| 4,851 |
Provenance for Natural Language Queries |
2017 |
VLDB |
5.8768322e-05 |
| 5,607 |
HYPER: Hypothetical Reasoning With What-If and How-To Queries Using a Probabilistic Causal Approach |
2022 |
SIGMOD |
5.4137872e-05 |
| 6,662 |
Selective Provenance for Datalog Programs Using Top-K Queries |
2015 |
VLDB |
4.9704872e-05 |
| 6,887 |
Synthesizing Linked Data Under Cardinality and Integrity Constraints |
2021 |
SIGMOD |
4.8937852e-05 |
| 6,975 |
NLProveNAns: Natural Language Provenance for Non-Answers |
2018 |
VLDB |
4.8772572e-05 |
| 7,066 |
On Multiple Semantics for Declarative Database Repairs |
2020 |
SIGMOD |
4.8445108e-05 |
| 7,172 |
Summarized Causal Explanations For Aggregate Views |
2024 |
SIGMOD |
4.8114797e-05 |
| 7,364 |
ExplainED: Explanations for EDA Notebooks |
2020 |
VLDB |
4.7519211e-05 |
| 8,388 |
FEDEX: An Explainability Framework for Data Exploration Steps |
2022 |
VLDB |
4.5297787e-05 |
| 8,609 |
PreFair: Privately Generating Justifiably Fair Synthetic Data |
2023 |
VLDB |
4.4853979e-05 |
| 8,840 |
The Cost of Representation by Subset Repairs |
2025 |
VLDB |
4.4388652e-05 |
| 8,954 |
Understanding Queries by Conditional Instances |
2022 |
SIGMOD |
4.4221863e-05 |
| 9,622 |
NLProv: Natural Language Provenance |
2016 |
VLDB |
4.3163112e-05 |
| 9,623 |
Qr-Hint: Actionable Hints Towards Correcting Wrong SQL Queries |
2024 |
SIGMOD |
4.3161663e-05 |
| 9,766 |
DPXPlain: Privately Explaining Aggregate Query Answers |
2023 |
VLDB |
4.2856106e-05 |
| 10,015 |
Differentially Private Explanations for Clusters |
2026 |
SIGMOD |
4.1945683e-05 |
| 10,140 |
Analyzing Deviations from Monotonic Trends through Database Repair |
2026 |
SIGMOD |
4.1945683e-05 |
| 10,429 |
CauSumX: Summarized Causal Explanations For Group-By-Average Queries |
2025 |
SIGMOD |
4.1945683e-05 |
| 10,513 |
Computing Inconsistency Measures Under Differential Privacy |
2025 |
SIGMOD |
4.1945683e-05 |
| 10,740 |
Finding Convincing Views to Endorse a Claim |
2025 |
VLDB |
4.1945683e-05 |
| 10,809 |
ClaimIt: Finding Convincing Views to Endorse a Claim |
2025 |
VLDB |
4.1945683e-05 |
| 11,123 |
PD-Explain: A Unified Python-native Framework for Query Explanations Over DataFrames |
2024 |
VLDB |
4.1945683e-05 |
| 11,143 |
DP-PQD: Privately Detecting Per-Query Gaps In Synthetic Data Generated By Black-Box Mechanisms |
2024 |
VLDB |
4.1945683e-05 |
| 11,281 |
Explaining Differentially Private Query Results With DPXPlain |
2023 |
VLDB |
4.1945683e-05 |
| 11,471 |
On Optimizing the Trade-off between Privacy and Utility in Data Provenance |
2021 |
SIGMOD |
4.1945683e-05 |
| 11,584 |
T-REx: Table Repair Explanations |
2020 |
SIGMOD |
4.1945683e-05 |
| 11,616 |
MuSe: Multiple Deletion Semantics for Data Repair |
2020 |
VLDB |
4.1945683e-05 |
| 11,735 |
QuestPro: Queries in SPARQL Through Provenance |
2018 |
VLDB |
4.1945683e-05 |
| 13,099 |
Demonstration of DPClustX: Differentially Private Explanations for Clusters |
2025 |
SIGMOD |
- |