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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
6.0935553e-05
Overall Rank
5,529 | 62.83%
DOI
10.1145/3448016.3459246

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@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}
}

Incoming Citations (Sorted by Pagerank)

Showing 14 of 14 citing papers.

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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 Provenance Semirings 2007 PODS 0.00059752575
189 Scorpion: Explaining Away Outliers in Aggregate Queries 2013 VLDB 0.00025840026
391 Why Not? 2009 SIGMOD 0.00019238698
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
717 Finding Related Tables 2012 SIGMOD 0.00014532116
777 To Join or Not to Join? Thinking Twice about Joins before Feature Selection 2016 SIGMOD 0.00014054709
819 Provenance for Aggregate Queries 2011 PODS 0.00013666629
878 Interpretable and Informative Explanations of Outcomes 2015 VLDB 0.00013302631
1,038 ARDA: Automatic Relational Data Augmentation for Machine Learning 2020 VLDB 0.00012370691
1,476 Diversifying Top-K Results 2012 VLDB 0.00010551487
1,610 Efficient Provenance Storage 2008 SIGMOD 0.00010076616
1,746 SMOKE: Fine-grained Lineage at Interactive Speed 2018 VLDB 9.7341914e-05
1,785 Approximate Lineage for Probabilistic Databases 2008 VLDB 9.6482655e-05
1,827 Querying Data Provenance 2010 SIGMOD 9.5545888e-05
2,160 DIFF: A Relational Interface for Large-Scale Data Explanation 2019 VLDB 8.9364035e-05
2,222 Explaining Query Answers with Explanation-Ready Databases 2016 VLDB 8.8109051e-05
2,612 Auto-Join: Joining Tables by Leveraging Transformations 2017 VLDB 8.2265197e-05
3,670 Are Key-Foreign Key Joins Safe to Avoid when Learning High-Capacity Classifiers? 2018 VLDB 7.1108704e-05
4,206 SEMA-JOIN: Joining Semantically-Related Tables Using Big Table Corpora 2015 VLDB 6.7348167e-05
4,687 Provenance for Natural Language Queries 2017 VLDB 6.4706848e-05
4,729 Interactive Summarization and Exploration of Top Aggregate Query Answers 2018 VLDB 6.4470592e-05
4,739 Going Beyond Provenance: Explaining Query Answers with Pattern-based Counterbalances 2019 SIGMOD 6.4441962e-05
4,840 High-Level Why-Not Explanations using Ontologies 2015 PODS 6.3858476e-05
6,486 Approximate Summaries for Why and Why-not Provenance 2020 VLDB 5.7686627e-05
6,600 Explain3D: Explaining Disagreements in Disjoint Datasets 2019 VLDB 5.7403306e-05
9,940 NLProv: Natural Language Provenance 2016 VLDB 5.1062975e-05
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