.1145/3448016.3459246},
year = {2021}
}
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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
17
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2007
PODS
0.00059813669
190
Scorpion: Explaining Away Outliers in Aggregate Queries
2013
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0.0002582857
389
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2009
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0.00019313101
671
A Formal Approach to Finding Explanations for Database Queries
2014
SIGMOD
0.00014948069
694
JOSIE: Overlap Set Similarity Search for Finding Joinable Tables in Data Lakes
2019
SIGMOD
0.00014721161
718
Finding Related Tables
2012
SIGMOD
0.00014526813
779
To Join or Not to Join? Thinking Twice about Joins before Feature Selection
2016
SIGMOD
0.00014048128
811
Provenance for Aggregate Queries
2011
PODS
0.00013746145
878
Interpretable and Informative Explanations of Outcomes
2015
VLDB
0.00013296412
1,038
ARDA: Automatic Relational Data Augmentation for Machine Learning
2020
VLDB
0.000123653
1,476
Diversifying Top-K Results
2012
VLDB
0.00010546498
1,611
Efficient Provenance Storage
2008
SIGMOD
0.00010072076
1,713
SMOKE: Fine-grained Lineage at Interactive Speed
2018
VLDB
9.8129981e-05
1,785
Approximate Lineage for Probabilistic Databases
2008
VLDB
9.6438009e-05
1,827
Querying Data Provenance
2010
SIGMOD
9.5518797e-05
2,162
DIFF: A Relational Interface for Large-Scale Data Explanation
2019
VLDB
8.9344773e-05
2,222
Explaining Query Answers with Explanation-Ready Databases
2016
VLDB
8.8106741e-05
2,613
Auto-Join: Joining Tables by Leveraging Transformations
2017
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8.2229082e-05
3,673
Are Key-Foreign Key Joins Safe to Avoid when Learning High-Capacity Classifiers?
2018
VLDB
7.1075403e-05
4,204
SEMA-JOIN: Joining Semantically-Related Tables Using Big Table Corpora
2015
VLDB
6.7331936e-05
4,684
Provenance for Natural Language Queries
2017
VLDB
6.4692258e-05
4,731
Interactive Summarization and Exploration of Top Aggregate Query Answers
2018
VLDB
6.4440072e-05
4,741
Going Beyond Provenance: Explaining Query Answers with Pattern-based Counterbalances
2019
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6.4411456e-05
4,842
High-Level Why-Not Explanations using Ontologies
2015
PODS
6.3828247e-05
6,488
Approximate Summaries for Why and Why-not Provenance
2020
VLDB
5.7659318e-05
6,602
Explain3D: Explaining Disagreements in Disjoint Datasets
2019
VLDB
5.7376131e-05
9,947
NLProv: Natural Language Provenance
2016
VLDB
5.1038803e-05
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