| 4,568 |
HYPER: Hypothetical Reasoning With What-If and How-To Queries Using a Probabilistic Causal Approach |
2022 |
SIGMOD |
16 |
6.5279657e-05 |
| 4,684 |
Provenance for Natural Language Queries |
2017 |
VLDB |
8 |
6.4692258e-05 |
| 6,438 |
Summarized Causal Explanations For Aggregate Views |
2024 |
SIGMOD |
12 |
5.7826256e-05 |
| 6,525 |
PreFair: Privately Generating Justifiably Fair Synthetic Data |
2023 |
VLDB |
5 |
5.7542422e-05 |
| 6,712 |
Selective Provenance for Datalog Programs Using Top-K Queries |
2015 |
VLDB |
10 |
5.70009e-05 |
| 6,734 |
On Multiple Semantics for Declarative Database Repairs |
2020 |
SIGMOD |
14 |
5.6919385e-05 |
| 6,898 |
Synthesizing Linked Data Under Cardinality and Integrity Constraints |
2021 |
SIGMOD |
3 |
5.6506085e-05 |
| 7,321 |
NLProveNAns: Natural Language Provenance for Non-Answers |
2018 |
VLDB |
2 |
5.5521254e-05 |
| 7,513 |
ExplainED: Explanations for EDA Notebooks |
2020 |
VLDB |
4 |
5.5040697e-05 |
| 8,360 |
FEDEX: An Explainability Framework for Data Exploration Steps |
2022 |
VLDB |
11 |
5.3456868e-05 |
| 8,890 |
DPXPlain: Privately Explaining Aggregate Query Answers |
2023 |
VLDB |
9 |
5.2542808e-05 |
| 8,981 |
Qr-Hint: Actionable Hints Towards Correcting Wrong SQL Queries |
2024 |
SIGMOD |
4 |
5.2433963e-05 |
| 9,014 |
The Cost of Representation by Subset Repairs |
2025 |
VLDB |
2 |
5.2364868e-05 |
| 9,069 |
Understanding Queries by Conditional Instances |
2022 |
SIGMOD |
6 |
5.2265129e-05 |
| 9,947 |
NLProv: Natural Language Provenance |
2016 |
VLDB |
3 |
5.1038803e-05 |
| 10,534 |
Differentially Private Explanations for Clusters |
2026 |
SIGMOD |
0 |
4.9769913e-05 |
| 10,629 |
Analyzing Deviations from Monotonic Trends through Database Repair |
2026 |
SIGMOD |
0 |
4.9769913e-05 |
| 10,805 |
Measuring Database Unfairness via Dependency Quantification Under Differential Privacy |
2026 |
VLDB |
0 |
4.9769913e-05 |
| 10,975 |
Qr-Hint: Formally Verified and AI-Explained SQL Tutoring |
2026 |
VLDB |
0 |
4.9769913e-05 |
| 11,152 |
CauSumX: Summarized Causal Explanations For Group-By-Average Queries |
2025 |
SIGMOD |
0 |
4.9769913e-05 |
| 11,211 |
Computing Inconsistency Measures Under Differential Privacy |
2025 |
SIGMOD |
2 |
4.9769913e-05 |
| 11,369 |
Finding Convincing Views to Endorse a Claim |
2025 |
VLDB |
4 |
4.9769913e-05 |
| 11,406 |
ClaimIt: Finding Convincing Views to Endorse a Claim |
2025 |
VLDB |
1 |
4.9769913e-05 |
| 11,653 |
PD-Explain: A Unified Python-native Framework for Query Explanations Over DataFrames |
2024 |
VLDB |
0 |
4.9769913e-05 |
| 11,673 |
DP-PQD: Privately Detecting Per-Query Gaps In Synthetic Data Generated By Black-Box Mechanisms |
2024 |
VLDB |
1 |
4.9769913e-05 |
| 11,796 |
Explaining Differentially Private Query Results With DPXPlain |
2023 |
VLDB |
0 |
4.9769913e-05 |
| 11,981 |
On Optimizing the Trade-off between Privacy and Utility in Data Provenance |
2021 |
SIGMOD |
1 |
4.9769913e-05 |
| 12,088 |
T-REx: Table Repair Explanations |
2020 |
SIGMOD |
0 |
4.9769913e-05 |
| 12,119 |
MuSe: Multiple Deletion Semantics for Data Repair |
2020 |
VLDB |
1 |
4.9769913e-05 |
| 12,246 |
QuestPro: Queries in SPARQL Through Provenance |
2018 |
VLDB |
0 |
4.9769913e-05 |
| 13,632 |
Demonstration of DPClustX: Differentially Private Explanations for Clusters |
2025 |
SIGMOD |
0 |
- |