Explain3D: Explaining Disagreements in Disjoint Datasets
Summary: Explain3D explains why similar queries differ on disjoint datasets via provenance/evidence mapping. It offers provenance- and value-based explanations under a framework with a partitioning optimizer, beating single-dataset and integration-based methods. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Xiaolan Wang (University of Massachusetts Amherst)
- 2. Alexandra Meliou (University of Massachusetts Amherst)
BibTeX Citation
@article{wang_vldb19,
title = {{Explain3D: Explaining Disagreements in Disjoint Datasets}},
author = {Wang, Xiaolan and Meliou, Alexandra},
journal = {PVLDB},
series = {{VLDB} '19},
volume = {12},
number = {7},
pages = {779--792},
doi = {10.14778/3317315.3317320},
url = {https://doi.org/10.14778/3317315.3317320},
year = {2019}
}Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,097 | Explaining Dataset Changes for Semantic Data Versioning with Explain-Da-V | 2023 | VLDB | 6.2742361e-05 |
| 5,529 | Putting Things into Context: Rich Explanations for Query Answers using Join Graphs | 2021 | SIGMOD | 6.0935553e-05 |
| 10,023 | CaJaDE: Explaining Query Results by Augmenting Provenance with Context | 2022 | VLDB | 5.0934925e-05 |
| 11,515 | Counterfactual Explanation at Will, with Zero Privacy Leakage | 2024 | SIGMOD | 4.9793485e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 31 of 31 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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| 1 | 622 | On the Provenance of Non-Answers to Queries over Extracted Data | 2008 | VLDB |
| 2 | 10,739 | Database Views as Explanations for Relational Deep Learning | 2026 | VLDB |
| 3 | 4,840 | High-Level Why-Not Explanations using Ontologies | 2015 | PODS |
| 4 | 11,450 | SDEcho: Efficient Explanation of Aggregated Sequence Difference | 2025 | VLDB |
| 5 | 5,055 | ShapGraph: An Holistic View of Explanations through Provenance Graphs and Shapley Values | 2022 | SIGMOD |
| 6 | 7,703 | Interactive Query Explanations Using Fine Grained Provenance | 2022 | SIGMOD |
| 7 | 5,097 | Explaining Dataset Changes for Semantic Data Versioning with Explain-Da-V | 2023 | VLDB |
| 8 | 2,222 | Explaining Query Answers with Explanation-Ready Databases | 2016 | VLDB |
| 9 | 670 | A Formal Approach to Finding Explanations for Database Queries | 2014 | SIGMOD |
| 10 | 5,529 | Putting Things into Context: Rich Explanations for Query Answers using Join Graphs | 2021 | SIGMOD |