| 4,566 |
HYPER: Hypothetical Reasoning With What-If and How-To Queries Using a Probabilistic Causal Approach |
2022 |
SIGMOD |
6.5310568e-05 |
| 4,687 |
Provenance for Natural Language Queries |
2017 |
VLDB |
6.4706848e-05 |
| 6,436 |
Summarized Causal Explanations For Aggregate Views |
2024 |
SIGMOD |
5.7853643e-05 |
| 6,522 |
PreFair: Privately Generating Justifiably Fair Synthetic Data |
2023 |
VLDB |
5.7569675e-05 |
| 6,708 |
Selective Provenance for Datalog Programs Using Top-K Queries |
2015 |
VLDB |
5.7027474e-05 |
| 6,728 |
On Multiple Semantics for Declarative Database Repairs |
2020 |
SIGMOD |
5.6946342e-05 |
| 6,896 |
Synthesizing Linked Data Under Cardinality and Integrity Constraints |
2021 |
SIGMOD |
5.6532847e-05 |
| 7,317 |
NLProveNAns: Natural Language Provenance for Non-Answers |
2018 |
VLDB |
5.554755e-05 |
| 7,508 |
ExplainED: Explanations for EDA Notebooks |
2020 |
VLDB |
5.5066763e-05 |
| 8,355 |
FEDEX: An Explainability Framework for Data Exploration Steps |
2022 |
VLDB |
5.3482186e-05 |
| 8,881 |
DPXPlain: Privately Explaining Aggregate Query Answers |
2023 |
VLDB |
5.2567693e-05 |
| 8,972 |
Qr-Hint: Actionable Hints Towards Correcting Wrong SQL Queries |
2024 |
SIGMOD |
5.2458797e-05 |
| 9,004 |
The Cost of Representation by Subset Repairs |
2025 |
VLDB |
5.2389669e-05 |
| 9,061 |
Understanding Queries by Conditional Instances |
2022 |
SIGMOD |
5.2289882e-05 |
| 9,940 |
NLProv: Natural Language Provenance |
2016 |
VLDB |
5.1062975e-05 |
| 10,523 |
Differentially Private Explanations for Clusters |
2026 |
SIGMOD |
4.9793485e-05 |
| 10,618 |
Analyzing Deviations from Monotonic Trends through Database Repair |
2026 |
SIGMOD |
4.9793485e-05 |
| 10,795 |
Measuring Database Unfairness via Dependency Quantification Under Differential Privacy |
2026 |
VLDB |
4.9793485e-05 |
| 10,966 |
Qr-Hint: Formally Verified and AI-Explained SQL Tutoring |
2026 |
VLDB |
4.9793485e-05 |
| 11,143 |
CauSumX: Summarized Causal Explanations For Group-By-Average Queries |
2025 |
SIGMOD |
4.9793485e-05 |
| 11,202 |
Computing Inconsistency Measures Under Differential Privacy |
2025 |
SIGMOD |
4.9793485e-05 |
| 11,362 |
Finding Convincing Views to Endorse a Claim |
2025 |
VLDB |
4.9793485e-05 |
| 11,400 |
ClaimIt: Finding Convincing Views to Endorse a Claim |
2025 |
VLDB |
4.9793485e-05 |
| 11,647 |
PD-Explain: A Unified Python-native Framework for Query Explanations Over DataFrames |
2024 |
VLDB |
4.9793485e-05 |
| 11,667 |
DP-PQD: Privately Detecting Per-Query Gaps In Synthetic Data Generated By Black-Box Mechanisms |
2024 |
VLDB |
4.9793485e-05 |
| 11,790 |
Explaining Differentially Private Query Results With DPXPlain |
2023 |
VLDB |
4.9793485e-05 |
| 11,975 |
On Optimizing the Trade-off between Privacy and Utility in Data Provenance |
2021 |
SIGMOD |
4.9793485e-05 |
| 12,082 |
T-REx: Table Repair Explanations |
2020 |
SIGMOD |
4.9793485e-05 |
| 12,113 |
MuSe: Multiple Deletion Semantics for Data Repair |
2020 |
VLDB |
4.9793485e-05 |
| 12,240 |
QuestPro: Queries in SPARQL Through Provenance |
2018 |
VLDB |
4.9793485e-05 |
| 13,626 |
Demonstration of DPClustX: Differentially Private Explanations for Clusters |
2025 |
SIGMOD |
- |