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Complaint-driven Training Data Debugging for Query 2.0

Summary: Rain is a complaint-driven debugger for training data in Query 2.0, letting users lodge complaints on outputs to prune minimal training-set fixes. Two influence-function-based heuristics enable linear retraining, achieving high recall@k and interactive performance on real-world datasets. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
hf44158c511f5c6d7
Venue
SIGMOD
Year
2020
Pagerank
8.2385793e-05
Overall Rank
2,598 | 82.54%
DOI
10.1145/3318464.3389696

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{wu_sigmod20,
        title = {{Complaint-driven Training Data Debugging for Query 2.0}},
        author = {Wu, Weiyuan and Flokas, Lampros and Wu, Eugene and Wang, Jiannan},
        series = {{SIGMOD} '20},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3318464.3389696},
        url = {https://dl.acm.org/doi/10.1145/3318464.3389696},
        year = {2020}
}

Incoming Citations (Sorted by Pagerank)

Showing 15 of 15 citing papers.

Rank Citing Paper Year Venue Pagerank
1,941 Interpretable Data-Based Explanations for Fairness Debugging 2022 SIGMOD 9.3297671e-05
3,331 VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition 2021 VLDB 7.4166095e-05
4,982 XInsight: eXplainable Data Analysis Through The Lens of Causality 2023 SIGMOD 6.328859e-05
5,178 Explainable AI: Foundations, Applications, Opportunities for Data Management Research 2022 SIGMOD 6.2411759e-05
5,247 Enabling SQL-based Training Data Debugging for Federated Learning 2022 VLDB 6.2121767e-05
7,028 Explaining Inference Queries with Bayesian Optimization 2021 VLDB 5.6177289e-05
7,183 DataPrism: Exposing Disconnect between Data and Systems 2022 SIGMOD 5.5917354e-05
7,648 MisDetect: Iterative Mislabel Detection using Early Loss 2024 VLDB 5.4772833e-05
7,703 Interactive Query Explanations Using Fine Grained Provenance 2022 SIGMOD 5.4744994e-05
8,277 Hyper-Tune: Towards Efficient Hyper-parameter Tuning at Scale 2022 VLDB 5.3639084e-05
8,411 SHiFT: An Efficient, Flexible Search Engine for Transfer Learning 2023 VLDB 5.336291e-05
9,019 Complaint-Driven Training Data Debugging at Interactive Speeds 2022 SIGMOD 5.2364886e-05
10,526 Guardrail: Automated Integrity Constraint Synthesis From Noisy Data 2026 SIGMOD 4.9793485e-05
11,556 MetaStore: Analyzing Deep Learning Meta-Data at Scale 2024 VLDB 4.9793485e-05
11,936 Ease.ML: A Lifecycle Management System for MLDev and MLOps 2021 CIDR 4.9793485e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 36 of 36 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
17 Provenance Semirings 2007 PODS 0.00059752575
51 Efficient Query Evaluation on Probabilistic Databases 2004 VLDB 0.00042936299
104 HoloClean: Holistic Data Repairs with Probabilistic Inference 2017 VLDB 0.00033690989
105 The MADlib Analytics Library or MAD Skills, the SQL 2012 VLDB 0.00033638251
189 Scorpion: Explaining Away Outliers in Aggregate Queries 2013 VLDB 0.00025840026
281 Accelerating Machine Learning Inference with Probabilistic Predicates 2018 SIGMOD 0.00022295232
391 Why Not? 2009 SIGMOD 0.00019238698
415 SystemML: Declarative Machine Learning on Spark 2016 VLDB 0.0001865959
483 ActiveClean: Interactive Data Cleaning For Statistical Modeling 2016 VLDB 0.00017590977
530 Magellan: Toward Building Entity Matching Management Systems 2016 VLDB 0.00016855162
537 MLbase: A Distributed Machine-learning System 2013 CIDR 0.00016768109
670 A Formal Approach to Finding Explanations for Database Queries 2014 SIGMOD 0.00014954494
791 How to ConQueR Why-Not Questions 2010 SIGMOD 0.00013976192
803 Interventional Fairness : Causal Database Repair for Algorithmic Fairness 2019 SIGMOD 0.00013836858
819 Provenance for Aggregate Queries 2011 PODS 0.00013666629
883 HoloDetect: Few-Shot Learning for Error Detection 2019 SIGMOD 0.00013268059
1,051 Tiresias: The Database Oracle for How-To Queries 2012 SIGMOD 0.00012279821
1,153 Data Management Challenges in Production Machine Learning 2017 SIGMOD 0.00011798912
1,344 Detecting Data Errors: Where are we and what needs to be done? 2016 VLDB 0.00010956518
1,615 Sensitivity Analysis and Explanations for Robust Query Evaluation in Probabilistic Databases 2011 SIGMOD 0.00010067073
1,805 Raha: A Configuration-Free Error Detection System 2019 SIGMOD 9.59842e-05
1,981 Causality and Explanations in Databases 2014 VLDB 9.2586863e-05
2,160 DIFF: A Relational Interface for Large-Scale Data Explanation 2019 VLDB 8.9364035e-05
2,222 Explaining Query Answers with Explanation-Ready Databases 2016 VLDB 8.8109051e-05
2,400 Declarative Recursive Computation on an RDBMS or, Why You Should Use a Database For Distributed Machine Learning 2019 VLDB 8.5213814e-05
2,686 Data X-Ray: A Diagnostic Tool for Data Errors 2015 SIGMOD 8.1308928e-05
2,949 Reverse Data Management 2011 VLDB 7.821181e-05
3,307 Cleaning Crowdsourced Labels Using Oracles for Statistical Classification 2019 VLDB 7.4414303e-05
3,349 Reverse Engineering Aggregation Queries 2017 VLDB 7.391292e-05
3,683 Cloudy with High Chance of DBMS: A 10-year Prediction for Enterprise-Grade ML 2020 CIDR 7.1006425e-05
3,736 Snorkel: Fast Training Set Generation for Information Extraction 2017 SIGMOD 7.0642592e-05
3,997 Overton: A Data System for Monitoring and Improving Machine-Learned Products 2020 CIDR 6.8655222e-05
4,827 QFix: Diagnosing Errors through Query Histories 2017 SIGMOD 6.3917868e-05
6,490 HypDB: A Demonstration of Detecting, Explaining and Resolving Bias in OLAP queries 2018 VLDB 5.7675055e-05
6,559 DeepBase: Deep Inspection of Neural Networks 2019 SIGMOD 5.7489089e-05
7,800 Subjective Databases 2019 VLDB 5.4507674e-05
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