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)
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 |
|---|---|---|---|---|
| 11,508 | SDEcho: Efficient Explanation of Aggregated Sequence Difference | 2025 | VLDB | 4.9605343e-05 |
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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.
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