| 1,640 |
Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series |
2021 |
VLDB |
0.00011048873 |
| 1,942 |
SliceLine: Fast, Linear-Algebra-based Slice Finding for ML Model Debugging |
2021 |
SIGMOD |
0.00010010569 |
| 2,122 |
SystemDS: A Declarative Machine Learning System for the End-to-End Data Science Lifecycle |
2020 |
CIDR |
9.4905306e-05 |
| 2,129 |
MacroBase: Prioritizing Attention in Fast Data |
2017 |
SIGMOD |
9.4799835e-05 |
| 3,107 |
Data X-Ray: A Diagnostic Tool for Data Errors |
2015 |
SIGMOD |
7.5549177e-05 |
| 5,192 |
Going Beyond Provenance: Explaining Query Answers with Pattern-based Counterbalances |
2019 |
SIGMOD |
5.6324589e-05 |
| 5,283 |
Explaining Dataset Changes for Semantic Data Versioning with Explain-Da-V |
2023 |
VLDB |
5.5843052e-05 |
| 5,706 |
Putting Things into Context: Rich Explanations for Query Answers using Join Graphs |
2021 |
SIGMOD |
5.3633001e-05 |
| 6,565 |
Toward Interpretable and Actionable Data Analysis with Explanations and Causality |
2022 |
VLDB |
5.0033542e-05 |
| 6,689 |
REDS: Rule Extraction for Discovering Scenarios |
2021 |
SIGMOD |
4.9575975e-05 |
| 6,700 |
Approximate Summaries for Why and Why-not Provenance |
2020 |
VLDB |
4.9534371e-05 |
| 6,948 |
DataPrism: Exposing Disconnect between Data and Systems |
2022 |
SIGMOD |
4.8865863e-05 |
| 7,067 |
Smart Drill-Down: A New Data Exploration Operator |
2015 |
VLDB |
4.8387221e-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 |
| 8,109 |
The Cascading Analysts Algorithm |
2018 |
SIGMOD |
4.5807394e-05 |
| 8,335 |
BugDoc: Algorithms to Debug Computational Processes |
2020 |
SIGMOD |
4.538972e-05 |
| 8,360 |
Query Log Compression for Workload Analytics |
2019 |
VLDB |
4.5314883e-05 |
| 8,886 |
Provenance-based Data Skipping |
2022 |
VLDB |
4.4237386e-05 |
| 9,025 |
Causality-Guided Adaptive Interventional Debugging |
2020 |
SIGMOD |
4.4032759e-05 |
| 9,223 |
BugDoc: A System for Debugging Computational Pipelines |
2020 |
SIGMOD |
4.3660299e-05 |
| 9,644 |
Fair and Actionable Causal Prescription Ruleset |
2025 |
SIGMOD |
4.3067693e-05 |
| 9,702 |
CaJaDE: Explaining Query Results by Augmenting Provenance with Context |
2022 |
VLDB |
4.2964668e-05 |
| 9,708 |
Outlier Summarization via Human Interpretable Rules |
2024 |
VLDB |
4.2951473e-05 |
| 9,768 |
DPXPlain: Privately Explaining Aggregate Query Answers |
2023 |
VLDB |
4.2815042e-05 |
| 10,029 |
Outliers: The Good, the Bad and the Ugly |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,147 |
Causal Explanations for Disparate Trends: Where and Why? |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,152 |
Data-Semantics-Aware Recommendation of Diverse Pivot Tables |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,213 |
Stress-Testing Causal Claims via Cardinality Repairs |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,269 |
Database Views as Explanations for Relational Deep Learning |
2026 |
VLDB |
4.1905499e-05 |
| 10,439 |
CauSumX: Summarized Causal Explanations For Group-By-Average Queries |
2025 |
SIGMOD |
4.1905499e-05 |
| 10,747 |
Finding Convincing Views to Endorse a Claim |
2025 |
VLDB |
4.1905499e-05 |
| 10,942 |
Relative Keys: Putting Feature Explanation into Context |
2024 |
SIGMOD |
4.1905499e-05 |
| 10,957 |
Counterfactual Explanation at Will, with Zero Privacy Leakage |
2024 |
SIGMOD |
4.1905499e-05 |
| 11,055 |
Efficiently Mitigating the Impact of Data Drift on Machine Learning Pipelines |
2024 |
VLDB |
4.1905499e-05 |
| 11,283 |
Explaining Differentially Private Query Results With DPXPlain |
2023 |
VLDB |
4.1905499e-05 |
| 11,478 |
Exploring Ratings in Subjective Databases |
2021 |
SIGMOD |
4.1905499e-05 |
| 11,740 |
Provenance Summaries for Answers and Non-Answers |
2018 |
VLDB |
4.1905499e-05 |