Reptile: Aggregation-level Explanations for Hierarchical Data

Summary: Iterative, human-in-the-loop system that explains and cleans hierarchical data by learning group-level statistics and guiding drill-downs to fix distributive aggregation errors. Introduces factorised learning for aggregation-join queries with hierarchical optimisations, delivering >6× speedups and real-world deployments on Covid-19 and African farmer surveys used for policy-relevant data cleaning. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 6368
Venue: SIGMOD
Year: 2022
Pagerank: 5.1814573e-05
Overall Rank: 10,001 | 31.39%
DOI: 10.1145/3514221.3517854

Incoming Non-self Citations Over Time

Authors

1. Zezhou Huang (Columbia University)
2. Eugene Wu (Columbia University)

BibTeX Citation

@inproceedings{huang_sigmod22,
        title = {{Reptile: Aggregation-level Explanations for Hierarchical Data}},
        author = {Huang, Zezhou and Wu, Eugene},
        series = {{SIGMOD} '22},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3514221.3517854},
        url = {https://dl.acm.org/doi/10.1145/3514221.3517854},
        year = {2022}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
11,098	SDEcho: Efficient Explanation of Aggregated Sequence Difference	2025	VLDB	5.093636e-05

Outgoing Citations (Sorted by Pagerank)

Showing 27 of 27 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
112	HoloClean: Holistic Data Repairs with Probabilistic Inference	2017	VLDB	0.00032801121
191	Scorpion: Explaining Away Outliers in Aggregate Queries	2013	VLDB	0.00026096009
376	Discovering Denial Constraints	2013	VLDB	0.00019677674
536	Learning Linear Regression Models over Factorized Joins	2016	SIGMOD	0.0001693369
549	ERACER: A Database Approach for Statistical Inference and Data Cleaning	2010	SIGMOD	0.00016692839
661	Don’t be SCAREd: Use SCalable Automatic REpairing with Maximal Likelihood and Bounded Changes	2013	SIGMOD	0.0001519162
663	A Formal Approach to Finding Explanations for Database Queries	2014	SIGMOD	0.00015174751
700	Explaining differences in multidimensional aggregates	1999	VLDB	0.00014871032
714	Guided Data Repair	2011	VLDB	0.00014662041
946	HoloDetect: Few-Shot Learning for Error Detection	2019	SIGMOD	0.00013054126
1,121	ARDA: Automatic Relational Data Augmentation for Machine Learning	2020	VLDB	0.00012093059
1,323	Data Cleaning: Overview and Emerging Challenges	2016	SIGMOD	0.00011152602
1,476	Baran: Effective Error Correction via a Unified Context Representation and Transfer Learning	2020	VLDB	0.00010659277
1,792	MacroBase: Prioritizing Attention in Fast Data	2017	SIGMOD	9.7436856e-05
2,161	DIFF: A Relational Interface for Large-Scale Data Explanation	2019	VLDB	9.0606664e-05
2,297	Raha: A Configuration-Free Error Detection System	2019	SIGMOD	8.7897221e-05
3,206	Incremental View Maintenance with Triple Lock Factorization Benefits	2018	SIGMOD	7.6367549e-05
3,361	Cleaning Denial Constraint Violations through Relaxation	2020	SIGMOD	7.4872032e-05
3,380	UGuide – User-Guided Discovery of FD-Detectable Errors	2017	SIGMOD	7.457539e-05
4,392	Multi-Structural Databases	2005	PODS	6.7288249e-05
4,638	Going Beyond Provenance: Explaining Query Answers with Pattern-based Counterbalances	2019	SIGMOD	6.5919801e-05
4,890	Descriptive and Prescriptive Data Cleaning	2014	SIGMOD	6.4590072e-05
5,397	KATARA: Reliable Data Cleaning with Knowledge Bases and Crowdsourcing	2015	VLDB	6.232136e-05
5,508	LMFAO: An Engine for Batches of Group-By Aggregates	2020	VLDB	6.1922654e-05
6,763	Smart Drill-Down: A New Data Exploration Operator	2015	VLDB	5.7810308e-05
6,819	Estimating the Impact of Unknown Unknowns on Aggregate Query Results	2016	SIGMOD	5.7635226e-05
8,153	The Cascading Analysts Algorithm	2018	SIGMOD	5.4769543e-05

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