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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
6333
Venue
SIGMOD
Year
2021
Pagerank
6.2329373e-05
Overall Rank
5,396 | 62.98%
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
0.00059843817
191
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2013
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0.00026096009
385
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2009
SIGMOD
0.00019455743
663
A Formal Approach to Finding Explanations for Database Queries
2014
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0.00015174751
739
Finding Related Tables
2012
SIGMOD
0.0001448231
764
To Join or Not to Join? Thinking Twice about Joins before Feature Selection
2016
SIGMOD
0.00014226652
779
JOSIE: Overlap Set Similarity Search for Finding Joinable Tables in Data Lakes
2019
SIGMOD
0.00014092047
806
Provenance for Aggregate Queries
2011
PODS
0.00013890398
858
Interpretable and Informative Explanations of Outcomes
2015
VLDB
0.0001356511
1,121
ARDA: Automatic Relational Data Augmentation for Machine Learning
2020
VLDB
0.00012093059
1,454
Diversifying Top-K Results
2012
VLDB
0.00010739504
1,627
Efficient Provenance Storage
2008
SIGMOD
0.00010188097
1,752
Approximate Lineage for Probabilistic Databases
2008
VLDB
9.8358116e-05
1,798
SMOKE: Fine-grained Lineage at Interactive Speed
2018
VLDB
9.7361937e-05
1,912
Querying Data Provenance
2010
SIGMOD
9.4946756e-05
2,161
DIFF: A Relational Interface for Large-Scale Data Explanation
2019
VLDB
9.0606664e-05
2,195
Explaining Query Answers with Explanation-Ready Databases
2016
VLDB
8.9713779e-05
2,980
Auto-Join: Joining Tables by Leveraging Transformations
2017
VLDB
7.8975073e-05
3,681
Are Key-Foreign Key Joins Safe to Avoid when Learning High-Capacity Classifiers?
2018
VLDB
7.2037388e-05
4,525
SEMA-JOIN: Joining Semantically-Related Tables Using Big Table Corpora
2015
VLDB
6.6456999e-05
4,602
Provenance for Natural Language Queries
2017
VLDB
6.6107638e-05
4,631
Interactive Summarization and Exploration of Top Aggregate Query Answers
2018
VLDB
6.595034e-05
4,638
Going Beyond Provenance: Explaining Query Answers with Pattern-based Counterbalances
2019
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6.5919801e-05
4,747
High-Level Why-Not Explanations using Ontologies
2015
PODS
6.5252099e-05
6,360
Approximate Summaries for Why and Why-not Provenance
2020
VLDB
5.9009081e-05
6,461
Explain3D: Explaining Disagreements in Disjoint Datasets
2019
VLDB
5.8718966e-05
9,763
NLProv: Natural Language Provenance
2016
VLDB
5.2234888e-05
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