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Explaining Query Answers with Explanation-Ready Databases
Summary: Proposes explanation-ready databases that offline-precompute intervention effects to enable interactive explanations of query results. Formalizes an explanation-query that evaluates all explanations at once, with algorithms and real-data experiments.
(summarized by gpt-5-nano on Feb 09 2026)
Paper ID
h5f91804e94b96cea
Venue
VLDB
Year
2016
Pagerank
8.8106741e-05
Overall Rank
2,222 | 85.07%
DOI
10.14778/2856318.2856329
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(CC BY-NC-ND 4.0)
Incoming Non-self Citations Over Time
BibTeX Citation
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@article{roy_vldb16,
title = {{Explaining Query Answers with Explanation-Ready Databases}},
author = {Roy, Sudeepa and Orr, Laurel and Suciu, Dan},
journal = {PVLDB},
series = {{VLDB} '16},
volume = {9},
number = {4},
pages = {348--359},
doi = {10.14778/2856318.2856329},
url = {https://doi.org/10.14778/2856318.2856329},
year = {2016}
}
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39
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1986
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62
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1993
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190
Scorpion: Explaining Away Outliers in Aggregate Queries
2013
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0.0002582857
388
Incremental Maintenance of Views with Duplicates
1995
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0.00019342096
408
DBToaster: Higher-order Delta Processing for Dynamic, Frequently Fresh Views
2012
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0.00018894165
519
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2009
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578
The Complexity of Causality and Responsibility for Query Answers and non-Answers
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2011
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977
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2010
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1,149
Algorithms for Deferred View Maintenance
1996
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0.00011804215
1,708
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2012
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9.8184303e-05
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1997
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9.7204307e-05
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2014
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9.2548712e-05
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Tracing Data Errors with View-Conditioned Causality
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MRI: Meaningful Interpretations of Collaborative Ratings
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