| 36 |
Fast Algorithms for Mining Association Rules |
1994 |
VLDB |
0.00076114894 |
| 213 |
Scorpion: Explaining Away Outliers in Aggregate Queries |
2013 |
VLDB |
0.0003371037 |
| 247 |
On the Computation of Multidimensional Aggregates |
1996 |
VLDB |
0.00030923842 |
| 461 |
SeeDB: Efficient Data-Driven Visualization Recommendations to Support Visual Analytics |
2015 |
VLDB |
0.00022615628 |
| 487 |
Why Not? |
2009 |
SIGMOD |
0.00022030123 |
| 943 |
A Formal Approach to Finding Explanations for Database Queries |
2014 |
SIGMOD |
0.00015140995 |
| 1,001 |
Intelligent Rollups in Multidimensional OLAP Data |
2001 |
VLDB |
0.00014709145 |
| 1,037 |
Interventional Fairness : Causal Database Repair for Algorithmic Fairness |
2019 |
SIGMOD |
0.00014514825 |
| 1,096 |
Interpretable and Informative Explanations of Outcomes |
2015 |
VLDB |
0.00014088686 |
| 1,119 |
The Complexity of Causality and Responsibility for Query Answers and non-Answers |
2011 |
VLDB |
0.00013851012 |
| 1,406 |
Responsible Data Management |
2020 |
VLDB |
0.0001216385 |
| 1,454 |
Causal Relational Learning |
2020 |
SIGMOD |
0.00011921443 |
| 1,869 |
Interpretable Data-Based Explanations for Fairness Debugging |
2022 |
SIGMOD |
0.00010263235 |
| 2,400 |
Causality and Explanations in Databases |
2014 |
VLDB |
8.884178e-05 |
| 2,655 |
Explaining Query Answers with Explanation-Ready Databases |
2016 |
VLDB |
8.3638668e-05 |
| 2,816 |
Bias in OLAP Queries: Detection, Explanation, and Removal (Or Think Twice About Your AVG-Query) |
2018 |
SIGMOD |
8.0747683e-05 |
| 3,170 |
Looking for Trouble: Analyzing Classifier Behavior via Pattern Divergence |
2021 |
SIGMOD |
7.4517805e-05 |
| 3,827 |
Correlation Sketches for Approximate Join-Correlation Queries |
2021 |
SIGMOD |
6.7195959e-05 |
| 5,192 |
Going Beyond Provenance: Explaining Query Answers with Pattern-based Counterbalances |
2019 |
SIGMOD |
5.6324589e-05 |
| 5,321 |
XInsight: eXplainable Data Analysis Through The Lens of Causality |
2023 |
SIGMOD |
5.5676564e-05 |
| 5,428 |
High-Level Why-Not Explanations using Ontologies |
2015 |
PODS |
5.5125084e-05 |
| 5,617 |
HYPER: Hypothetical Reasoning With What-If and How-To Queries Using a Probabilistic Causal Approach |
2022 |
SIGMOD |
5.4085897e-05 |
| 5,706 |
Putting Things into Context: Rich Explanations for Query Answers using Join Graphs |
2021 |
SIGMOD |
5.3633001e-05 |
| 5,982 |
Responsible Data Integration: Next-generation Challenges |
2022 |
SIGMOD |
5.2409386e-05 |
| 6,081 |
The Fast and the Private: Task-based Dataset Search |
2024 |
CIDR |
5.2179192e-05 |
| 6,169 |
A Demonstration of Interactive Analysis of Performance Measurements with Viska |
2017 |
SIGMOD |
5.1708657e-05 |
| 6,445 |
Causal Data Integration |
2023 |
VLDB |
5.0539192e-05 |
| 6,462 |
Tailoring Data Source Distributions for Fairness-aware Data Integration |
2021 |
VLDB |
5.0479645e-05 |
| 6,565 |
Toward Interpretable and Actionable Data Analysis with Explanations and Causality |
2022 |
VLDB |
5.0033542e-05 |
| 6,644 |
Query Refinement for Diversity Constraint Satisfaction |
2024 |
VLDB |
4.973836e-05 |
| 7,172 |
Summarized Causal Explanations For Aggregate Views |
2024 |
SIGMOD |
4.8068645e-05 |
| 7,200 |
Guided Exploration of Data Summaries |
2022 |
VLDB |
4.7980895e-05 |
| 7,449 |
OTClean: Data Cleaning for Conditional Independence Violations using Optimal Transport |
2024 |
SIGMOD |
4.7224022e-05 |
| 7,605 |
Causal Feature Selection for Algorithmic Fairness |
2022 |
SIGMOD |
4.6943015e-05 |
| 8,058 |
iFlipper: Label Flipping for Individual Fairness |
2023 |
SIGMOD |
4.5903348e-05 |
| 8,388 |
FEDEX: An Explainability Framework for Data Exploration Steps |
2022 |
VLDB |
4.525436e-05 |
| 8,616 |
Nexus: Correlation Discovery over Collections of Spatio-Temporal Tabular Data |
2024 |
SIGMOD |
4.4795277e-05 |
| 9,373 |
Falcon: Fair Active Learning using Multi-armed Bandits |
2024 |
VLDB |
4.3460825e-05 |
| 9,768 |
DPXPlain: Privately Explaining Aggregate Query Answers |
2023 |
VLDB |
4.2815042e-05 |
| 11,422 |
On Detecting Cherry-picked Generalizations |
2022 |
VLDB |
4.1905499e-05 |