| 4,526 |
Provenance for Natural Language Queries |
2017 |
VLDB |
6.7160041e-05 |
| 4,990 |
HYPER: Hypothetical Reasoning With What-If and How-To Queries Using a Probabilistic Causal Approach |
2022 |
SIGMOD |
6.4792672e-05 |
| 6,484 |
Selective Provenance for Datalog Programs Using Top-K Queries |
2015 |
VLDB |
5.9237227e-05 |
| 6,545 |
On Multiple Semantics for Declarative Database Repairs |
2020 |
SIGMOD |
5.9036536e-05 |
| 6,649 |
Synthesizing Linked Data Under Cardinality and Integrity Constraints |
2021 |
SIGMOD |
5.8721678e-05 |
| 6,812 |
Summarized Causal Explanations For Aggregate Views |
2024 |
SIGMOD |
5.8250773e-05 |
| 7,046 |
NLProveNAns: Natural Language Provenance for Non-Answers |
2018 |
VLDB |
5.7695202e-05 |
| 7,258 |
ExplainED: Explanations for EDA Notebooks |
2020 |
VLDB |
5.7155992e-05 |
| 8,055 |
FEDEX: An Explainability Framework for Data Exploration Steps |
2022 |
VLDB |
5.5557054e-05 |
| 8,492 |
PreFair: Privately Generating Justifiably Fair Synthetic Data |
2023 |
VLDB |
5.4865709e-05 |
| 8,716 |
The Cost of Representation by Subset Repairs |
2025 |
VLDB |
5.4422152e-05 |
| 8,864 |
Understanding Queries by Conditional Instances |
2022 |
SIGMOD |
5.4202259e-05 |
| 9,607 |
Qr-Hint: Actionable Hints Towards Correcting Wrong SQL Queries |
2024 |
SIGMOD |
5.3068114e-05 |
| 9,616 |
NLProv: Natural Language Provenance |
2016 |
VLDB |
5.3044234e-05 |
| 9,759 |
DPXPlain: Privately Explaining Aggregate Query Answers |
2023 |
VLDB |
5.2759752e-05 |
| 10,015 |
Differentially Private Explanations for Clusters |
2026 |
SIGMOD |
5.1725247e-05 |
| 10,140 |
Analyzing Deviations from Monotonic Trends through Database Repair |
2026 |
SIGMOD |
5.1725247e-05 |
| 10,439 |
CauSumX: Summarized Causal Explanations For Group-By-Average Queries |
2025 |
SIGMOD |
5.1725247e-05 |
| 10,522 |
Computing Inconsistency Measures Under Differential Privacy |
2025 |
SIGMOD |
5.1725247e-05 |
| 10,747 |
Finding Convincing Views to Endorse a Claim |
2025 |
VLDB |
5.1725247e-05 |
| 10,814 |
ClaimIt: Finding Convincing Views to Endorse a Claim |
2025 |
VLDB |
5.1725247e-05 |
| 11,126 |
PD-Explain: A Unified Python-native Framework for Query Explanations Over DataFrames |
2024 |
VLDB |
5.1725247e-05 |
| 11,146 |
DP-PQD: Privately Detecting Per-Query Gaps In Synthetic Data Generated By Black-Box Mechanisms |
2024 |
VLDB |
5.1725247e-05 |
| 11,283 |
Explaining Differentially Private Query Results With DPXPlain |
2023 |
VLDB |
5.1725247e-05 |
| 11,474 |
On Optimizing the Trade-off between Privacy and Utility in Data Provenance |
2021 |
SIGMOD |
5.1725247e-05 |
| 11,588 |
T-REx: Table Repair Explanations |
2020 |
SIGMOD |
5.1725247e-05 |
| 11,620 |
MuSe: Multiple Deletion Semantics for Data Repair |
2020 |
VLDB |
5.1725247e-05 |
| 11,743 |
QuestPro: Queries in SPARQL Through Provenance |
2018 |
VLDB |
5.1725247e-05 |
| 13,113 |
Demonstration of DPClustX: Differentially Private Explanations for Clusters |
2025 |
SIGMOD |
- |