ChARLES: Change-Aware Recovery of Latent Evolution Semantics in Relational Data
Summary: ChARLES yields semantic, human-readable summaries of changes between database snapshots, not exhaustive diff lists. It infers latent evolution by fitting regression lines over partitions and ranks transformations by accuracy-interpretability tradeoffs. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Shiyi He (University of Utah)
- 2. Alexandra Meliou (University of Massachusetts Amherst)
- 3. Anna Fariha (University of Utah)
BibTeX Citation
@inproceedings{he_sigmod25,
title = {{ChARLES: Change-Aware Recovery of Latent Evolution Semantics in Relational Data}},
author = {He, Shiyi and Meliou, Alexandra and Fariha, Anna},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3722212.3725089},
url = {https://dl.acm.org/doi/10.1145/3722212.3725089},
year = {2025}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,946 | OrpheusDB: Bolt-on Versioning for Relational Databases | 2017 | VLDB | 9.4334506e-05 |
| 4,990 | Explaining Dataset Changes for Semantic Data Versioning with Explain-Da-V | 2023 | VLDB | 6.4105738e-05 |
| 5,435 | Exploring Change – A New Dimension of Data Analytics | 2019 | VLDB | 6.2207492e-05 |
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