| 1,634 |
Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series |
2021 |
VLDB |
0.00011058945 |
| 1,940 |
SliceLine: Fast, Linear-Algebra-based Slice Finding for ML Model Debugging |
2021 |
SIGMOD |
0.00010020173 |
| 2,122 |
SystemDS: A Declarative Machine Learning System for the End-to-End Data Science Lifecycle |
2020 |
CIDR |
9.4989076e-05 |
| 2,126 |
MacroBase: Prioritizing Attention in Fast Data |
2017 |
SIGMOD |
9.4887794e-05 |
| 3,105 |
Data X-Ray: A Diagnostic Tool for Data Errors |
2015 |
SIGMOD |
7.5568954e-05 |
| 5,191 |
Going Beyond Provenance: Explaining Query Answers with Pattern-based Counterbalances |
2019 |
SIGMOD |
5.6378768e-05 |
| 5,280 |
Explaining Dataset Changes for Semantic Data Versioning with Explain-Da-V |
2023 |
VLDB |
5.5896735e-05 |
| 5,691 |
Putting Things into Context: Rich Explanations for Query Answers using Join Graphs |
2021 |
SIGMOD |
5.3684557e-05 |
| 6,565 |
Toward Interpretable and Actionable Data Analysis with Explanations and Causality |
2022 |
VLDB |
5.0081626e-05 |
| 6,688 |
REDS: Rule Extraction for Discovering Scenarios |
2021 |
SIGMOD |
4.9623586e-05 |
| 6,696 |
Approximate Summaries for Why and Why-not Provenance |
2020 |
VLDB |
4.9581958e-05 |
| 6,944 |
DataPrism: Exposing Disconnect between Data and Systems |
2022 |
SIGMOD |
4.8912787e-05 |
| 7,071 |
Smart Drill-Down: A New Data Exploration Operator |
2015 |
VLDB |
4.8429461e-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 |
| 8,104 |
The Cascading Analysts Algorithm |
2018 |
SIGMOD |
4.5851358e-05 |
| 8,341 |
BugDoc: Algorithms to Debug Computational Processes |
2020 |
SIGMOD |
4.5433282e-05 |
| 8,364 |
Query Log Compression for Workload Analytics |
2019 |
VLDB |
4.5357797e-05 |
| 8,886 |
Provenance-based Data Skipping |
2022 |
VLDB |
4.4279829e-05 |
| 9,024 |
Causality-Guided Adaptive Interventional Debugging |
2020 |
SIGMOD |
4.4075011e-05 |
| 9,220 |
BugDoc: A System for Debugging Computational Pipelines |
2020 |
SIGMOD |
4.3702188e-05 |
| 9,644 |
Fair and Actionable Causal Prescription Ruleset |
2025 |
SIGMOD |
4.3109001e-05 |
| 9,703 |
CaJaDE: Explaining Query Results by Augmenting Provenance with Context |
2022 |
VLDB |
4.3005882e-05 |
| 9,709 |
Outlier Summarization via Human Interpretable Rules |
2024 |
VLDB |
4.299267e-05 |
| 9,766 |
DPXPlain: Privately Explaining Aggregate Query Answers |
2023 |
VLDB |
4.2856106e-05 |
| 10,029 |
Outliers: The Good, the Bad and the Ugly |
2026 |
SIGMOD |
4.1945683e-05 |
| 10,147 |
Causal Explanations for Disparate Trends: Where and Why? |
2026 |
SIGMOD |
4.1945683e-05 |
| 10,152 |
Data-Semantics-Aware Recommendation of Diverse Pivot Tables |
2026 |
SIGMOD |
4.1945683e-05 |
| 10,213 |
Stress-Testing Causal Claims via Cardinality Repairs |
2026 |
SIGMOD |
4.1945683e-05 |
| 10,269 |
Database Views as Explanations for Relational Deep Learning |
2026 |
VLDB |
4.1945683e-05 |
| 10,429 |
CauSumX: Summarized Causal Explanations For Group-By-Average Queries |
2025 |
SIGMOD |
4.1945683e-05 |
| 10,740 |
Finding Convincing Views to Endorse a Claim |
2025 |
VLDB |
4.1945683e-05 |
| 10,939 |
Relative Keys: Putting Feature Explanation into Context |
2024 |
SIGMOD |
4.1945683e-05 |
| 10,954 |
Counterfactual Explanation at Will, with Zero Privacy Leakage |
2024 |
SIGMOD |
4.1945683e-05 |
| 11,052 |
Efficiently Mitigating the Impact of Data Drift on Machine Learning Pipelines |
2024 |
VLDB |
4.1945683e-05 |
| 11,281 |
Explaining Differentially Private Query Results With DPXPlain |
2023 |
VLDB |
4.1945683e-05 |
| 11,474 |
Exploring Ratings in Subjective Databases |
2021 |
SIGMOD |
4.1945683e-05 |
| 11,733 |
Provenance Summaries for Answers and Non-Answers |
2018 |
VLDB |
4.1945683e-05 |