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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
8.3984284e-05
Overall Rank
2,473 | 83.43%
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 16 of 16 citing papers.

Rank Citing Paper Year Venue Pagerank
1,954 Interpretable Data-Based Explanations for Fairness Debugging 2022 SIGMOD 9.2965173e-05
3,340 VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition 2021 VLDB 7.3886578e-05
5,008 XInsight: eXplainable Data Analysis Through The Lens of Causality 2023 SIGMOD 6.3049458e-05
5,201 Explainable AI: Foundations, Applications, Opportunities for Data Management Research 2022 SIGMOD 6.2175939e-05
5,273 Enabling SQL-based Training Data Debugging for Federated Learning 2022 VLDB 6.1887043e-05
7,050 Explaining Inference Queries with Bayesian Optimization 2021 VLDB 5.5965026e-05
7,194 DataPrism: Exposing Disconnect between Data and Systems 2022 SIGMOD 5.573413e-05
7,667 MisDetect: Iterative Mislabel Detection using Early Loss 2024 VLDB 5.4565877e-05
7,722 Interactive Query Explanations Using Fine Grained Provenance 2022 SIGMOD 5.4538143e-05
7,932 Complaint-Driven Training Data Debugging at Interactive Speeds 2022 SIGMOD 5.4074941e-05
8,298 Hyper-Tune: Towards Efficient Hyper-parameter Tuning at Scale 2022 VLDB 5.3437127e-05
8,435 SHiFT: An Efficient, Flexible Search Engine for Transfer Learning 2023 VLDB 5.3161281e-05
10,390 Detecting Contradictions Within and Across Documents 2027 SIGMOD 4.9605343e-05
10,603 Guardrail: Automated Integrity Constraint Synthesis From Noisy Data 2026 SIGMOD 4.9605343e-05
11,611 MetaStore: Analyzing Deep Learning Meta-Data at Scale 2024 VLDB 4.9605343e-05
11,986 Ease.ML: A Lifecycle Management System for MLDev and MLOps 2021 CIDR 4.9605343e-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 0.00059660827
51 Efficient Query Evaluation on Probabilistic Databases 2004 VLDB 0.00042804864
100 HoloClean: Holistic Data Repairs with Probabilistic Inference 2017 VLDB 0.00033951027
105 The MADlib Analytics Library or MAD Skills, the SQL 2012 VLDB 0.00033605348
191 Scorpion: Explaining Away Outliers in Aggregate Queries 2013 VLDB 0.0002578784
278 Accelerating Machine Learning Inference with Probabilistic Predicates 2018 SIGMOD 0.00022277737
389 Why Not? 2009 SIGMOD 0.00019254758
415 SystemML: Declarative Machine Learning on Spark 2016 VLDB 0.00018619173
460 ActiveClean: Interactive Data Cleaning For Statistical Modeling 2016 VLDB 0.00017836918
517 Magellan: Toward Building Entity Matching Management Systems 2016 VLDB 0.00016946929
538 MLbase: A Distributed Machine-learning System 2013 CIDR 0.00016725463
672 A Formal Approach to Finding Explanations for Database Queries 2014 SIGMOD 0.00014903109
790 How to ConQueR Why-Not Questions 2010 SIGMOD 0.00013929166
804 Interventional Fairness : Causal Database Repair for Algorithmic Fairness 2019 SIGMOD 0.00013788292
811 Provenance for Aggregate Queries 2011 PODS 0.00013713646
853 HoloDetect: Few-Shot Learning for Error Detection 2019 SIGMOD 0.00013437314
1,055 Tiresias: The Database Oracle for How-To Queries 2012 SIGMOD 0.0001225364
1,151 Data Management Challenges in Production Machine Learning 2017 SIGMOD 0.000117662
1,306 Detecting Data Errors: Where are we and what needs to be done? 2016 VLDB 0.00011076619
1,619 Sensitivity Analysis and Explanations for Robust Query Evaluation in Probabilistic Databases 2011 SIGMOD 0.00010044303
1,731 Raha: A Configuration-Free Error Detection System 2019 SIGMOD 9.7585913e-05
1,989 Causality and Explanations in Databases 2014 VLDB 9.2277193e-05
2,167 DIFF: A Relational Interface for Large-Scale Data Explanation 2019 VLDB 8.9166414e-05
2,233 Explaining Query Answers with Explanation-Ready Databases 2016 VLDB 8.7844607e-05
2,407 Declarative Recursive Computation on an RDBMS or, Why You Should Use a Database For Distributed Machine Learning 2019 VLDB 8.4928721e-05
2,685 Data X-Ray: A Diagnostic Tool for Data Errors 2015 SIGMOD 8.116387e-05
2,962 Reverse Data Management 2011 VLDB 7.7949147e-05
3,318 Cleaning Crowdsourced Labels Using Oracles for Statistical Classification 2019 VLDB 7.4142395e-05
3,353 Reverse Engineering Aggregation Queries 2017 VLDB 7.3674867e-05
3,691 Cloudy with High Chance of DBMS: A 10-year Prediction for Enterprise-Grade ML 2020 CIDR 7.0771037e-05
3,737 Snorkel: Fast Training Set Generation for Information Extraction 2017 SIGMOD 7.0425964e-05
4,009 Overton: A Data System for Monitoring and Improving Machine-Learned Products 2020 CIDR 6.8427225e-05
4,852 QFix: Diagnosing Errors through Query Histories 2017 SIGMOD 6.3680596e-05
6,496 HypDB: A Demonstration of Detecting, Explaining and Resolving Bias in OLAP queries 2018 VLDB 5.7489341e-05
6,573 DeepBase: Deep Inspection of Neural Networks 2019 SIGMOD 5.7271875e-05
7,788 Subjective Databases 2019 VLDB 5.4356299e-05
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