| 1,867 |
Interpretable Data-Based Explanations for Fairness Debugging |
2022 |
SIGMOD |
0.00010272055 |
| 1,883 |
The iBench Integration Metadata Generator |
2016 |
VLDB |
0.00010215862 |
| 2,638 |
Messing Up with BART: Error Generation for Evaluating Data-Cleaning Algorithms |
2016 |
VLDB |
8.399764e-05 |
| 4,426 |
Data Debugging and Exploration with Vizier |
2019 |
SIGMOD |
6.1969994e-05 |
| 4,664 |
Efficient Answering of Historical What-if Queries |
2022 |
SIGMOD |
6.0127053e-05 |
| 4,806 |
Uncertainty Annotated Databases - A Lightweight Approach for Approximating Certain Answers |
2019 |
SIGMOD |
5.9092698e-05 |
| 5,128 |
CAPE: Explaining Outliers by Counterbalancing |
2019 |
VLDB |
5.6758584e-05 |
| 5,191 |
Going Beyond Provenance: Explaining Query Answers with Pattern-based Counterbalances |
2019 |
SIGMOD |
5.6378768e-05 |
| 5,249 |
Value Invention in Data Exchange |
2013 |
SIGMOD |
5.6054145e-05 |
| 5,691 |
Putting Things into Context: Rich Explanations for Query Answers using Join Graphs |
2021 |
SIGMOD |
5.3684557e-05 |
| 6,295 |
Your notebook is not crumby enough, REPLace it |
2020 |
CIDR |
5.1249204e-05 |
| 6,359 |
Snapshot Semantics for Temporal Multiset Relations |
2019 |
VLDB |
5.0963959e-05 |
| 6,696 |
Approximate Summaries for Why and Why-not Provenance |
2020 |
VLDB |
4.9581958e-05 |
| 6,796 |
InferDB: In-Database Machine Learning Inference Using Indexes |
2024 |
VLDB |
4.9241624e-05 |
| 6,943 |
TRAMP: Understanding the Behavior of Schema Mappings through Provenance |
2010 |
VLDB |
4.8916728e-05 |
| 7,000 |
Generating Interpretable Data-Based Explanations for Fairness Debugging using Gopher |
2022 |
SIGMOD |
4.8676312e-05 |
| 7,478 |
Debugging Transactions and Tracking their Provenance with Reenactment |
2017 |
VLDB |
4.7184026e-05 |
| 7,516 |
The Perm Provenance Management System in Action |
2009 |
SIGMOD |
4.7180617e-05 |
| 7,678 |
To Not Miss the Forest for the Trees - A Holistic Approach for Explaining Missing Answers over Nested Data |
2021 |
SIGMOD |
4.6813062e-05 |
| 7,941 |
Efficient Uncertainty Tracking for Complex Queries with Attribute-level Bounds |
2021 |
SIGMOD |
4.613363e-05 |
| 8,886 |
Provenance-based Data Skipping |
2022 |
VLDB |
4.4279829e-05 |
| 9,044 |
Efficient Approximation of Certain and Possible Answers for Ranking and Window Queries over Uncertain Data |
2023 |
VLDB |
4.4039656e-05 |
| 9,356 |
Debugging Data Exchange with Vagabond |
2011 |
VLDB |
4.3513974e-05 |
| 9,703 |
CaJaDE: Explaining Query Results by Augmenting Provenance with Context |
2022 |
VLDB |
4.3005882e-05 |
| 9,704 |
Debugging Missing Answers for Spark Queries over Nested Data with Breadcrumb |
2021 |
VLDB |
4.3005882e-05 |
| 9,851 |
Adaptive Schema Databases |
2017 |
CIDR |
4.2721228e-05 |
| 10,277 |
Efficient Query Repair for Aggregate Constraints |
2026 |
VLDB |
4.1945683e-05 |
| 10,359 |
Smallest Synthetic Witnesses for Conjunctive Queries |
2025 |
PODS |
4.1945683e-05 |
| 10,377 |
FastPDB: Towards Bag-Probabilistic Queries at Interactive Speeds |
2025 |
SIGMOD |
4.1945683e-05 |
| 10,463 |
Zorro: Quantifying Uncertainty in Models & Predictions Arising from Dirty Data |
2025 |
SIGMOD |
4.1945683e-05 |
| 10,469 |
Alsatian: Optimizing Model Search for Deep Transfer Learning |
2025 |
SIGMOD |
4.1945683e-05 |
| 10,758 |
Stress-Testing ML Pipelines with Adversarial Data Corruption |
2025 |
VLDB |
4.1945683e-05 |
| 10,895 |
Towards an Objective Metric for Data Value Through Relevance |
2024 |
CIDR |
4.1945683e-05 |
| 11,733 |
Provenance Summaries for Answers and Non-Answers |
2018 |
VLDB |
4.1945683e-05 |
| 11,841 |
BART in Action: Error Generation and Empirical Evaluations of Data-Cleaning Systems |
2016 |
SIGMOD |
4.1945683e-05 |
| 11,941 |
Gain Control over your Integration Evaluations |
2015 |
VLDB |
4.1945683e-05 |
| 13,233 |
DataSense: Display Agnostic Data Documentation |
2021 |
CIDR |
- |
| 13,389 |
Sharing and Reproducing Database Applications |
2015 |
VLDB |
- |