COUNTATA: Dataset Labeling Using Pattern Counts
Summary: COUNTATA introduces compact dataset labels storing counts for selected attribute-value patterns, enabling estimates for arbitrary combinations without exhaustive profiling. Its interactive system exposes the label-size–estimation-error trade-off for bias and fitness-for-use analysis. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Yuval Moskovitch (University of Michigan)
- 2. H. V. Jagadish (University of Michigan)
BibTeX Citation
@article{moskovitch_vldb20,
title = {{COUNTATA: Dataset Labeling Using Pattern Counts}},
author = {Moskovitch, Yuval and Jagadish, H. V.},
journal = {PVLDB},
series = {{VLDB} '20},
volume = {13},
number = {12},
pages = {2829--2832},
doi = {10.14778/3415478.3415486},
url = {https://doi.org/10.14778/3415478.3415486},
year = {2020}
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,577 | A Nutritional Label for Rankings | 2018 | SIGMOD | 5.8364579e-05 |
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