| 36 |
Fast Algorithms for Mining Association Rules |
1994 |
VLDB |
0.00076161096 |
| 214 |
Scorpion: Explaining Away Outliers in Aggregate Queries |
2013 |
VLDB |
0.0003363692 |
| 247 |
On the Computation of Multidimensional Aggregates |
1996 |
VLDB |
0.00030927763 |
| 460 |
SeeDB: Efficient Data-Driven Visualization Recommendations to Support Visual Analytics |
2015 |
VLDB |
0.00022516069 |
| 487 |
Why Not? |
2009 |
SIGMOD |
0.00022050218 |
| 942 |
A Formal Approach to Finding Explanations for Database Queries |
2014 |
SIGMOD |
0.00015155714 |
| 1,000 |
Intelligent Rollups in Multidimensional OLAP Data |
2001 |
VLDB |
0.00014709252 |
| 1,041 |
Interventional Fairness : Causal Database Repair for Algorithmic Fairness |
2019 |
SIGMOD |
0.00014482047 |
| 1,099 |
Interpretable and Informative Explanations of Outcomes |
2015 |
VLDB |
0.00014096312 |
| 1,119 |
The Complexity of Causality and Responsibility for Query Answers and non-Answers |
2011 |
VLDB |
0.0001386199 |
| 1,404 |
Responsible Data Management |
2020 |
VLDB |
0.00012174977 |
| 1,449 |
Causal Relational Learning |
2020 |
SIGMOD |
0.0001193267 |
| 1,867 |
Interpretable Data-Based Explanations for Fairness Debugging |
2022 |
SIGMOD |
0.00010272055 |
| 2,402 |
Causality and Explanations in Databases |
2014 |
VLDB |
8.8928361e-05 |
| 2,649 |
Explaining Query Answers with Explanation-Ready Databases |
2016 |
VLDB |
8.3719123e-05 |
| 2,810 |
Bias in OLAP Queries: Detection, Explanation, and Removal (Or Think Twice About Your AVG-Query) |
2018 |
SIGMOD |
8.0810163e-05 |
| 3,162 |
Looking for Trouble: Analyzing Classifier Behavior via Pattern Divergence |
2021 |
SIGMOD |
7.4589576e-05 |
| 3,824 |
Correlation Sketches for Approximate Join-Correlation Queries |
2021 |
SIGMOD |
6.7260705e-05 |
| 5,191 |
Going Beyond Provenance: Explaining Query Answers with Pattern-based Counterbalances |
2019 |
SIGMOD |
5.6378768e-05 |
| 5,313 |
XInsight: eXplainable Data Analysis Through The Lens of Causality |
2023 |
SIGMOD |
5.573009e-05 |
| 5,418 |
High-Level Why-Not Explanations using Ontologies |
2015 |
PODS |
5.5178123e-05 |
| 5,607 |
HYPER: Hypothetical Reasoning With What-If and How-To Queries Using a Probabilistic Causal Approach |
2022 |
SIGMOD |
5.4137872e-05 |
| 5,691 |
Putting Things into Context: Rich Explanations for Query Answers using Join Graphs |
2021 |
SIGMOD |
5.3684557e-05 |
| 5,976 |
Responsible Data Integration: Next-generation Challenges |
2022 |
SIGMOD |
5.245976e-05 |
| 6,077 |
The Fast and the Private: Task-based Dataset Search |
2024 |
CIDR |
5.2229324e-05 |
| 6,160 |
A Demonstration of Interactive Analysis of Performance Measurements with Viska |
2017 |
SIGMOD |
5.1758344e-05 |
| 6,449 |
Causal Data Integration |
2023 |
VLDB |
5.0587746e-05 |
| 6,467 |
Tailoring Data Source Distributions for Fairness-aware Data Integration |
2021 |
VLDB |
5.0528156e-05 |
| 6,565 |
Toward Interpretable and Actionable Data Analysis with Explanations and Causality |
2022 |
VLDB |
5.0081626e-05 |
| 6,643 |
Query Refinement for Diversity Constraint Satisfaction |
2024 |
VLDB |
4.9786132e-05 |
| 7,172 |
Summarized Causal Explanations For Aggregate Views |
2024 |
SIGMOD |
4.8114797e-05 |
| 7,222 |
Guided Exploration of Data Summaries |
2022 |
VLDB |
4.797186e-05 |
| 7,449 |
OTClean: Data Cleaning for Conditional Independence Violations using Optimal Transport |
2024 |
SIGMOD |
4.7269357e-05 |
| 7,602 |
Causal Feature Selection for Algorithmic Fairness |
2022 |
SIGMOD |
4.6988081e-05 |
| 8,055 |
iFlipper: Label Flipping for Individual Fairness |
2023 |
SIGMOD |
4.5947404e-05 |
| 8,388 |
FEDEX: An Explainability Framework for Data Exploration Steps |
2022 |
VLDB |
4.5297787e-05 |
| 8,618 |
Nexus: Correlation Discovery over Collections of Spatio-Temporal Tabular Data |
2024 |
SIGMOD |
4.4838259e-05 |
| 9,365 |
Falcon: Fair Active Learning using Multi-armed Bandits |
2024 |
VLDB |
4.3502315e-05 |
| 9,766 |
DPXPlain: Privately Explaining Aggregate Query Answers |
2023 |
VLDB |
4.2856106e-05 |
| 11,419 |
On Detecting Cherry-picked Generalizations |
2022 |
VLDB |
4.1945683e-05 |