| CaJaDE: Explaining Query Results by Augmenting Provenance with Context. | 0 | 0.34 | 2022 |
| 2022 ACM PODS Alberto O. Mendelzon Test-of-Time Award | 0 | 0.34 | 2022 |
| Toward Interpretable and Actionable Data Analysis with Explanations and Causality. | 0 | 0.34 | 2022 |
| Understanding Queries by Conditional Instances | 0 | 0.34 | 2022 |
| HYPER: Hypothetical Reasoning With What-If and How-To Queries Using a Probabilistic Causal Approach | 0 | 0.34 | 2022 |
| CaJaDE: Explaining Query Results by Augmenting Provenance with Context. | 0 | 0.34 | 2022 |
| Selectivity Functions of Range Queries are Learnable | 0 | 0.34 | 2022 |
| Putting Things into Context: Rich Explanations for Query Answers using Join Graphs | 0 | 0.34 | 2021 |
| Properties of Inconsistency Measures for Databases | 0 | 0.34 | 2021 |
| Flame: A Fast Large-Scale Almost Matching Exactly Approach To Causal Inference | 0 | 0.34 | 2021 |
| Aggregated Deletion Propagation for Counting Conjunctive Query Answers. | 0 | 0.34 | 2021 |
| Computing Optimal Repairs for Functional Dependencies | 0 | 0.34 | 2020 |
| Computing Local Sensitivities of Counting Queries with Joins | 1 | 0.36 | 2020 |
| Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation | 0 | 0.34 | 2020 |
| MuSe: multiple deletion semantics for data repair | 0 | 0.34 | 2020 |
| I-Rex: an interactive relational query explainer for SQL | 0 | 0.34 | 2020 |
| Causal Relational Learning | 0 | 0.34 | 2020 |
| MuSe: Multiple Deletion Semantics for Data Repair. | 0 | 0.34 | 2020 |
| Learning to Sample: Counting with Complex Queries. | 0 | 0.34 | 2020 |
| On Multiple Semantics for Declarative Database Repairs | 0 | 0.34 | 2020 |
| I-Rex: An Interactive Relational Query Explainer for SQL. | 0 | 0.34 | 2020 |
| Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference | 0 | 0.34 | 2020 |
| Learning to Sample: Counting with Complex Queries. | 1 | 0.35 | 2019 |
| Explaining Wrong Queries Using Small Examples. | 1 | 0.35 | 2019 |
| LensXPlain: Visualizing and Explaining Contributing Subsets for Aggregate Query Answers. | 0 | 0.34 | 2019 |
| Going Beyond Provenance: Explaining Query Answers with Pattern-based Counterbalances | 1 | 0.36 | 2019 |
| Interpretable Almost-Matching-Exactly With Instrumental Variables. | 0 | 0.34 | 2019 |
| iQCAR: inter-Query Contention Analyzer for Data Analytics Frameworks | 0 | 0.34 | 2019 |
| Opportunities for Data Management Research in the Era of Horizontal AI/ML. | 0 | 0.34 | 2019 |
| RATest: Explaining Wrong Relational Queries Using Small Examples | 0 | 0.34 | 2019 |
| On Benchmarking for Crowdsourcing and Future of Work Platforms. | 0 | 0.34 | 2019 |
| CAPE: Explaining Outliers by Counterbalancing. | 0 | 0.34 | 2019 |
| LensXPlain: Visualizing and Explaining Contributing Subsets for Aggregate Query Answers. | 0 | 0.34 | 2019 |
| Principles of Progress Indicators for Database Repairing. | 0 | 0.34 | 2019 |
| CAPE: Explaining Outliers by Counterbalancing. | 0 | 0.34 | 2019 |
| iQCAR: A Demonstration of an Inter-Query Contention Analyzer for Cluster Computing Frameworks. | 1 | 0.63 | 2018 |
| Interactive summarization and exploration of top aggregate query answers | 2 | 0.36 | 2018 |
| Query Perturbation Analysis: An Adventure of Database Researchers in Fact-Checking. | 0 | 0.34 | 2018 |
| Collapsing-Fast-Large-Almost-Matching-Exactly: A Matching Method for Causal Inference. | 0 | 0.34 | 2018 |
| Interactive Summarization and Exploration of Top Aggregate Query Answers. | 0 | 0.34 | 2018 |
| QAGView: Interactively Summarizing High-Valued Aggregate Query Answers. | 0 | 0.34 | 2018 |
| iQCAR: Inter-Query Contention Analyzer. | 0 | 0.34 | 2018 |
| Computing Optimal Repairs for Functional Dependencies | 3 | 0.38 | 2017 |
| A Framework for Inferring Causality from Multi-Relational Observational Data using Conditional Independence. | 0 | 0.34 | 2017 |
| Analyzing Query Performance and Attributing Blame for Contentions in a Cluster Computing Framework. | 1 | 0.52 | 2017 |
| Optimizing Iceberg Queries with Complex Joins. | 0 | 0.34 | 2017 |
| Exact Model Counting of Query Expressions: Limitations of Propositional Methods. | 4 | 0.39 | 2017 |
| FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference. | 2 | 0.45 | 2017 |
| Explaining Query Answers with Explanation-Ready Databases. | 0 | 0.34 | 2016 |
| On the Complexity of Evaluating Order Queries with the Crowd. | 1 | 0.35 | 2015 |
