| 4,602 |
Provenance for Natural Language Queries |
2017 |
VLDB |
6.6107638e-05 |
| 5,025 |
HYPER: Hypothetical Reasoning With What-If and How-To Queries Using a Probabilistic Causal Approach |
2022 |
SIGMOD |
6.3974766e-05 |
| 6,587 |
Selective Provenance for Datalog Programs Using Top-K Queries |
2015 |
VLDB |
5.8333865e-05 |
| 6,664 |
On Multiple Semantics for Declarative Database Repairs |
2020 |
SIGMOD |
5.8090585e-05 |
| 6,755 |
Synthesizing Linked Data Under Cardinality and Integrity Constraints |
2021 |
SIGMOD |
5.7830386e-05 |
| 6,932 |
Summarized Causal Explanations For Aggregate Views |
2024 |
SIGMOD |
5.7362362e-05 |
| 7,175 |
NLProveNAns: Natural Language Provenance for Non-Answers |
2018 |
VLDB |
5.6820504e-05 |
| 7,358 |
PreFair: Privately Generating Justifiably Fair Synthetic Data |
2023 |
VLDB |
5.6344213e-05 |
| 7,383 |
ExplainED: Explanations for EDA Notebooks |
2020 |
VLDB |
5.6284447e-05 |
| 8,184 |
FEDEX: An Explainability Framework for Data Exploration Steps |
2022 |
VLDB |
5.4709725e-05 |
| 8,719 |
DPXPlain: Privately Explaining Aggregate Query Answers |
2023 |
VLDB |
5.3774243e-05 |
| 8,834 |
Qr-Hint: Actionable Hints Towards Correcting Wrong SQL Queries |
2024 |
SIGMOD |
5.3599176e-05 |
| 8,838 |
The Cost of Representation by Subset Repairs |
2025 |
VLDB |
5.3592132e-05 |
| 8,970 |
Understanding Queries by Conditional Instances |
2022 |
SIGMOD |
5.3422757e-05 |
| 9,763 |
NLProv: Natural Language Provenance |
2016 |
VLDB |
5.2234888e-05 |
| 10,313 |
Differentially Private Explanations for Clusters |
2026 |
SIGMOD |
5.093636e-05 |
| 10,429 |
Analyzing Deviations from Monotonic Trends through Database Repair |
2026 |
SIGMOD |
5.093636e-05 |
| 10,710 |
CauSumX: Summarized Causal Explanations For Group-By-Average Queries |
2025 |
SIGMOD |
5.093636e-05 |
| 10,786 |
Computing Inconsistency Measures Under Differential Privacy |
2025 |
SIGMOD |
5.093636e-05 |
| 10,979 |
Finding Convincing Views to Endorse a Claim |
2025 |
VLDB |
5.093636e-05 |
| 11,036 |
ClaimIt: Finding Convincing Views to Endorse a Claim |
2025 |
VLDB |
5.093636e-05 |
| 11,329 |
PD-Explain: A Unified Python-native Framework for Query Explanations Over DataFrames |
2024 |
VLDB |
5.093636e-05 |
| 11,349 |
DP-PQD: Privately Detecting Per-Query Gaps In Synthetic Data Generated By Black-Box Mechanisms |
2024 |
VLDB |
5.093636e-05 |
| 11,480 |
Explaining Differentially Private Query Results With DPXPlain |
2023 |
VLDB |
5.093636e-05 |
| 11,668 |
On Optimizing the Trade-off between Privacy and Utility in Data Provenance |
2021 |
SIGMOD |
5.093636e-05 |
| 11,780 |
T-REx: Table Repair Explanations |
2020 |
SIGMOD |
5.093636e-05 |
| 11,811 |
MuSe: Multiple Deletion Semantics for Data Repair |
2020 |
VLDB |
5.093636e-05 |
| 11,941 |
QuestPro: Queries in SPARQL Through Provenance |
2018 |
VLDB |
5.093636e-05 |
| 13,304 |
Demonstration of DPClustX: Differentially Private Explanations for Clusters |
2025 |
SIGMOD |
- |