| 4,577 |
HYPER: Hypothetical Reasoning With What-If and How-To Queries Using a Probabilistic Causal Approach |
2022 |
SIGMOD |
16 |
6.5111757e-05 |
| 4,697 |
Provenance for Natural Language Queries |
2017 |
VLDB |
8 |
6.4479645e-05 |
| 6,450 |
Summarized Causal Explanations For Aggregate Views |
2024 |
SIGMOD |
12 |
5.7635046e-05 |
| 6,538 |
PreFair: Privately Generating Justifiably Fair Synthetic Data |
2023 |
VLDB |
5 |
5.7352151e-05 |
| 6,728 |
Selective Provenance for Datalog Programs Using Top-K Queries |
2015 |
VLDB |
10 |
5.6812425e-05 |
| 6,753 |
On Multiple Semantics for Declarative Database Repairs |
2020 |
SIGMOD |
14 |
5.673161e-05 |
| 6,916 |
Synthesizing Linked Data Under Cardinality and Integrity Constraints |
2021 |
SIGMOD |
3 |
5.6320169e-05 |
| 7,339 |
NLProveNAns: Natural Language Provenance for Non-Answers |
2018 |
VLDB |
2 |
5.5337666e-05 |
| 7,531 |
ExplainED: Explanations for EDA Notebooks |
2020 |
VLDB |
4 |
5.4858756e-05 |
| 8,375 |
FEDEX: An Explainability Framework for Data Exploration Steps |
2022 |
VLDB |
11 |
5.3280107e-05 |
| 8,910 |
DPXPlain: Privately Explaining Aggregate Query Answers |
2023 |
VLDB |
9 |
5.2369069e-05 |
| 9,001 |
Qr-Hint: Actionable Hints Towards Correcting Wrong SQL Queries |
2024 |
SIGMOD |
4 |
5.2260584e-05 |
| 9,035 |
The Cost of Representation by Subset Repairs |
2025 |
VLDB |
2 |
5.2191717e-05 |
| 9,090 |
Understanding Queries by Conditional Instances |
2022 |
SIGMOD |
6 |
5.2092307e-05 |
| 9,980 |
NLProv: Natural Language Provenance |
2016 |
VLDB |
3 |
5.0870037e-05 |
| 10,600 |
Differentially Private Explanations for Clusters |
2026 |
SIGMOD |
0 |
4.9605343e-05 |
| 10,690 |
Analyzing Deviations from Monotonic Trends through Database Repair |
2026 |
SIGMOD |
0 |
4.9605343e-05 |
| 10,866 |
Measuring Database Unfairness via Dependency Quantification Under Differential Privacy |
2026 |
VLDB |
0 |
4.9605343e-05 |
| 11,036 |
Qr-Hint: Formally Verified and AI-Explained SQL Tutoring |
2026 |
VLDB |
0 |
4.9605343e-05 |
| 11,211 |
CauSumX: Summarized Causal Explanations For Group-By-Average Queries |
2025 |
SIGMOD |
0 |
4.9605343e-05 |
| 11,269 |
Computing Inconsistency Measures Under Differential Privacy |
2025 |
SIGMOD |
2 |
4.9605343e-05 |
| 11,422 |
Finding Convincing Views to Endorse a Claim |
2025 |
VLDB |
4 |
4.9605343e-05 |
| 11,459 |
ClaimIt: Finding Convincing Views to Endorse a Claim |
2025 |
VLDB |
1 |
4.9605343e-05 |
| 11,700 |
PD-Explain: A Unified Python-native Framework for Query Explanations Over DataFrames |
2024 |
VLDB |
0 |
4.9605343e-05 |
| 11,719 |
DP-PQD: Privately Detecting Per-Query Gaps In Synthetic Data Generated By Black-Box Mechanisms |
2024 |
VLDB |
1 |
4.9605343e-05 |
| 11,840 |
Explaining Differentially Private Query Results With DPXPlain |
2023 |
VLDB |
0 |
4.9605343e-05 |
| 12,025 |
On Optimizing the Trade-off between Privacy and Utility in Data Provenance |
2021 |
SIGMOD |
1 |
4.9605343e-05 |
| 12,132 |
T-REx: Table Repair Explanations |
2020 |
SIGMOD |
0 |
4.9605343e-05 |
| 12,163 |
MuSe: Multiple Deletion Semantics for Data Repair |
2020 |
VLDB |
1 |
4.9605343e-05 |
| 12,289 |
QuestPro: Queries in SPARQL Through Provenance |
2018 |
VLDB |
0 |
4.9605343e-05 |
| 13,673 |
Demonstration of DPClustX: Differentially Private Explanations for Clusters |
2025 |
SIGMOD |
0 |
- |